(Around 1992)
I thought you might find this interesting (and that you might even be tempted to join in). The discussion started with the following passage by Pat Hayes from a Virtual Symposium on the Virtual Mind that will appear in Minds & Machines in a few months. The rest is self-explanatory. I've included only the abstract plus the pertinent passages from the Symposium. (A few messages were unfortunately not saved, but I think they are easily reconstructed from context.)
-- Cheers, Stevan Harnad
[To Appear in: "Minds and Machines" 1992]
Virtual Symposium on the Virtual Mind
Patrick Hayes CSLI Stanford University
Stevan Harnad Psychology Department Princeton University
Donald Perlis Department of Computer Science University of Maryland
Ned Block Department of Philosophy and Linguistics Massachussetts Institute of Technology
ABSTRACT: When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual mind" real?
This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one.
[text deleted]
HAYES: You have heard me make this distinction, Stevan (in the Symposium on Searle's Chinese Room Argument at the 16th Annual Meeting of the Society for Philosophy and Psychology in College Park, Maryland, June 1990). I now think that the answer is, No, Searle isn't a (possible) implementation of that algorithm. Let me start with the abacus, which is clearly not an implementation of anything. There is a mistake here (which is also made by Putnam (1975, p. 293) when he insists that a computer might be realized by human clerks; the same mistake is made by Searle (1990), more recently, when he claims that the wall behind his desk is a computer): Abacusses are passive. They can't actually run a program unless you somehow give them a motor and bead feelers, etc.; in other words, unless you make them into a computer! The idea of the implementation-independence of the computational level does not allow there to be NO implementation; it only suggests that how the program is implemented is not important for understanding what it does.
[text deleted]
Searle, J. R. (1990) Is the Brain a Digital Computer? Presidential Address. Proceedings of the American Philsophical Association.
---------------------------------------------------------
> Date: Wed, 18 Mar 92 08:12:10 -0800
> From: searle@cogsci.Berkeley.EDU (John R. Searle)
> To: harnad@princeton.edu (Stevan Harnad)
>
> Subject: Re: "My wall is a computer"
>
> Stevan, I don't actually say that. I say that on the standard Turing
> definition it is hard to see how to avoid the conclusion that
> everything is a computer under some description. I also say that I
> think this result can be avoided by introducing counterfactuals and
> causation into the definition of computation. I also claim that Brian
> Smith, Batali, etc. are working on a definition to avoid this result.
> But it is not my view that the wall behind me is a digital computer.
>
> I think the big problem is NOT universal realizability. That is only a
> SYMPTOM of the big problem. the big problem is : COMPUTATION IS AN
> OBSERVER RELATIVE FEATURE. Just as semantics is not intrinsic to syntax
> (as shown by the Chinese Room) so SYNTAX IS NOT INTRINSIC TO PHYSICS.
> The upshot is that the question : Is the wall (or the brain) a
> digital computer is meaningless, as it stands. If the question is "Can
> you assign a computational interpretation to the wall/brain?" the
> answer is trivially yes. you can assign an interpretation to anything.
>
> If the question is : "Is the wall/brain INTRINSICALLY a digital
> computer?" the answer is: NOTHING is intrisically a digital computer.
> Please explain this point to your colleagues. they seem to think the
> issue is universal realizability. Thus Chrisley's paper for example.
>
> Anyhow the reference is to my APA presidential address " IS the Brain a
> Digital Computer?" proceeding of the Am Philos Assoc, for 90 or 91.
> I will send you the paper formatted for troff.
> Best john
John, many thanks for the reference and the details of your view about computers/computation. I think another way to phrase the question is:
(1) What is computation? and
(2) What is the implementation of a computation?
The answer I favor would be that computation is formal symbol manipulation (symbols are arbitrary objects that are manipulated on the basis of formal rules that operate only on their arbitrary shapes).
Syntax is unproblematic (just as it is in mathematics): It consists of rules that apply only to the arbitrary shapes of symbols (symbol tokens), not to their meanings. The problem is deciding what is NONTRIVIAL symbol manipulation (or nontrivially interpretable symbol manipulation): A symbol system with only two states, "0" and "1," respectively interpretable as "Life is like a bagel" and "Life is not like a bagel," is a trivial symbol system. Arithmetic and English are nontrivial symbol systems.
The trick will be to specify formally how to distinguish the trivial kind of symbol system from the nontrivial kind, and I suspect that this will turn out to depend on the property of systematicity: Trivial symbol systems have countless arbitrary "duals": You can swap the interpretations of their symbols and still come up with a coherent semantics (e.g., swap bagel and not-bagel above). Nontrivial symbol systems do not in general have coherently interpretable duals, or if they do, they are a few specific formally provable special cases (like the swappability of conjunction/negation and disjunction/negation in the propositional calculus). You cannot arbitrarily swap interpretations in general, in Arithmetic, English or LISP, and still expect the system to be able to bear the weight of a coherent systematic interpretation.
For example, in English try swapping the interpretations of true vs. false or even red vs. green, not to mention functors like if vs. not: the corpus of English utterances is no longer likely to be coherently interpretable under this arbitrary nonstandard interpretation; to make it so, EVERY symbol's interpretation would have to change in order to systematically adjust for the swap. It is this rigidity and uniqueness of the system with respect to the standard, "intended" interpretation that will, I think, distinguish nontrivial symbol systems from trivial ones. And although I'm not sure, I have an intuition that the difference will be an all-or-none one, rather than a matter of degree.
A computer, then, will be the physical implementation of a symbol system -- a dynamical system whose states and state-sequences are the interpretable objects (whereas in a static formal symbol system the objects are, say, just scratches on paper). A Turing Machine is an abstract idealization of the class of implementations of symbol systems; a digital computer is a concrete physical realization. I think a wall, for example, is only the implementation of a trivial computation, and hence if the nontrivial/trivial distinction can be formally worked out, a wall can be excluded from the class of computers (or included only as a trivial computer).
Best wishes, Stevan
---------
Cc: Allen.Newell@cs.cmu.edu, GOLDFARB%unb.ca@UNBMVS1.csd.unb.ca (Lev Goldfarb), carroll@watson.ibm.com (John M Carroll), dennett@pearl.tufts.edu (Dan Dennett), fb0m+@andrew.cmu.edu (Frank Boyle), haugelan@unix.cis.pitt.edu, hayes@sumex-aim.stanford.edu (Pat Hayes), searle@cogsci.berkeley.edu
Herb, this Turing-like criterion surely fits most cases of computation
(though perhaps not all: we might not want to exclude mindless
rote-iterations or tasks so unlike human ones that we might not even be
able to say whether we would judge them as intelligent if performed by
a human). But even if your criterion were extensionally equivalent to
nontrivial computation, it still would not tell us what nontrivial
computation was, because it does not tell us what "tasks whose
performance by a human..." are! In other words, if this were the right
criterion, it would not be explicated till we had a theory of what the
human mind can do, and how.
In general, although the human element certainly enters our definition
of computation (trivial and nontrivial) in that the symbol system must
be systematically interpretable (by/to a human), I think that apart
from that our definition must be independent of human considerations. I
think it should be just as unnecessary to draw upon a theory of how the
human mind works in order to explain what computation is as it is
unnecessary to draw upon a theory of how the human mind works in order
to explain what mathematics (or engineering, or physics) is.
Stevan Harnad
Hi John (Carroll)! You didn't eavesdrop; I branched it to you and
others (by blind CC) intentionally, because I thought you might be
interested. I've gotten several responses so far, but not yet from
Searle. Dan Dennett wrote that he had published a similar "duals" idea,
which he called the "cryptographer's criterion," and Frank Boyle wrote
that Haugeland had made a similar rigid interpretability proposal in
"AI and the Western Mind." I made the suggestion independently several
years ago in a paper called "The Origin of Words: A Psychophysical
Hypothesis" and first thought about it in reflecting on Quinean
underdetermination of word meaning and inverted spectra several years
earlier.
Although the artifact-design/user-theory problem and the problem of
what is a computer/computation have some things in common, I suspect
they part paths at the same Platonic point where the truths of formal
mathematics part paths from the purposes of their creators. (Lev
Goldfarb responded with a similar suggestion: that explaining
nontrivial computation requires a theory of inductive learning.)
Stevan Harnad
Date: Sat, 21 Mar 92 03:14:43 EST
To: roitblat@uhunix.uhcc.Hawaii.Edu (Herb Roitblat)
Herb (Roitblat), we disagree on a lot! I don't think a computer is the
class of devices that can simulate other devices, or if it is, then
that leaves me as uncertain what that class of devices is as before. I
think a computer is a device that implements a nontrivial symbol
system, and what makes a symbol system nontrivial is that it can bear
the weight of one systematic interpretation (the standard one, and in
a few special cases, some provable nonstandard ones). I think a
grounded symbol system is one in which the interpretations of its
symbols do not just square with what is in the mind of us outside
interpreters, but also with what the system does in the real world. The
nontrivial grounded symbol system that interests me is the robot that
can pass the Total Turing Test (behave indistinguishably from
ourselves).
We disagree even more on categories. I think the Roschian view you
describe is all wrong, and that the "classical" view -- that categories
have invariant features that allow us to categorize in the all-or-none
way we clearly do -- is completely correct. Introspections about how
we categorize are irrelevant (did we expect introspection to do
our theoretical work for us, as cognitive theorists?), as are
reaction times and typicality judgments. The performance capacity
at issue is our capacity to learn to sort and label things as we do, not
how fast we do it, not how typical we find the members we can
correctly sort and label, not the cases we CANNOT sort and label,
not the metaphysical status of the "correctness" (just its relation
to the Skinnerian consequences of MIScategorization), and certainly
not how we happen to think we do it. And the categories of interest
are all-or-none categories like "bird," not graded ones like "big."
Cheers, Stevan
----------------
Date: Sun, 22 Mar 92 20:45:49 EST
From: "Stevan Harnad"
On: Nontrivial Computation, Nonarbitrary Interpretability, and Complexity
Gary, thanks for your comments. Although I can't demonstrate it formally
(but then a lot of this is informal and nondemonstrative), I suspect
that there is a homology between a nonarbitrary sense in which a
system is a computer and (the implementation of) a nontrivial computation,
both resting on similar combinatorial, complexity-theoretic
considerations. Coherent, systematic alternative interpretations are
hard to come by, if at all, precisely because fitting an interpretation
to a physical system is not arbitrary. There is, after all, a difference
between a random string of symbols (typed by a chimp, say) that is
(PERHAPS, and surely tortuously) interpretable as a Shakespearean play
and a nonrandom string of symbols that is readily interpretable as a
Shakespearean play. The complexity-theoretic difference would be that
the algorithm you would need in order to interpret the random string as
Shakespeare would be at least as long as the random string itself,
whereas in the case of the real Shakespeare it would be orders of
magnitude shorter. Moreover, one epsilon of perturbation in the random
string, and you're back to square one insofar as its interpretability
as Shakespeare is concerned. Not so with nonrandom strings and their
interpretations. So interpretations the path to which is NP-complete
hardly seem worth more attention than the possibility that this message
could be interpreted as Grand Unified Field Theory.
I continue to think that we should be able to specify what (nontrivial)
computation and computers are just as observer-independently as we can
specify what flying, birds and aiplanes are. The only way the observer
ever got into it in the first place was because a nontrivial symbol
system must be able to bear the weight of a coherent systematic
interpretation, which is something an observer might happen to want to
project onto it.
Best wishes,
Stevan
----------
Gary, Two-part reply: First, the bit-string generated by the
black-white levels on the surface of the pages of a book look like a
reasonable default encoding (then, together with a
character-recognition algorithm and an English parser the string is
parsimoniously reduced to a non-random one). But if that default option
strikes you as too "observer-dependent," then pull the observer's MIND
out of it entirely and simply allow the CAUSAL interaction -- between
the book's surface optical structure (as demonstrably distinct from,
say, the molecular structure of its ink) and organisms' visual
transducers -- to serve as the objective basis for "picking out" the
default encoding.
This uses only the fact that these symbols are parts of "dedicated"
systems in the world -- not that any part of the system has a mind or
interprets them -- in order to do the nonarbitrary parsing (the
NP-completeness of "rival" reductions takes care of the rest).
This is no different from the isolation of an experimental system in
physics -- and it leaves computation as mind-independent as physics.
And if you rejoin that physics has the same "observer-dependence"
problem (perhaps even citing quantum mechanical puzzles as evidence
[which I would reject, by the way]), my reply is that computation is in
good company then, and computers are no more or less of a natural kind
than stones, birds or electrons.
Stevan Harnad
------------------
As an example, consider arithmetic, the scratches on paper, consisting
of "0", "1", "+" etc., the axioms (strings of scratches) and rules of
inference (applying to the scratches and strings of scratches). That's
a formal symbol system. The scratches on paper (symbol tokens) are
manipulated only on the basis of their shapes, not what they "mean."
(e.g., "0" is an arbitrary shape, and we have rules about what we can
do with that shape, e.g., "0 + 1 = 1 + 0" etc.).
That's the symbol system, and what we mean by numbers, equality, etc.,
is the systematic interpretation that can be PROJECTED onto those
scratches on paper, and they will bear the weight of that
interpretation. The very same scratches can also be given a few provably
coherent "nonstandard" interpretations, but in general, rival
interpretations simply won't fit. For example, you cannot take the same
set of scratches and interpret "=" as addition and "0" as equality and
still come up with a coherent interpretation.
The same is true with the Sonnets of Shakespeare as a set of symbols
interpretable as English, vs some other putative systematic
interpretation of the very same scratches on paper.
It does matter for this discussion of what computation is, because
computation is concerned only with systematically interpretable symbol
systems, not random gibberish.
There is more than one Turing Test (TT) at issue, and the differences
between them are critical. The standard TT is purely symbolic (symbols
in, symbols out) and calls for indistinguishability in all symbolic
performance only. The Total Turing Test (TTT) I have proposed in its
place (Harnad 1989, 1991) calls for indistinguishability in all
symbolic AND robotic (sensorimotor interactions with the world of
objects and events) performance. A lot rides on the TT vs. TTT
distinction.
Nonhuman species TTT's would of course be welcome, and empirically prior to
human TTT's, but unfortunately we lack both the ecological knowledge and
the intuitive capacity (based on shared human homologies) to apply
the TTT confidently to any species but our own. (This doesn't mean we
can't try, of course, but that too is not what's at issue in this
discussion, which is about what COMPUTATION is.)
I didn't say humans were computers, nontrivial or otherwise (they might
be, but it seems to me they're also a lot of other things that are more
relevant and informative). The question was about what COMPUTERS are.
And I think "nontrivial" is a very useful term, a reasonable goal for
discussion, and does not merely refer to what we have already
understood.
Incorrect. I focus on categorical (all-or-none) categories because I
think they, rather than graded categories, form the core of our
conceptual repertoire as well as its foundations (grounding).
Herb, I've trodden this ground many times before. You just said before
that you were a comparative psychologist. The ontology of the biosphere
is hence presumably not your data domain, but rather the actual
categorizing capacity and performance of human beings (and other
species). It does not matter a whit to the explanation of the mechanisms
of this performance capacity what the "truth" about montotremes, viruses
or priests is. Either we CAN categorize them correctly (with respect to
some Skinnerian consequence of MIScategorization, not with respect to
some Platonic reality that is none of our business as psychologists) or
we cannot. If we can, our success is all-or-none: We have not said that
cows are 99% mammals whereas monotremes are 80% mammals. We have said
that cows are mammals. And montotremes are whatever the biological
specialists (hewing to their OWN, more sophisticated consequences of
MIScategorization) tell us they are. And if we can't say whether a
priest is or is not a bachelor, that too does not make "bachelor" a
graded category. It just means we can't successfully categorize priests
as bachelors or otherwise!
We're modelling the cognitive mechanisms underlying our actual
categorization capacity; we're not trying to give an account of the
true ontology of categories. Nor is it relevant that we cannot
introspect and report the features (perfectly classical) that generate
our success in categorization: Who ever promised that the subject's
introspection would do the cognitive theorist's work for him? (These
are all lingering symptoms of the confused Roschian legacy I have been
inveighing against for years in my writings.)
Nope. The symbol grounding problem is the problem that formal symbol
systems do not contain their own meanings. They must be projected onto
them by outside interpreters. My candidate solution is robotic grounding;
there may be others. Leave formal truth to the philosophers and worry
more about how organisms (and robots) actually manage to be able to do
what they can do.
The arguments are, unfortunately, familiar mumbo-jumbo to me. Forget
about truth and ontology and return to the way organisms actually
behave in the world (including what absolute discriminations they can
and do make, and under what conditions): Successful (TTT-scale) models
for THAT is what we're looking for. Induction and "ceteris paribus" has
nothing to do with it!
I'm interested in what mechanisms will actually generate the
categorization capacity and performance of people (and animals). My own
models happen to use neural nets to learn the invariants in the sensory
projection of objects that will allow them to be categorized
"correctly" (i.e., with respect to feedback from the consequences of
MIScategorization). The "names" of these elementary sensorimotor
categories are then grounded elementary symbols that can enter into
higher-order combinations (symbolic representation), but inheriting the
analog constraints of their grounding.
I don't know what the ground-level elementary symbols will turn out to
be, I'm just betting they exist -- otherwise it's all hanging by a
skyhook. Nor do I know the Golden Mountain conundrum, but I do know the
putative "vanishing intersections" problem, according to which my
approach to grounding is hopeless because not even sensory categories
(not to mention abstract categories) HAVE any invariants at all: My
reply is that this is not an apriori matter but an empirical one, and
no one has yet tried to see whether bottom-up sensory grounding of a
TTT-scale robot is possible. They've just consulted their own (and
their subjects') introspections on the matter. I would say that our own
success in categorization is some inductive ground for believing that
our inputs are not too underdetermined to provide an invariant basis
for that success, given a sufficiently powerful category learning
mechanism.
In the event, it probably wasn't, but I managed to say what I meant
anyway. I have an iron-clad policy of not sending people off to look up
chapter and verse of what I've written on a topic under discussion; I
willingly recreate it on-line from first principles, as long as my
interlocutor does me the same courtesy -- and you haven't sent me off to
chapter and verse either. I find this policy easy enough to be faithful
to, because I don't have any ideas that cannot be explained in a few
paragraphs (nothing longer than a 3-minute idea). Nor have I encountered
many others who have longer ideas (though I have encountered many others
who have been longer-winded or fuzzier about describing them).
Regarding the non-role introspections play in your conceptualization,
see what you asked me about the essential features of bachelors above.
Why should I be able to introspect essential features, and what does it
prove if I can't? All that matters is that I can actually sort and
label bachelors as I do: Then finding the features I use become's the
THEORIST's problem, not the subject's.
I would suggest, by the way, that you abandon your uncertainty about
whether anybody's home inside you, experiencing experiences (as I
confidently assume there is in you, and am certain there is in me).
Cartesian reasons alone should be sufficient to persuade you that the
very possibility of experiencing uncertainty about whether there is
somebody home in your own case is self-contradictory, because
"uncertainty" or "doubt" is itself a experiential state.
The discussion is about what computers/computation are, and whether
there is any principled way to distinguish them from what
computers/computation aren't. In one view (not mine), what is a
computer is just a matter of interpretation, hence everything is and
isn't a computer depending on how you interpret it. In my view, one CAN
distinguish computers -- at least those that do nontrivial computation
-- on a complexity-theoretic basis, because systematic interpretations
of arbitrary objects are as hard to come by as chimpanzees typing
Shakespeare.
Now once we have settled on what computers/computation are (namely,
nontrivial symbol manipulation systems), we still face the symbol
grounding problem: These nontrivially interpretable systems still do
not "contain" their own interpretations. The interpretations must be
projected onto them by us. A grounded symbol system is one whose
robotic performance in the real world of objects and events to which
its symbols can be interpreted as referring squares systematically with
the interpretation. The symbol interpretations are then grounded in its
robotic performance capacity, not just in our projections.
References (nonobligatory) follow.
Cheers, Stevan
Harnad, S. (1989) Minds, Machines and Searle. Journal of Theoretical
and Experimental Artificial Intelligence 1: 5-25.
Harnad, S. (1990a) The Symbol Grounding Problem. Physica D 42: 335-346.
Harnad, S. (1990b) Against Computational Hermeneutics. (Invited
commentary of Eric Dietrich's Computationalism)
Social Epistemology 4: 167-172.
Harnad, S. (1990c) Lost in the hermeneutic hall of mirrors. Invited
Commentary on: Michael Dyer: Minds, Machines, Searle and Harnad.
Journal of Experimental and Theoretical Artificial Intelligence
2: 321 - 327.
Harnad, S. (1991) Other bodies, Other minds: A machine incarnation
of an old philosophical problem. Minds and Machines 1: 43-54.
Harnad, S. (1992) Connecting Object to Symbol in Modeling
Cognition. In: A. Clarke and R. Lutz (Eds) Connectionism in Context
Springer Verlag.
Hayes, P., Harnad, S., Perlis, D. & Block, N. (1992) Virtual Symposium
on the Virtual Mind. Minds and Machines (in press)
----------------------
Date: Sun, 29 Mar 92 18:39:31 EST
From: "Stevan Harnad"
Frank, thanks for the passage. As I noted earlier, not only I, but also
Dan Dennett came up with something like this independently. But I would
stress that the uniqueness (or near-uniqueness, modulo duals) of the
standard interpretation of a given symbol system, remarkable though it
is (and this remarkable property is at the heart of all of formal
mathematics), it is not enough to make that interpretation intrinsic to
the system: If the right interpretation is projected onto the system,
it will square systematically with the interpretation, but the
projection will still be from outside the system. That's good enough
for doing formal maths, but not enough for modelling a mind. For the
latter you need AT LEAST a grounded symbol system.
Stevan Harnad
Date: Tue, 31 Mar 92 19:38:10 EST
From: "Stevan Harnad"
SH: Ron, no, the "What is Computation" discussion was actually initiated
by a reply by John Searle to a passage from a 4-way "skywriting"
exchange that will be published in the journal Minds and Machines under
the title "Virtual Symposium on the Virtual Mind." The authors are Pat
Hayes, Don Perlis, Ned Block and me. The passage in question was by Pat
Hayes, in which he cited John Searle as claiming his wall was a
computer.
I will send you the full exchange separately. Meanwhile, you wrote:
SH: If I may interpolate some commentary: I agree about the physical
grounding as picking out this machine running WORDSTAR as a privileged
interpretation. I would add only two remarks.
(1) I think (though I can't prove it) that there is probably a
complexity-based way of picking out the privileged interpretation of a
system as a computer running a program (rather than other, more
arbitrary interpretations) based on parsimony alone.
(2) This discussion of what a computer is does not necessarily have any
bearing on the question of what the mind is, or whether the brain is a
computer. One could argue yes or no that computers/computation pick out
a nonarbitrary kind. And one can independently argue yes or no that
this has any substantive bearing on what kind of system can have a
mind. (E.g., I happen to agree with Searle that a system will not have
a mind merely because it implements the right computer program --
because, according to me, it must also be robotically grounded in the
world -- but I disagree that there is no nonarbitrary sense in which
some systems are computers and others are not. I.e., I agree with him
about [intrinsic] semantics but not about syntax.)
SH: It seems to me that everything admits of a trivial computational
description. Only things with a certain kind of (not yet adequately
specified) complexity admit of a nontrivial computational description
(and those are computers). Now things that have minds will probably
also admit of nontrivial computational descriptions, hence they too
will be computers, but only in a trivial sense insofar as their MENTAL
capacities are concerned, because they will not be ONLY computers, and
their noncomputational robotic properties (e.g., transducers/actuators
and other analog structures and processes) will turn out to be the
critical ones for their mental powers; and those noncomputational
properties will at the same time ground the semantics of the system's
symbolic states.
SH: I unfortunately can't explain this for Searle, because I happen to
disagree with him on this point, although I do recognize that no one has
yet come up with a satisfactory, principled way of distinguishing
computers from noncomputers...
SH: I don't think you'll be able to get computer scientists or
physicists excited about the factor of "causality" in the abstract, but
IMPLEMENTATION is certainly something they think about and have views
on, because a program is just as an abstraction until and unless it's
implemented (i.e., realized in a dynamical physical ["causal"] system
-- a computer). But there's still not much room for a convergence of
views there, because good "symbolic functionalists" hold that all the
particulars of implementation are irrelevant -- i.e., that the same
program can be implemented in countless radically different ways with
nothing in common except that they are all implementations of the same
computer program. Hence the right level to talk about is again the
purely symbolic (copmputational) one. I happen to disagree with these
symbolic functionalists insofar as the mind is concerned, but not
because I think there is something magic about the "causality" of
implementation, but because I think a symbol system is just as
ungrounded when it's implemented as when it's just scratches on
static paper. The mere implementation of a program on a computer is the
wrong kind of "causality" if a mind is what you're interested in
implementing (or even if it's an airplane or a furnace). What's needed
is the robotic (TTT) power to ground the interpretations of its
internal symbols in the robot's interactions with the real world of
objects, events and states of affairs that its symbols are
interpretable as being "about" (TTT-indistinguishably from our own
interactions with the world). (I list some of the publications in which
I've been trying to lay this out below.)
Stevan Harnad
------------------------------------------------------------
Harnad, S. (ed.) (1987) Categorical Perception: The Groundwork of
Cognition. New York: Cambridge University Press.
Harnad, S. (1989) Minds, Machines and Searle. Journal of Theoretical
and Experimental Artificial Intelligence 1: 5-25.
Harnad, S. (1990a) The Symbol Grounding Problem. Physica D 42: 335-346.
Harnad, S. (1990b) Against Computational Hermeneutics. (Invited
commentary of Eric Dietrich's Computationalism)
Social Epistemology 4: 167-172.
Harnad, S. (1990c) Lost in the hermeneutic hall of mirrors. Invited
Commentary on: Michael Dyer: Minds, Machines, Searle and Harnad.
Journal of Experimental and Theoretical Artificial Intelligence
2: 321 - 327.
Harnad, S. (1991) Other bodies, Other minds: A machine incarnation
of an old philosophical problem. Minds and Machines 1: 43-54.
Harnad, S. (1992) Connecting Object to Symbol in Modeling
Cognition. In: A. Clarke and R. Lutz (Eds) Connectionism in Context
Springer Verlag.
Hayes, P., Harnad, S., Perlis, D. & Block, N. (1992) Virtual Symposium
on the Virtual Mind. Minds and Machines (in press)
Andrews, J., Livingston, K., Harnad, S. & Fischer, U. (1992) Learned
Categorical Perception in Human Subjects: Implications for Symbol
Grounding. Proceedings of Annual Meeting of Cognitive Science Society
(submitted)
Harnad, S. Hanson, S.J. & Lubin, J. (1992) Learned Categorical
Perception in Neural Nets: Implications for Symbol Grounding.
Proceedings of Annual Meeting of Cognitive Science Society (submitted)
Harnad, S. (1993, in press) Icon, Category, Symbol: Essays on the
Foundations and Fringes of Cognition. Cambridge University
Press.
---------------------------------------------
Pat, I think the trivial case is covered by Church's Thesis and Turing
Equivalence. Consider a stone, just sitting there. It has one state,
let's call it "0." Trivial computational description. Now consider the
door, it has two states, open and shut; let's call one "0" and the
other "1." Trivial computational description.
I think that's pretty standard, and has to do with the elementariness of
the notion of computation, and hence that it can trivially capture,
among other things, every static or simple dynamic description of a
physical system. Nontrivial computation, on the other hand, is where
Searle and I diverge. I think that if someone can define nontrivial
computation in a principled way, it will separate computers from
noncomputers (the way trivial computation does not).
Unfortunately, I do not have such a principled criterion for nontrivial
computation except that (1) I think it will have a complexity-theoretic
basis, perhaps related to NP-Completeness of the search for
systematic rival interpretations of nontrivial symbol systems that
differ radically from the standard interpretation (or its provable
"duals"); and (2), even more vaguely, I feel the difference between
trivial and nontrivial computation will be all-or-none rather than a
matter of degree.
Earlier in this discussion it was pointed out that both Haugeland and
Dennett (and now apparently McCarthy before them, and perhaps even
Descartes -- see below) have also proposed a similar "cryptographer's
constraint" on the nonarbitrariness of a systematic interpretation of a
nontrivial symbol system (like natural language).
Stevan Harnad
Date: Wed, 1 Apr 92 09:09:47 EST
From: "Stevan Harnad"
Pat, alas, what "trivializes" the computational idea is Goedel, Turing,
Church, Post, von Neumann, and all the others who have come up with
equivalent formulations of what computation is: It's just a very
elementary, formal kind of thing, and its physical implementation is
equally elementary. And by the way, the same problem arises with
defining "symbols" (actually, "symbol-tokens," which are physical objects
that are instances of an abstract "symbol-type"): For, until further
notice, these too are merely objects that can be interpreted as
if they meant something. Now the whole purpose of this exercise is to
refute the quite natural conclusion that anything and everything can
be interpreted as if it meant something, for that makes it look as if
being a computer is just a matter of interpretation. Hence my attempt
to invoke what others have apparently dubbed the "cryptographer's
constraint" -- to pick out symbol systems whose systematic interpretation
is unique and hard to come by (in a complexity-based sense), hence not
arbitrary or merely dependent on the way we choose to look at them.
I also share your intuition (based on the programmable digital computer)
that a computer is something that is mechanically influenced by its
internal symbols (though we differ on two details -- I think it is only
influenced by the SHAPE of those symbols, you think it's influenced by
their MEANING [which I think would just put as back into the hermeneutic
circle we're trying to break out of], and of course you think a
conscious human implementation of a symbol system, as in Searle's
Chinese Room, somehow does not qualify as an implementation, whereas I
think it does). However, I recognize that, unlike in the case of
formalizing the abstract notion of computation above, no one has yet
succeeded in formalizing this intuition about physical implementation,
at least not in such a way as to distinguish computers from
noncomputers -- except as a matter of interpretation.
The "cryptographer's constraint" is my candidate for making this a
matter of INTERPRETABILITY rather than interpretation, in the hope that
this will get the interpreter out of the loop and let computers be
computers intrinsically, rather than derivatively. However, your own
work on defining "implementation" may turn out to give us a better way.
To assess whether it succeeds, however, we're going to have to hear
what your definition turns out to be! What you said above certainly
won't do the trick.
One last point: As I've said before in this discussion, it is a mistake
to conflate the question of what a computer is with the question of
what a mind is. Even if we succeed in showing that computers are
computers intrinsically, and not just as a matter of interpretation,
there remains the independent problem of "intrinsic intentionality"
(the fact that our thoughts are about what they are about
intrinsically, and not just as a matter of interpretation by someone
else). I, as you know, have recast this as the symbol grounding
problem, and have concluded that, because of it, the implementation of
a mind cannot possibly be merely the implementation of the "right"
symbol system. There ARE other things under the sun besides computers,
after all (indeed, confirming that is part of the goal of this
exercise), and other processes besides (nontrivial) computation, and
these will, I hypothesize, turn out to play an essential role in
grounding MENTAL symbols, which are NOT sufficiently specified by
their systematic interpretability alone: According to me, they must be
grounded in the system's robotic interaction with the real world of
objects that its symbols are "about," and this must likewise square
systematically with the interpretation of its symbols. If you wish,
this is a more rigorous "cryptographer's constraint," but this time a
physical one rather than a merely a formal one. (Minds will accordingly
turn out to be the TTT-scale class of "dedicated" computers, their
"situated" "peripherals" and other analog structures and processes
being essential substrates for their mental powers.)
Stevan Harnad
----------------
Martin, I'm sure what you wrote will be relevant, so please do send me
the reference. But can you also tell me whether you believe computers
(in the real world) can be distinguished from noncomputers in any way
that does not depend merely on how we choose to interpret their
"states" (I think they can, Searle and others think they can't)? Do you
think memory-size does it? Can we define "memory" interpretation-
independently (to exclude, say, ocean tides from being computers)? And
would your memory-size criterion mean that everything is a computer to
some degree? Or that nothing is a computer, but some things are closer
to being one than others? -- Cheers, Stevan
------------------
From: Ronald L Chrisley
Stevan:
I think there is a large degree of agreement between us:
Date: Tue, 31 Mar 92 19:38:10 EST
From: Stevan Harnad
> From: Ronald L Chrisley
SH: If I may interpolate some commentary: I agree about the physical
grounding as picking out this machine running WORDSTAR as a privileged
interpretation. I would add only two remarks.
(1) I think (though I can't prove it) that there is probably a
complexity-based way of picking out the privileged interpretation of a
system as a computer running a program (rather than other, more
arbitrary interpretations) based on parsimony alone.
This may be true, but I think that "parsimony" here will probably have
to make reference to causal relations.
(2) This discussion of what a computer is does not necessarily have any
bearing on the question of what the mind is, or whether the brain is a
computer. One could argue yes or no that computers/computation pick out
a nonarbitrary kind. And one can independently argue yes or no that
this has any substantive bearing on what kind of system can have a
mind. (E.g., I happen to agree with Searle that a system will not have
a mind merely because it implements the right computer program --
because, according to me, it must also be robotically grounded in the
world -- but I disagree that there is no nonarbitrary sense in which
some systems are computers and others are not. I.e., I agree with him
about [intrinsic] semantics but not about syntax.)
I agree. I only mentioned the cognitivist's claim for perspective.
Searle's claim that physics does not determine syntax is indeed
distinct from his claim that syntax does not determine semantics. I'm
very sympathetic with a grounded, embodied understanding of cognition.
But that doesn't mean that I have to agree with Searle that the claim
"mind is computation" is incoherent; it might just be wrong.
SH: It seems to me that everything admits of a trivial computational
description.
I pretty much said the same when I said even a stone could admit of a
computational description, and that such a notion of compoutation is
unenlightening. But consider: perhaps the injustice in South Africa
is something that does not even admit of a trivial computational
description...
Only things with a certain kind of (not yet adequately
specified) complexity admit of a nontrivial computational description
(and those are computers). Now things that have minds will probably
also admit of nontrivial computational descriptions, hence they too
will be computers, but only in a trivial sense insofar as their MENTAL
capacities are concerned, because they will not be ONLY computers, and
their noncomputational robotic properties (e.g., transducers/actuators
and other analog structures and processes) will turn out to be the
critical ones for their mental powers; and those noncomputational
properties will at the same time ground the semantics of the system's
symbolic states.
This might be; but we should nevertheless resist Searle's following claim:
> > JS: If the question is : "Is the wall/brain INTRINSICALLY a digital
(BTW: was Searle assuming that the others had read/heard of my
paper?!)
SH: I unfortunately can't explain this for Searle, because I happen to
disagree with him on this point, although I do recognize that no one has
yet come up with a satisfactory, principled way of distinguishing
computers from noncomputers...
I agree.
SH: I don't think you'll be able to get computer scientists or
physicists excited about the factor of "causality" in the abstract, but
IMPLEMENTATION is certainly something they think about and have views
on, because a program is just as an abstraction until and unless it's
implemented (i.e., realized in a dynamical physical ["causal"] system
-- a computer).
But Searle and Putnam have a point here: unless causality counts in
determining what is and what is not an implementation, then just about
anything can be seen as an implementation of anything else. So those
interested in implementation will have to pay attention to causality.
But there's still not much room for a convergence of
views there, because good "symbolic functionalists" hold that all the
particulars of implementation are irrelevant -- i.e., that the same
program can be implemented in countless radically different ways with
nothing in common except that they are all implementations of the same
computer program. Hence the right level to talk about is again the
purely symbolic (copmputational) one.
But perhaps the point to be made is that there's a lot more involved
in implementation than we previously realized. Symbolic
functionalists knew that it placed some restriction on the physics;
perhaps they just under-estimated how much.
I happen to disagree with these
symbolic functionalists insofar as the mind is concerned, but not
because I think there is something magic about the "causality" of
implementation, but because I think a symbol system is just as
ungrounded when it's implemented as when it's just scratches on
static paper. The mere implementation of a program on a computer is the
wrong kind of "causality" if a mind is what you're interested in
implementing (or even if it's an airplane or a furnace). What's needed
is the robotic (TTT) power to ground the interpretations of its
internal symbols in the robot's interactions with the real world of
objects, events and states of affairs that its symbols are
interpretable as being "about" (TTT-indistinguishably from our own
interactions with the world). (I list some of the publications in which
I've been trying to lay this out below.)
Yes, I'm very sympathetic with your writings on this point. Even
though the claim that everything realizes every Turing machine is
false, that merely makes the claim "to have a mind is to implement TM
No. xxx" coherent and false, not coherent and true. One still needs
grounding.
But the reverse is also true. In a section of my paper ("Symbol
Grounding is not sufficient"), I pointed out that one thing we can
take home from Searle's paper is that without some appeal to
causation, etc., in order to justify computational predicates, symbol
grounding is mere behaviorism. We can agree with Searle on that and
yet believe 1) that we *can* make the necessary appeals to causation
in order to make sense of computational predicates (such appeals are
implicit in our practice and theory); and 2) that symbol grounding,
although not sufficient, is necessary for a computational
understanding of mind.
Ronald L. Chrisley New College
---------------
Martin,
Because I tend to agree with you in believing that a principled and
interpretation-independent basis can be found for determining what is
and is not a computer (and perhaps universality will be part of that
basis), I'll leave it to other contributors to contest what you have
suggested above. I do want to point out, however, that what we are
trying to rule out here is arbitrary, gerrymandered interpretations of,
say, the microstructure (and perhaps even the surface blemishes) of a
stone according to which they COULD be mapped into the computations you
describe. Of course the mapping itself, and the clever mind that
formulated it, would be doing all the work, not the stone, but I think
Searle would want to argue that it's no different with the "real"
computer! The trick would be to show exactly why/how that rejoinder
would be incorrect. It is for this reason that I have groped for a
complexity-based (cryptographic?) criterion, according to which the
gerrymandered interpretation of the stone could somehow be ruled out as
too improbable to come by, either causally or conceptually, whereas the
"natural" interpretation of the SPARC running WORDSTAR would not.
Stevan Harnad
PS Pat Hayes has furnished yet another independent source for this
"cryptographer's constraint":
------------
From: Ronald L Chrisley
Stevan:
I think there is a large degree of agreement between us:
Date: Tue, 31 Mar 92 19:38:10 EST
From: Stevan Harnad
> From: Ronald L Chrisley
SH: If I may interpolate some commentary: I agree about the physical
grounding as picking out this machine running WORDSTAR as a privileged
interpretation. I would add only two remarks.
(1) I think (though I can't prove it) that there is probably a
complexity-based way of picking out the privileged interpretation of a
system as a computer running a program (rather than other, more
arbitrary interpretations) based on parsimony alone.
This may be true, but I think that "parsimony" here will probably have
to make reference to causal relations.
(2) This discussion of what a computer is does not necessarily have any
bearing on the question of what the mind is, or whether the brain is a
computer. One could argue yes or no that computers/computation pick out
a nonarbitrary kind. And one can independently argue yes or no that
this has any substantive bearing on what kind of system can have a
mind. (E.g., I happen to agree with Searle that a system will not have
a mind merely because it implements the right computer program --
because, according to me, it must also be robotically grounded in the
world -- but I disagree that there is no nonarbitrary sense in which
some systems are computers and others are not. I.e., I agree with him
about [intrinsic] semantics but not about syntax.)
I agree. I only mentioned the cognitivist's claim for perspective.
Searle's claim that physics does not determine syntax is indeed
distinct from his claim that syntax does not determine semantics. I'm
very sympathetic with a grounded, embodied understanding of cognition.
But that doesn't mean that I have to agree with Searle that the claim
"mind is computation" is incoherent; it might just be wrong.
SH: It seems to me that everything admits of a trivial computational
description.
I pretty much said the same when I said even a stone could admit of a
computational description, and that such a notion of compoutation is
unenlightening. But consider: perhaps the injustice in South Africa
is something that does not even admit of a trivial computational
description...
Only things with a certain kind of (not yet adequately
specified) complexity admit of a nontrivial computational description
(and those are computers). Now things that have minds will probably
also admit of nontrivial computational descriptions, hence they too
will be computers, but only in a trivial sense insofar as their MENTAL
capacities are concerned, because they will not be ONLY computers, and
their noncomputational robotic properties (e.g., transducers/actuators
and other analog structures and processes) will turn out to be the
critical ones for their mental powers; and those noncomputational
properties will at the same time ground the semantics of the system's
symbolic states.
This might be; but we should nevertheless resist Searle's following claim:
> > JS: If the question is : "Is the wall/brain INTRINSICALLY a digital
(BTW: was Searle assuming that the others had read/heard of my
paper?!)
SH: I unfortunately can't explain this for Searle, because I happen to
disagree with him on this point, although I do recognize that no one has
yet come up with a satisfactory, principled way of distinguishing
computers from noncomputers...
I agree.
SH: I don't think you'll be able to get computer scientists or
physicists excited about the factor of "causality" in the abstract, but
IMPLEMENTATION is certainly something they think about and have views
on, because a program is just as an abstraction until and unless it's
implemented (i.e., realized in a dynamical physical ["causal"] system
-- a computer).
But Searle and Putnam have a point here: unless causality counts in
determining what is and what is not an implementation, then just about
anything can be seen as an implementation of anything else. So those
interested in implementation will have to pay attention to causality.
But there's still not much room for a convergence of
views there, because good "symbolic functionalists" hold that all the
particulars of implementation are irrelevant -- i.e., that the same
program can be implemented in countless radically different ways with
nothing in common except that they are all implementations of the same
computer program. Hence the right level to talk about is again the
purely symbolic (copmputational) one.
But perhaps the point to be made is that there's a lot more involved
in implementation than we previously realized. Symbolic
functionalists knew that it placed some restriction on the physics;
perhaps they just under-estimated how much.
I happen to disagree with these
symbolic functionalists insofar as the mind is concerned, but not
because I think there is something magic about the "causality" of
implementation, but because I think a symbol system is just as
ungrounded when it's implemented as when it's just scratches on
static paper. The mere implementation of a program on a computer is the
wrong kind of "causality" if a mind is what you're interested in
implementing (or even if it's an airplane or a furnace). What's needed
is the robotic (TTT) power to ground the interpretations of its
internal symbols in the robot's interactions with the real world of
objects, events and states of affairs that its symbols are
interpretable as being "about" (TTT-indistinguishably from our own
interactions with the world). (I list some of the publications in which
I've been trying to lay this out below.)
Yes, I'm very sympathetic with your writings on this point. Even
though the claim that everything realizes every Turing machine is
false, that merely makes the claim "to have a mind is to implement TM
No. xxx" coherent and false, not coherent and true. One still needs
grounding.
But the reverse is also true. In a section of my paper ("Symbol
Grounding is not sufficient"), I pointed out that one thing we can
take home from Searle's paper is that without some appeal to
causation, etc., in order to justify computational predicates, symbol
grounding is mere behaviorism. We can agree with Searle on that and
yet believe 1) that we *can* make the necessary appeals to causation
in order to make sense of computational predicates (such appeals are
implicit in our practice and theory); and 2) that symbol grounding,
although not sufficient, is necessary for a computational
understanding of mind.
Ronald L. Chrisley New College
---------------
Date: Thu, 2 Apr 1992 16:27:18 -0500
From: Drew McDermott
Here's my two-cents worth on the "everything is a computer" discussion.
From: "Stevan Harnad"
Pat, I think the trivial case is covered by Church's Thesis and Turing
Equivalence. Consider a stone, just sitting there. It has one state,
let's call it "0." Trivial computational description. Now consider the
door, it has two states, open and shut; let's call one "0" and the
other "1." Trivial computational description.
> From: Pat Hayes
Unfortunately, I think this explanation by Stevan is not what Searle
meant. Searle means to say that "computers are in the mind of the
beholder." That is, if I take a system, and wish to view it as
performing a computational sequence S, I can map the thermal-noise
states (or any other convenient ways of partitioning its physical
states) into computational states in a way that preserves the
sequence. Putnam makes a similar claim in an appendix to, I think,
"Representation and Reality." A long discussion about this has been
going on in comp.ai.philosophy.
I agree with Stevan that Searle is wrong, and that computation is no
more a matter of subjective interpretation than, say, metabolism is.
However, I differ on where the problem arises:
[Stevan:]
Pat, alas, what "trivializes" the computational idea is Goedel, Turing,
Church, Post, von Neumann, and all the others who have come up with
equivalent formulations of what computation is: It's just a very
elementary, formal kind of thing, and its physical implementation is
equally elementary. And by the way, the same problem arises with
defining "symbols" (actually, "symbol-tokens," which are physical objects
that are instances of an abstract "symbol-type"): For, until further
notice, these too are merely objects that can be interpreted as
if they meant something. Now the whole purpose of this exercise is to
refute the quite natural conclusion that anything and everything can
be interpreted as if it meant something, for that makes it look as if
being a computer is just a matter of interpretation. Hence my attempt
to invoke what others have apparently dubbed the "cryptographer's
constraint" -- to pick out symbol systems whose systematic interpretation
is unique and hard to come by (in a complexity-based sense), hence not
arbitrary or merely dependent on the way we choose to look at them.
I also share your intuition (based on the programmable digital computer)
that a computer is something that is mechanically influenced by its
internal symbols (though we differ on two details -- I think it is only
influenced by the SHAPE of those symbols, you think it's influenced by
their MEANING [which I think would just put as back into the hermeneutic
circle we're trying to break out of], and of course you think a
conscious human implementation of a symbol system, as in Searle's
Chinese Room, somehow does not qualify as an implementation, whereas I
think it does). However, I recognize that, unlike in the case of
formalizing the abstract notion of computation above, no one has yet
succeeded in formalizing this intuition about physical implementation,
at least not in such a way as to distinguish computers from
noncomputers -- except as a matter of interpretation.
The "cryptographer's constraint" is my candidate for making this a
matter of INTERPRETABILITY rather than interpretation, in the hope that
this will get the interpreter out of the loop and let computers be
computers intrinsically, rather than derivatively. However, your own
work on defining "implementation" may turn out to give us a better way.
I don't think it matters one little bit whether the symbols
manipulated by a computer can be given any meaning at all. As I hope
I've made clear before, the requirement that computers' manipulations
have a meaning has been 'way overblown by philosopher types.
The real reason why not every system can be interpreted as a computer
is that the exercise of assigning interpretations to sequences of
physical states of a system does not come near to verifying that the
system is a computer. To verify that, you have to show that the
states are generated in a lawlike way in response to future events (or
possible events). It seems to me that for Searle to back up his claim
that his wall can be viewed as a computer, he would have to
demonstrate that it can be used to compute something, and of course he
can't.
This point seems so obvious to me that I feel I must be missing
something. Please enlighten me.
-- Drew
------------
Date: Mon, 6 Apr 92 00:31:21 EDT
Message-Id: <9204060431.AA00673@psycho>
To: mcdermott-drew@CS.YALE.EDU
Drew, I don't think anybody's very interested in uninterpretable formal
systems (like Hesse's "Glass Bead Game"). Not just computational
theory, but all of formal mathematics is concerned only with
interpretable formal systems. What would they be otherwise? Just
squiggles and squoggles we can say no more about (except that they
follow arbitrary systematic rules like, "after a sguiggle and a
squiggle comes a squaggle," etc.)? Now if THAT were all computation
was, I would be agreeing with Searle!
It's precisely the fact that it's interpretable as amounting to MORE
than just meaningless syntax that makes computation (and formal
symbol systems in general) special, and of interest. And you yourself
seem to be saying as much when you say "you have to show that the
states are generated in a lawlike way in response to future events (or
possible events)." For if this can be shown, then, among other things,
it will also have been shown that they were interpretable. And, by the
way, I don't think that a computer playing, say, backgammon, is going
to be shown to be a computer in virtue of "lawlike responses to future
and possible events." It's a computer because its states can be
systematically interpreted as playing backgammon -- and a lot of other
things (as suggested by those who have been stressing the criterion of
universality in this discussion).
Now I really don't think anything (even the human mind) can be
coherently said to "respond" to future (or possible) events, whether in
a lawlike or an unlawlike way (what is "lawlike," anyway, --
"interpretable as if governed by a law"?). So I can't see how your
proposed criteria help. But to answer your question about Searle: He
didn't say his wall could be USED to compute something, he said it
could be DESCRIBED as if it were computing something. And you say as
much in your own first paragraph.
Can you take a second pass at making your intutions explicit about this
"lawlike performance" criterion, and how it separates computers from
the rest? I think your criterion will have to be independent of the
uses we may want to put the computer to, because making their
computerhood depend on our uses sounds no better than making it depend
on our interpretations.
Stevan Harnad
------------------
Date: Fri, 3 Apr 1992 16:31:05 PST
From: Pat Hayes
Stevan,
I think that you (and others) are making a mistake in taking all the
mathematical models of computation to be DEFINITIONS of computation.
What makes it tempting to do so, I think, is the remarkable (and
surprising) phenomenon of universality: that apparently any computer
can be simulated on any other one, with enough resources. Trying to
prove this led the theoretical folks in the forties to seek a
definition, and it was tempting to choose some very simple device and
say that THAT defined computation, since universality meant that this
wasn't any kind of restriction, it seemed, on what could (possibly) be
computed. This enabled some good mathematics to be developed, but it
was only a leap of faith, rather like the P/=NP hypothesis now: indeed
it was actually called Church's Thesis, if you recall. And as time has
gone by it seems like it must be true, and one can kind of see why.
But to look in the literature and say that this means that computers
are DEFINED to be, say, Turing machines, or any other kind of
mathematical object, is just a philosophical mistake. You can't run a
Turing machine, for one thing, unless its engineered properly. (For
example, the symbols on the tape would have to be in a form in which
the processing box could read them, which rules out thermodynamic
states of walls or rolls of toilet paper with pebbles on, and so
forth.)
You might respond, well, what IS a computer, then? And my answer would
be that this is essentially an empirical question. Clearly they are
remarkable machines which have some properties unlike all other
artifacts. What are the boundaries of the concept? Who knows, and why
should I really care very much? For example, are neural net programs a
form of computer, or are they something completely different? I would
be inclined to say the former, but if someone wants to draw sharp lines
excluding them, thats just a matter of terminology.
One point from your last message for clarification:
No, I don't think that the processor has access to anything other than
the shape of the symbols (except when those symbols denote something
internal to the machine itself, as when it is computing the length of a
list: this point due to Brian Smith). I think we agree on this. But
sometimes that suffices to cause the machine to act in a way that is
systematically related to the symbol's meaning. All the machine has is
some bitstring which is supposed to mean 'plus', but it really does
perform addition.
---------
For the record, I agree with you about the need for grounding of
symbols to ultimately attach them to the world they purport to denote,
but I also think that language enables us to extend this grounding to
almost anything in the universe without actually seeing
(feeling/hearing/etc) it, to the extent that the sensory basis of the
glue is almost abstracted. One could imagine making a program which
'knew' a trmendous amount, could 'converse' well enough to pass the
Turing Test in spades, etc., but be blind, deaf, etc.: a brain in a
box. I think that its linguistic contact would suffice to say that its
internal representations were meaningful, but you would require that it
had some sensory contact. If we gave it eyes, you would say that all
its beliefs then suddenly acquired meaning: its protests that it could
remember the time when it was blind would be denied by you, since it
would not have been nailed down sufficiently to the world then. Ah no,
you would say to it: you only THOUGHT you knew anything then, in fact I
KNOW you knew nothing. While I would have more humility.
best wishes
Pat Hayes
-------
I agree it's an empirical question, but it's an empirical question we
better be prepared to answer if there is to be any real substance to the
two sides of the debate about whether or not the brain is (or is merely)
a computer, or whether or not a computer can have a mind.
If Searle is right about the Chinese Room (and I am right about the
Symbol Grounding Problem) AND there ARE things that are computers
(implemented symbol-manipulating systems) as well as things that are
NOT computers, then the former cannot have minds merely in virtue of
implementing the right symbol system.
But if Searle is right about the "ungroundedness" of syntax too (I
don't happen to think he is), the foregoing alternatives are incoherent,
because everything is a computer implementing any and every symbol
system.
I'm not sure what really performing addition is, but I do know what
really meaning "The cat is on the mat" is. And I don't think that when
either an inert book or a dynamical TT-passing computer produces the
string of symbols that is systematically interpretable as meaning "The
cat is on the mat" (in relation to all the other symbols and their
combinations) it really means "The cat is on the mat." And that is the
symbol grounding problem. I do believe, however, that when a
TTT-passing robot's symbols are not only (1) systematically
interpretable, but (2) those interpretations cohere systematically with
all the robot's verbal and sensorimotor interactions with the world of
objects, events and states of affairs that the symbols are
interpretable as being about, THEN when that robot produces the string
of symbols that is systematically interpretable as "The cat is on the
mat," it really means "The cat is on the mat."
Part of this is of course sci-fi, because we're not just imagining this
de-afferented, de-efferented entity, but even imagining what capacities,
if any, it would or could have left under those conditions. Let me say
where I think the inferential error occurs. I can certainly imagine a
conscious creature like myself losing its senses one by one and
remaining conscious, but is that imagined path really traversable? Who
knows what would be left of me if I were totally de-afferented and
de-efferented. Note, though, that it would not suffice to pluck out my
eye-balls, puncture my ears and peel off my skin to de-afferent me. You
would have to remove all the analog pathways that are simply inward
extensions of my senses. If you kept on peeling, deeper and deeper into
the nervous system, removing all the primary and secondary sensory
projections, you would soon find yourself close to the motor
projections, and once you peeled those off too, you'd have nothing much
left but the "vegetative" parts of the brain, controlling vital
functions and arousal, plus a few very sparse and enigmatic sensory and
sensorimotor "association" areas (but now with nothing left to
associate) -- nor would what was left in any way resemble the requisite
hardware for a computer (whatever that might be)!
Sure language is powerful, and once it's grounded, it can take you into
abstractions remote from the senses; but I would challenge you to try to
teach Helen Keller language if she had not been only deaf and dumb, but
she had had no sensory or motor functions at all!
But never mind all that. I will remain agnostic about what the robot
has to have inside it in order to have TTT power (although I suspect it
resides primarily in that analog stuff we're imagining yanked out
here), I insist only on the TTT-passing CAPACITY, not its necessarily
its exercise. Mine is not a "causal" theory of grounding that says the
word must "touch" its referent through some mystical baptismal "causal
chain." The reason, I think, a person who is paralyzed and has lost his
hearing, vision and touch might still have a mind is that the inner
wherewithal for passing the TTT is still intact. But we know people can
pass the TTT. A mystery candidate who can only pass the TT but not the
TTT is suspect, precisely because of Searle's Argument and the Symbol
Grounding Problem, for if it is just an implemented symbol system
(i.e., a "computer" running a program), then there's nobody home in
there.
The "need for grounding of symbols" is not merely "to ultimately attach
them to the world they purport to denote," it is so that they
denote the world on their own, rather than merely because we interpret
them that way, as we do the symbols in a book.
Stevan Harnad
-----------
: Thu, 2 Apr 92 10:28:14 EST
Joe:
Your point would be valid if it were not for the fact that "Y" and "Z"
in the above are assumed (recursively) to be either directly grounded
or grounded indirectly in something that is ultimatly directly
grounded. "X" inherits its grounding from Y and Z. E.g., if "horse" is
directly grounded in a robot's capacity to identify (and discriminate
and manipulate) horses on the basis of the sensorimotor interactions of
the robot's transducers and effectors with horses, and "stripes" is
likewise grounded, then "Zebra" in "A Zebra is a Horse with Stripes"
inherits that grounding, and the proof of it is that the robot can now
identify (etc.) a zebra upon its very first (sensorimotor) encounter
with one. Such is the power of a grounded symbolic proposition.
To put it another way, the meanings of the symbols in a grounded symbol
system must cohere systematically not only with (1) the interpretations
we outsiders project on them (that's a standard symbol system), but
also with (2) all of the robot's interactions with the world of
objects, events and states of affairs that the symbols are
interpretable as being about. No outsider or homunculus is needed to
mediate this systematic coherence; it is mediated by the robot's own
(TTT-scale) performance capacity, and in particular, of course, by
whatever the internal structures and processes are that underlie that
successful capacity. (According to my own particular grounding model,
these would be analog projections connected to arbitrary symbols by neural
nets that learn to extract the invariant features that make it possible
for the robot to categorize correctly the objects of which they are the
projections.)
A grounded symbol system is a dedicated symbol system, hence a hybrid
one. In a pure symbol system, the "shape" of a symbol is arbitrary with
respect to what it can be interpreted as standing for, and this
arbitrary shape is operated upon on the basis of formal rules (syntax)
governing the symbol manipulations. The only constraints on the
manipulations are formal, syntactic ones. The remarkable thing about
such pure symbol systems is that the symbols and symbol manipulations
can be given a coherent systematic interpretation (semantics). Their
short-coming, on the other hand (at least insofar as their suitability
as models for cognition is concerned), is that the interpretations with
which they so systematically cohere are nevertheless not IN the symbol
system (any more than interpretations are in a book): They are
projected onto them from the outside by us.
A grounded symbol system, by contrast, has a second set of constraints
on it, over and above the syntactic ones above (indeed, this second set
of constraints may be so overwhelming that it may not be useful to
regard grounded symbol systems as symbol systems at all): The
manipulation of both the directly grounded symbols and the indirectly
grounded symbols (which are in turn grounded in them) is no longer
contrained only by the arbitrary shapes of the symbols and the
syntactic rules operating on those shapes; it is also constrained (or
"co-determined," as you put it) by the NON-arbitrary shapes of the
analog projections to which the ground-level symbols are physically
connected by the category-invariance detectors (and, ultimately, to the
objects those are the projections of). Indeed, because grounding is
bottom-up, the non-arbitrary constraints are primary. ~X" is not free
to enter into symbolic combinations except if the category relations
the symbols describe square with the analog dictates of the
ground-level symbols and their respective connections with the analog
world of objects.
And the reason I say that such a dedicated symbol system may no longer
even be usefully regarded as a symbol system at all can be illustrated
if you try to imagine formal arithmetic -- Peano's Axioms, the formal
rules of inference, and the full repertoire of symbols: "0", "1" "="
"+", etc. -- with all the elementary symbols "hard-wired" to the actual
real-world quantities and operations that they are interpretable as
referring to, with all symbol combinations rigidly constrained by those
connections. This would of course not be formal arithmetic any more,
but a "dedicated model." (I don't think it would be a good model for
arithmetic COGNITION, by the way, because I don't think the elementary
arithmetic symbols are directly grounded in this way; I'm just using it
to illustrate the radical effects of nonarbitrary shape constraints on
a formal system.)
So you see this is certainly not traditional AI. Nor is it homuncular.
And what inferences it can make are hewing to more than one drummer --
the "higher" one of syntax and logic, but also the "lower" one of
causal causal connections with the analog world of objects. And I do
think that categorization is primary, rather than predication; to put
it another way, predication and its interpretation is grounded in
categorization. There is already categorization involved in "asserting"
that an object is "Y." Conjunction may be an innate primitive, or it
may be a primitive learned invariant. But once you can assert that this is
a horse by reliably identifying it as "Horse" whenever you encounter
it, and once you can do the same with "Stripes," then you are just a
blank symbol away from identifying whatever has a conjunction of their
invariants as "Zebra" (if that's the arbitrary symbol we choose to
baptize it with).
Stevan Harnad
NOT POSTED
Leva, I cannot post this to the list because as it stands it is
immediately and trivially satisfied by countless actual computers
running countless actual programs. I think you will have to follow
the discussion a little longer to see what is at issue with this
question of what is computation and what is a computer. Quick, vague,
general criteria just won't resolve things. -- Stepa
----------------
Date: Mon, 6 Apr 92 01:25:31 EST
From: David Chalmers
I don't think there's a big problem here. Of course an answer to the
question of whether "everything is a computer" depends on a criterion
for when a computer, or a computation, is being physically implemented.
But fairly straightforward criteria exist. While there is certainly
room for debate about just what should be included or excluded, any
reasonable criterion will put strong constraints on the physical form
of an implementation: essentially, through the requirement that the
state-transitional structure of the physical system mirror the formal
state-transitional structure of the computation.
Start with finite state automata, which constitute the simplest
formalism for talking about computation. An FSA is fixed by
specification of a set of states S1,...,Sn, a set of inputs
I1,...,Im, and a set of state-transition rules that map
This might look complex, but it's very straightforward: the causal
structure of the physical system must mirror the formal structure
of the FSA, under an appropriate correspondence of states.
Some consequences:
(1) Any physical system will implement various FSAs -- as every
physical system has *some* causal structure. e.g. the trivial
one-state FSA will be implemented by any system. There's no
single canonical computation that a given object is implementing;
a given object might implement various different FSAs, depending
on the state correspondence that one makes. To that extent,
computation is "interest-relative", but that's a very weak degree
of relativity: there's certainly a fact of the matter about
whether a given system is implementing a given FSA.
(2) Given a particular complex FSA -- e.g. one that a
computationalist might claim is sufficient for cognition -- it
will certainly not be the case that most objects implement it,
as most objects will not have the requisite causal structure.
There will be no mapping of physical states to FSA states such
that state-transitional structure is reflected.
Putnam has argued in _Representation and Reality_ that any system
implements any FSA, but that is because he construes the
state-transition requirement on the physical system as a mere
material conditional -- i.e. as if it were enough to find a mapping
so that pairs are followed by the right s' on the occasions
that they happen to come up in a given time interval; and if
never comes up, then the conditional is satisfied vacuously. Of
course the computationalist should construe the conditional as a
strong one, with counterfactual force: i.e. whenever and however
comes up, it must be followed by the right s'. Putnam's
mappings fail to satisfy this condition -- if were to have
come up another time, there's no guarantee that s' would have
followed. There has been a long and interesting discussion of
this topic on comp.ai.philosophy.
(3) Maybe someone will complain that by this definition, everything
is performing some computation. But that's OK, and it doesn't make
computation a useless concept. The computationalist claim is that
cognition *supervenes* on computation, i.e. that there are certain
computations such that any implementation of that computation will
have certain cognitive properties. That's still a strong claim,
unaffected by the fact that all kinds of relatively uninteresting
computations are being performed all over the place.
To the person who says "doesn't this mean that digestion is a
computation", the answer is yes and no. Yes, a given digestive
process realizes a certain FSA structure; but this is not a very
interesting or useful way to see it, because unlike cognition,
digestion does not supervene on computation -- i.e. there will be
other systems that realize the same FSA structure but that are not
performing digestion. So: particular instances of digestion may be
computations in a weak sense, but digestion as a type is not. It's
only useful to take a computational view for properties that are
invariant over the manner in which a computation is implemented.
(Of course, Searle argues that cognition is not such a property,
but that's a whole different can of worms.)
Finite state automata are a weak formalism, of course, and many
if not most people will want to talk in terms of Turing machines
instead. The extension is straightforward. We say that a
physical system realizes a given Turing machine if we can map
states of the system to states of the Turing-machine head, and
separately map states of the system to symbols on each
Turing-machine tape square (note that there will be a separate
mapping for each square, and for the head, and also for the
position of the head if we're to be complete), such that the
state-transitional structure of the system mirrors the
state-transitional structure of the Turing machine. For a
Turing machine of any complexity, this will be a huge constraint
on possible implementations.
So far, in talking about FSAs and Turing machines, we've really
been talking about what it takes to implement a computation,
rather than a computer. To be a computer presumably requires
even stricter standards -- i.e., that the system be universal.
But that is straightforward: we can simply require that the
system implement a universal Turing machine, using the criteria
above.
Personally I think that the notion of "computation" is more
central to cognitive science than the notion of "computer". I
don't see any interesting sense in which the human mind is a
universal computer. It's true that we have the ability to
consciously simulate any given algorithm, but that's certainly
not a central cognitive property. Rather, the mind is
performing a lot of interesting computations, upon which our
cognitive properties supervene. So it's probably most useful
to regard cognitive processes as implementing a given
non-universal Turing machine, or even an FSA, rather than a
universal computer.
So, it seems to me that there are very straightforward grounds
for judging that not everything is a computer, and that although
it may be true that everything implements some computation,
that's not something that should worry anybody.
Dave Chalmers.
------------
From: Stevan Harnad
David Chalmers
I agree with Dave Chalmers's criteria for determining what computation
and computers are, but, as I suggested earlier, the question of whether
or not COGNITION is computation is a second, independent one, and on
this I completely disagree:
"Supervenience" covers a multitude of sins (mostly sins of omission).
Whatever system turns out to be sufficient for having a mind, mental
states will "supervene" on it. I don't feel as if I've said much of a
mouthful there.
But it is a much more specific hypothesis that what the mind will
"supervene" on is the right computations. We've agreed that what's
special about computation is that there are many different ways to
implement the same computations. So if a mind supervenes on (the right)
computations because of their computational properties (rather than
because of the physical details of any particular implementation of
them), then it must supervene on ALL implementations of those
computations. I think Searle's Chinese Room Argument has successfully
pointed out that this will not be so, at least in the case of Searle's
own implementation of the hypothetical Chinese-TT-passing computations
-- except if we're willing to believe that his memorizing and executing
a bunch of meaningless symbols is sufficient to cause a second mind to
"supervene" on what's going on in his head -- something I, for one,
would not be prepared to believe for a minute.
Because of certain similarities (similarities that on closer scrutiny
turn out to be superficial), it was reasonable to have at first
entertained the "computationalist" thesis that cognition might be a
form of computation (after all, both thoughts and computations are put
together out of strings of "symbols," governed by rules, semantically
interpretable; both have "systematicity," etc.). But, because of the
other-minds problem, there was always a systematic ambiguity about the
standard Turing Test for testing whether a candidate system really had
a mind.
We thought TT-passing was a good enough criterion, and no more or less
exacting than the everyday criterion (indistinguishability from
ourselves) that we apply in inferring that any other body than our own
has a mind. But Searle showed this test was not exacting enough, because the
TT could in principle be passed by computations that were
systematically interpretable as a life-long correspondence with a pen
pal who was understanding what we wrote to him, yet they could also be
implemented without any understanding by Searle. So it turns out that we
would have been over-interpreting the TT in this case (understandably,
since the TT is predicated on the premise that to pass it is to
generate and respond to symbols in a way that is systematically
interpretable as -- and indistinguishable in any way from -- a
life-long correspondence with a real person who really understands what
we are writing). Such a test unfortunately trades on a critical
ambiguity arising from the fact that since the TT itself was merely
verbal -- only symbols in and symbols out -- there MIGHT have been only
computations (symbol manipulations) in between input and output.
Well now that Searle has shown that that's not enough, and the Symbol
Grounding Problem has suggested why not, and what might in fact turn
out to be enough (namely, a system that passes the robotic upgrade of
the TT, the Total Turing Test, able to discriminate, identify and
manipulate the objects, events and states of affairs that it's symbols
are systematically interpretable as being "about" in a way that is
indistinguishable from the way we do), it's clear that the only way to
resolve the ambiguity is to turn to abandon the TT for the TTT. But it
is clear that in order to pass the TTT a system will have to do more
than just compute (it must transduce, actuate, and probably do a lot of
analog processing), and the mind, if any, will have to "supervene" on
ALL of that -- not just the computations, which have already been shown
not to be mindful! Moreover, whatever real computation a TTT-passer
will be doing, if any, will be "dedicated" computation, constrained by
the analog constraints it inherits from its sensorimotor grounding. And
transducers, for example, are no more implementation-independent than
digestion is. So not every implementation of merely their computational
properties will be a transducer (or gastrointestinal tract) -- some
will be mere computational simulations of transducers, "virtual
transducers," and no mind (or digestion) will "supervene" on that.
Stevan Harnad
--------------
Date: Mon, 23 Mar 92 18:45:50 EST
From: Eric Dietrich
Stevan:
Maybe it's the season: sap is rising, bugs are buzzing, and
trees are budding -- but it seems to me that some progress has been
made on the question of computers, semantics, and intentionality.
(BTW: thank you for bouncing to me your discussion with Searle. I
enjoyed it.)
I agree with Searle on two points. First, nothing is
intrinsically a computer. And second, the big problem is not
universal realizability.
Furthermore, I agree with you that computation and
implementation are not the same thing, and that nontrivial symbol
systems will not have arbitrary duals because they have a certain
complex systematicity.
But, ...
1. Nothing is intrinsically a computer because nothing is
intrinsically anything. It's interpretation all the way down, as it
were.
2. Therefore, it's lack of imagination that prevents us from
swapping interpretations in general in English, arithmetic, and Lisp.
This lack of imagination is, though, is part of our epistemic
boundedness. We are not stupid, just finite. To keep things coherent,
and to swap all the meanings in English is something that we cannot
do. Perhaps no intelligent creature could do this because creatures
vastly more intelligent than we would have that much more
science -- explanations and semantics -- to juggle when trying to
invent and swap duals.
3. Still, we arrive at the same point: a wall is only an
implementation of a trivial turing machine or computation.
.
.
.
But, ... How can we arrive at the same point if I believe
that computers are NOT formal symbol manipulators while you and Searle
believe that they are? Because computation is an observer relative
feature precisely *because* semantics is. In other words, you can
interpret your wall, there just isn't much reason to do so. Planets
can be viewed as representing and computing their orbits, but there
isn't much reason to do so. Why? Because it involves too much "paper
work" for us. Other intelligent entities might prefer to
attribute/see such computations to the planets.
For me, computation, systematicity, and semantics are matters of
degree. Machines, computation, and meaning are in the eye of the
beholder, or more precisely, the explainer.
What recommends this view? Does it give us exactly the same
conclusions as your view? No, it is not the same.
Interpretationalism provides a different set of problems that must be
solved in order to build an intelligent artifact, problems that are
prima facie tractable. For example, on the interpretationalist view,
you don't have to solve the problem of original intentionality (or,
what is the same, the problem provided by the Chinese Room); nor do
you have to solve the symbol grounding problem (though you do have to
figure out how perception and categorization works). You can instead
spend your time searching for the algorithms (equivalently, the
architectures) responsible for our intelligence -- architectures for
plasticity, creativity and the like.
More deeply, it allows us the explanatory freedom to handle
the computational surprises that are no doubt in our future. In my
opinion, the semantical view espoused by you and Searle is too rigid
to do that.
And finally, interpretationalism holds out the promise that
cognitive science will integrate (integrate, NOT reduce) smoothly with
our other sciences. If intentionality is a real property of minds,
then minds become radically different from rocks. So different that I
for one despair of ever explaining them at all. (Where, for
example, do minds show up phylogenetically speaking? And why there
and not somewhere else? These are questions YOU must answer. I don't
have to.)
We don't need to preserve psychology as an independent
discipline by giving it phenomena to explain that don't exist anywhere
else in nature. Rather, we can preserve psychology because it
furthers our understanding in a way that we would miss if we stopped
doing it.
Sincerely,
Eric
---------------
"INTERPRETATIONALISM" AND ITS COSTS
Eric Dietrich
A view according to which particles have mass and spin ond obey
Newton's laws only as a matter of interpretation is undesirable not
only because it makes physics appear much more subjective and
impressionistic than necessary, but because it blurs a perfectly good
and informative distinction between the general theory-ladenness of all
scientific inferences and the special interpretation-dependence of the
symbols in a computer program (or a computer implementation of it). It
is the latter that is at issue here. There is, after all, a difference
between my "interpreting" a real plane as flying and my interpreting a
computer simulation of a plane as flying.
I don't know any reasons or evidence for believing that it is lack of
imagination that prevents us from being able to come up with coherent
interpretations for arbitrarily swapped symbols. NP-completeness sounds
like a good enough reason all on its own.
I think the reason planets don't compute their orbits has nothing to do
with paperwork; it is because planets are not computing anything. They
are describable as computing, and the computation is implementable as a
computer simulation of planetary motion (to an approximation), but
that's just because of the power of formal computation to approximate
(symbolically) any physical structure or process at all (this is
a variant of Church's Thesis).
Allowing oneself to be drawn into the hermeneutic hall of mirrors (and
leaving the virtual/real distinction at the door) can lead to illusory
after-effects even when one goes back into the real world. For not only
does one forget, while in the hall of mirrors, that the fact that
computations are interpretable as planetary motions does not make them
real planetary motions, but even when one re-enters the real world one
forgets that the fact that planets are describable as computing does
not mean they are really computing!
For me what distinguishes real planetary motion from a computer
simulation of it is definitely NOT a matter of degree. Ditto for
meaning and mind.
I adopt the simple intermediate position that if the meanings of
whatever symbols and computations are actually going on inside a robot are
grounded (TTT-indistinguishably) in the robot's sensorimotor
interactions (with the real world of objects that its symbols are
systematically interpretable as being about), then there are no
(solvable) problems left to solve, and the particular branch of reverse
bioengineering that is "cognitive science" will have done its work,
fully integrably with the rest of pure and applied science.
Of course, as with the computational modelling of planetry motion, a
great deal can be found out (empirically and analytically) about how to
get a robot to pass the TTT through simulations alone, but the
simulation itself is not the TTT and the simulated robot does not have
a mind. Alas, "interpretationalism" seems to lose this distinction.
Not at all. The do-able, empirical part of mind-modelling is
TTT-modelling, and that can in principle (though not in practice) be
accomplished for all species without ever having to answer the
(unanswerable) question of where mind starts and who/what does/doesn't
have a mind (apart from oneself). "Interpretationalism" can't answer
the question either, but it disposes of it at the very high price of
supposing (1) that everything has a mind to some degree and (2) that
the (real/virtual) difference between having any physical property P
and merely being systematically interpretable as having property P is
no difference at all -- at the price, in other words, of simultaneously
begging the question (2) and answering it by fiat (1)!
Stevan Harnad
----------------------------
----------------------------
Date: Tue, 7 Apr 92 23:56:53 PDT
From: sereno@cogsci.UCSD.EDU (Marty Sereno)
To: harnad@Princeton.EDU
Subject: Cells, Computers, and Minds
hi stevan
I have patiently read the many posts on the symbol-grounding problem
with interest for several years now. Many of the comments have
floundered around trying to find a clear definition of what it takes to
make a symbol-using system "really" understand something. They tend to
get tied up with various human artifacts, and it can be extremely
difficult to sort out the various sources of meaning-grounding. We can
avoid some of these problems by considering cells, which have the
distinction of being the first grounded symbol-using system--and one
whose grounding does not depend on any human artifact, or on humans at
all, for that matter.
The use of symbols strings in cells is well documented and rather
different than the use of symbol strings in human-designed computers.
The plan is to compare a computer to a cell, and then argue that human
symbol use looks more like that in cells.
The basic difference can be quite simply stated. Computers consist of
some kind of device that can read code strings and then write code
strings in a systematic, programmable way (with due respect to what has
been written on this topic). Reading and writing code is to perform
some kind of binary-like classification of symbol tokens (e.g., reading
4.8 volts to be the same as 5 volts). Computer designers have found
numerous ways to relate these written and read code strings to real
world tasks (e.g., A/D and D/A convertors, operators who understand
human and computer languages).
A cell reads code strings as well. Each living cell contains somewhere
between 1 and 200 megabytes of code. Messenger RNA sequences
transcribed from this permanent store are recognized by the cell during
the process of protein "translation" to contain codons each containing
3 nucleotides. Each nucleotide can each be described as having two
features: "long/short" (A and G [purines] vs. C and T [pyrimidines])
and "2/3 bonds" (A and T vs. G and C). The key point is that there are
no other examples of *naturally-occurring* systems that use long
code-strings like these that are conceivable *without* protein
translation or human thought (this disqualifies the immune system and
mathematical notation as independent naturally-occurring,
self-maintaining systems, for me at least).
But the way cells put these recognized symbols to work is remarkably
different than with computers. Instead of reading code for the purpose
of *operating on other code*, cells use the code to make proteins (esp.
enzymes), which they then use to maintain a metabolism. Proteins are
constructed by simply bonding amino acids into an (initially) 1-D chain
that is parallel to the recognized codons (words) in the messenger RNA
chain. Amino acids have none of the characteristics of nucleotide
symbol segment chains. Objective characteristics of (molecular) symbol
segment chains for me are: similar 3-D structure despite 1-D sequence
differences; small number of binary-like features for each segment;
their use as a 1-D chain in which small groups of segments are taken to
stand for a sequence of other, possibly non-symbolic things.
Proteins are extremely complex molecules, each containing thousands of
atoms in a precise 3-D arrangement. The DNA sequences in the genome,
however, constitute only a trivial portion of what would be required to
explicitly specify the 3-D structure of a protein; a single gene
typically contains only a few hundred bytes of information. This
information goes such a long way because it depends for its
interpretation on the existence of elaborate geometrical constraints
due to covalent chemical bonding, weak electronic interactions, the
hydrophobic effect, the structural details of the 20 amino acids, and
so on--a large set of 'hard-wired' effects that the cell harnesses, but
cannot change. Once the amino acid chain has been synthesized, its
self-assembly (folding) is directed entirely by these prebiotic,
non-symbolic chemical constraints.
Certain aspects of the architecture of cellular metabolism is much like
a production system. The enzymes ("productions") of metabolism operate
on their substrates ("objects") in a cytoplasm ("working memory"),
which requires that they have a great deal of specificity to avoid
inappropriate interactions. As in some kinds of production systems,
enzymes can operate on other enzymes as substrates. The key difference
is that the code in the cellular system is used strictly to make the
enzyme "productions"; once they are made, they fold up and operate
primarily in a non-symbolic milieu and on non-symbolic things in the
cytoplasm (this not exclusively the case; some proteins do in fact
control which part of the code is read).
No one in their right mind would want to make a computer more like a
cell for most of things that computers are currently used for. It is
much to hard too make arbitrary local changes in a cell's metabolism;
and evolution takes a miserably long time and involves large
populations. Molecular biologists, however, might, conversely, like to
engineer a cell into a computer by using overzealous error-correcting
polymerases to write DNA code. Code manipulations are not very fast
and would probably have to be rather local in cells, but it would be
easy to get billions or trillions of copies of a bacterial "program" in
a short time.
I suggest that we might take a cue from how cellular symbols are
grounded in thinking about how human symbols are grounded. Following
the cellular architecture, we might conjecture that the main use of
symbol strings for humans--in particular, external speech symbol
strings--is to construct an internal "mental metabolism". Small groups
of speech sounds are first internalized in auditory cortical areas, and
then small groups of them are recognized and taken to stand for other
non-symbolic internal patterns--e.g., visual category patterns in
higher cortical visual areas. Perhaps, human language involves relying
on pre-linguistic constraints on how sequentially activated and "bound
together" visual category activity patterns interact in higher primate
visual cortical areas. We could think of language as a kind of
code-directed scene comprehension that relies on implicit harnessing of
pre-existing constraints in a way analogous to the use of a complex
chemistry by cellular code strings. There is a similar compactness to
the code (a few hundred bytes of information specifies an enzyme and
the complex meaning of a discourse in the mind of the listener). It is
amazing to consider that the genetic code for an entire living,
reproducing, self-maintaining E. coli bacterium takes up less space
than the code for a decent word processor.
I would argue that a human-like symbol-using system depends on
harnessing complex dynamical constraints in a non-symbolic world, just
as cellular symbol systems depend on complex chemistry for their
grounding. It is not likely to be easy to construct such a "chemistry"
in an artificial machine. Real chemistry is extremely complex and the
specification of protein structure relies on many intricate details of
this complexity; it is not currently possible to predict the 3-D
structure of a protein given only the amino acid sequence. The
"chemistry" of interacting patterns in human neural networks is
undoubtedly even more complex. But there may be no other way to make a
grounded symbol-using system.
For a longer exposition of these ideas, see:
Sereno, M.I. (1991) Four analogies between biological and
cultural/linguistic evolution. Journal of Theoretical Biology
151:467-507.
Sereno, M.I. (1991) Language and the primate brain. Proceedings,
Thirteenth Annual Conference of the Cognitive Science Society, Lawrence
Erlbaum Assoc., pp. 79-84.
Though my note is a little long, please print it out before
singling out particular sentences for ridicule or praise...
marty
-----------
Date: Fri, 17 Apr 92 17:07:20 EDT
From: "Stevan Harnad"
ON SYMBOL SYSTEMS: DEDICATED, GROUNDED AND CELLULAR
Marty Sereno (sereno@cogsci.UCSD.EDU) wrote:
ms> cells... have the distinction of being the first grounded symbol-using
ms> system--and one whose grounding does not depend on any human artifact,
ms> or on humans at all, for that matter... The use of symbols strings in
ms> cells is well documented and rather different [from] the use of symbol
ms> strings in human-designed computers... But the way cells put these
ms> recognized symbols to work is remarkably different... Instead of
ms> reading code for the purpose of *operating on other code*, cells use
ms> the code to make proteins (esp. enzymes), which they then use to
ms> maintain a metabolism... The key difference is that the code in the
ms> cellular system is used strictly to make the enzyme "productions"; once
ms> they are made, they fold up and operate primarily in a non-symbolic
ms> milieu and on non-symbolic things in the cytoplasm...
ms>
ms> I would argue that a human-like symbol-using system depends on
ms> harnessing complex dynamical constraints in a non-symbolic world, just
ms> as cellular symbol systems depend on complex chemistry for their
ms> grounding. It is not likely to be easy to construct such a "chemistry"
ms> in an artificial machine... But there may be no other way to make a
ms> grounded symbol-using system.
A cell seems to be like a dedicated computer. A dedicated computer is
one for which the interpretations of some or all of its symbols are
"fixed" by the fact that it is hard-wired to its input and output. In
this sense, a dedicated chess-playing computer -- one whose inputs and
outputs are pysically connected only to a real chess board and
chess-men -- is a grounded symbol system (considered as a whole). Of
course, a dedicated chess-playing computer, even though it is grounded,
is still just a toy system, and toy systems are underdetermined in more
ways than one. To ground symbol meanings in such a way as to make them
completely independent of our interpretations (or at least no more nor
less indeterminate than they are), a symbol system must be not only
grounded but a grounded TTT-scale robot, with performance capacity
indistinguishable from our own.
In a pure symbol system, the "shapes" of the symbols are arbitrary in
relation to what they can be interpreted as meaning; in a dedicated or
grounded symbol system, they are not. A cell seems to be more than just
a dedicated computer, however, for mere dedicated computers still have
sizeable purely computational components whose function is
implementation-independent, hence they can be "swapped" for radically
different physical systems that perform the same computations. In a
dedicated chess-playing computer it is clear that a radically
different symbol-manipulator could be hard-wired to the same input and
output and would perform equivalent computations. It is not clear
whether there are any implementation-independent components that could
be swapped for radically different ones in a cell. This may either be a
feature of the "depth" of the grounding, or, more likely, an indication
that a cell is not really that much like a computer, even a dedicated
one. The protein-coding mechanisms may be biochemical modules rather
than formal symbols in any significant sense.
There's certainly one sense, however, in which cells and cellular
processes are not merely materials for analogies in this discussion,
because for at least one TTT-passing system (ourselves) they happen to
generate a real implementation! Now, although I am not a "symbolic"
functionalist (i.e., I don't believe that mental processes are
implementation-independent in the same way that software is
implementation-independent), I am still enough of a ("robotic")
functionalist to believe that there may be more than one way to
implement a mind, perhaps ways that are radically different from the
cellular implementation. As long as they have TTT-indistinguishable
performance capacity in the real world, I would have no nonarbitrary
grounds for denying such robots had minds.
ms> I suggest that we might take a cue from how cellular symbols are
ms> grounded in thinking about how human symbols are grounded. Following
ms> the cellular architecture, we might conjecture that the main use of
ms> symbol strings for humans--in particular, external speech symbol
ms> strings--is to construct an internal "mental metabolism". Small groups
ms> of speech sounds are first internalized in auditory cortical areas, and
ms> then small groups of them are recognized and taken to stand for other
ms> non-symbolic internal patterns--e.g., visual category patterns in
ms> higher cortical visual areas. Perhaps, human language involves relying
ms> on pre-linguistic constraints on how sequentially activated and "bound
ms> together" visual category activity patterns interact in higher primate
ms> visual cortical areas. We could think of language as a kind of
ms> code-directed scene comprehension that relies on implicit harnessing of
ms> pre-existing constraints in a way analogous to the use of a complex
ms> chemistry by cellular code strings.
This analogy is a bit vague, but I would certainly be sympathetic to
(and have indeed advocated) the kind of sensory grounding it seems to
point toward.
Stevan Harnad
-------------------
Date: Fri, 17 Apr 92 17:48:35 EDT
From: "Stevan Harnad"
Todd, I agree with this strategy for judging whether or not something
is computing (it is like the complexity-based criterion I proposed, and
the "cryptographic criterion" Dennett, Haugeland, McCarthy and perhaps
Descartes proposed), but it won't do for deciding whether the
interpretation is intrinsic or derived. For that, you need more than
interpretability (since it already presupposes interpretability).
My candidate is grounding in (TTT-scale) robotic interactions with
the world of objects the symbols are interpretable as being about.
Stevan Harnad
----------------------------------
From: Jeff Dalton
Steven Harnad writes:
One potential problem with the complexity constraint is that the
interpretations are expressed in a particular language (let us say).
An interpretation that is more complex in one language might be
simpler in another. Putnam makes a similar point about his "cats
are cherries" example, that which interpretation is the weird one
switches depending on whether you're expressing the interpretation
in the language where "cats" means cats or the one in which it
means cherries.
As a metaphor for this, consider random dot stereograms as an encoding
technique (something suggested to me by Richard Tobin). Someone mails
you a picture that consists of (random) dots. Is it a picture of the
Eiffel Tower, or a Big Mac? Well, they mail you another picture of
random dots and, viewed together with the first, you see a picture of
the Eiffel Tower. But they could just as well have mailed you a
different second picture that, together with the first, gave a Big
Mac.
Moreover, it is not true in general that the simpler interpretation is
always the right one. Someone who is encoding something can arrange
for there to be a simple interpretation that is incorrect. I suppose
an example might be where the encrypted form can be decrypted to an
English text, but the actual message can only be found buy taking the
(English) words that appear after every third word that contains an "a".
-- jeff
Date: Sat, 18 Apr 92 12:58:51 EDT
From: "Stevan Harnad"
COMPLEXITY, PARSIMONY and CRYPTOLOGY
Jeff Dalton
As I understand the Chaitin/Kolmogorov complexity-based criterion for
parsimony and randomness (Chaitin 1975; Rabin 1977), an algorithm (a
string of bits) is nonrandom and parsimonious to the degree that the
number of bits in it is smaller than the number of bits in the "random"
string (which is usually infinitely long) that it can be used to
generate. The measure of parsimony is the relative size of the short
("theory") and long ("data") bit string. It is stressed that language and
notational variations may alter the length of the algorithm by a few
bits, but that all variants would still be an order of magnitude
smaller than the data string (and therein lies the real parsimony).
Now I realize that the C/K criterion is only a thesis, but I think it
conveys the intuition that I too would have: that the relative ease with
which some things can be expressed in English rather than French (or
FORTRAN rather than ALGOL) is trivial relative to the fact that they
can be expressed at all, either way.
Two qualifications, however:
(1) The C/K criterion applies to algorithms as uninterpreted strings of
bits that "generate" much longer uninterpreted strings of bits. The
short and long strings are interpretable, respectively, as theory and
data, but -- as usual in formal symbol systems -- the interpretation is
external to the system; the counting applies only to the bits. So
although I don't think it is circular or irrelevant to invoke the C/K
analogy as an argument for discounting linguistic and notational
differences, doing so does not go entirely to the heart of the matter
of the parsimony of an INTERPRETATION (as opposed to an uninterpreted
algorithm).
(2) Another potential objection is more easily handled, however, and
again without any circularity (just some recursiveness): When one is
assessing the relative complexity of an algorithm string and the (much
longer) data string for which it is an algorithm, the potential
differences among the languages in which one formulates the algorithm
(and the data) clearly cannot include potential gerrymandered
languages whose interpretation itself requires an algorithm of the same
order of magnitude as the data string! That's precisely what this
complexity-based/cryptographic criterion is invoked to rule out!
This metaphor may be relevant to the cognitive process by which we
DISCOVER an interpretation, but it doesn't apply to the complexity
question, which is independent of (or perhaps much bigger than)
cognition. If we take the features that make random dots look like the
Eiffel Tower versus a Big Mac, those features, and the differences
between them, are tiny, compared to the overall number of bits in a
random dot array. Besides, to be strictly analogous to the case of the
same algorithm formulated in two languages yielding radically different
complexities, ALL the random dots would have to be interpretable using
either algorithm, whereas the trick with Julesz figures is that only a
small subset of the random dots is interpretable (those constituting
the figure -- Eiffel Tower or Big Mac, respectively) and not even the
same random dots in both cases. (I would also add that the highly
constrained class of perceptually ambiguous figures (like the Necker
Cube) is more like the rare cases of "dual" interpretability I've
already noted.)
Again, this seems to have more to do with the cognitive problem of how
to DISCOVER an interpretation than with the question of whether radically
different alternative interpretations (for the same symbol system)
exist and are accessible in real time. I would also say that the
differences in complexity between variant (but coherent)
interpretations of the kind you cite here would be tiny and trivial
compared to the complexity required to interpret a symbol system after
swapping the interpretations of an arbitrary pair of symbol types
(such as "if" and "not").
Once you've successfully decrypted something as English, for example,
it is trivial to add a second-oder decryption in which a particular
message (e.g., in English, or even in French) is embedded after every
third word containing an "a." All that would require (if this analogy
between algorithms and interpretations is tenable at all) is a few more
bits added to the original interpretative algorithm -- which would
still leave both algorithms MUCH closer to one another than to the
infinite corpus that they both decrypt.
Now there is an analogous argument one might try to make for if/not
swapping too: Take the standard English interpretative algorithm and
interpret "not" as if it meant "if" and vice versa: Just a few extra
bits! But this is not what radical alternative interpretation refers
to. It's not just a matter of using real English, but with the symbols
"if" and "not" swapped (i.e., it's not just a matter of decoding "Not
it rains then you can if go out" as "If it rains then you can not go
out"). You must have another interpretative algorithm altogether, a
"Schmenglish" one, in which the INTERPRETATION of "if" and "not" in
standard English strings like "If it rains then you can go out" (plus
all the rest of standard English) are given a coherent systematic
alternative interpretation in which "if" MEANS "not" and vice versa: A
much taller order, and requiring a lot more than a few bits tacked on!
Stevan Harnad
-------
Chaitin, G. (1975) Randomness and mathematical proof.
Scientific American 232: 47 - 52.
Rabin, M. O. (1977) Complexity of computations.
Communications of the Association of Computer Machinery 20:625-633.
-------------------------------------------------
Date: Sun, 12 Apr 1992 07:58:41 -0400
From: Drew McDermott
Let's distinguish between a computer's states' being
"microinterpretable" and "macrointerpretable." The former case is what
you assume: that if we consider the machine to be a rewrite system, the
rewrite rules map one coherently interpretable state into another. Put
another way, the rewrite rules specify a change in belief states of the
system. By contrast, the states of a macrointerpretable system "sort of
line up" with the world in places, but not consistently enough to
generate anything like a Tarskian interpretation. What I think you've
overlooked is that almost all computational processes are at best
macrointerpretable.
Take almost any example, a chess program, for instance. Suppose that
the machine is evaluating a board position after a hypothetical series
of moves. Suppose the evaluation function is a sum of terms. What does
each term denote? It is not necessary to be able to say. One might, for
instance, notice that a certain term is correlated with center control,
and claim that it denotes "the degree of center control," but what does
this claim amount to? In many games, the correlation will not hold, and
the computer may as a consequence make a bad move. But the evaluation
function is "good" if most of the time the machine makes "good moves."
The chess program keeps a tree of board positions. At each node of this
tree, it has a list of moves it is considering, and the positions that
would result. What does this list denote? The set of moves "worth
considering"? Not really; it's only guessing that these moves are worth
considering. We could say that it's the set the machine "is
considering," but this interpretation is trivial.
We can always imose a trivial interpretation on the states of the
computer. We can say that every register denotes a number, for
instance, and that every time it adds two registers the result denotes
the sum. The problem with this idea is that it doesn't distinguish the
interpreted computers from the uninterpreted formal systems, because I
can always find such a Platonic universe for the states of any formal
system to "refer" to. (Using techniques similar to those used in
proving predicate calculus complete.)
More examples: What do the states of a video game refer to? The Mario
brothers? Real asteroids?
What do the data structures of an air-traffic control system refer to?
Airplanes? What if a blip on the screen is initially the result of
thermal noise in the sensors, then tracks a cloud for a while, then
switches to tracking a flock of geese? What does it refer to in that
case?
Halfway through an application of Newton's method to an optimization
problem involving process control in a factory, what do the various
inverted Hessian matrices refer to? Entities in the factory? What in
the world would they be? Or just mathematical entities?
If no other argument convinces you, this one should: Nothing prevents
a computer from having inconsistent beliefs. We can build an expert
system that has two rules that either (a) cannot be interpreted as
about medical matters at all; or (b) contradict each other. The system,
let us say, happens never to use the two rules on the same case, so
that on any occasion its advice reflects a coherent point of view.
(Sometimes it sounds like a homeopath, we might say, and sometimes like
an allopath.) We would like to say that overall the computer's
inferences and pronouncements are "about" medicine. But there is no way
to give a coherent overall medical interpretation to its computational
states.
I could go on, but the point is, I hope, clear. For 99.9% of all
computer programs, either there is only a trivial intepretation of a
program's state as referring to numbers (or bit strings, or booleans);
or there is a vague, unsystematic, error-prone interpretation in terms
of the entities the machine is intended to concern itself with. The
*only* exceptions are theorem-proving programs, in which these two
interpretations coincide. In a theorem prover, intermediate steps are
about the same entities as the final result, and the computational
rules getting you from step to step are isomorphic to the deductive
rules that justify the computational rules. But this is a revealing
exception. It's one of the most pervasive fallacies in computer science
to see the formal-systems interpretation of a computer has having some
implications for the conclusions it draws when it is interpreted as a
reasoning system. I believe you have been sucked in by this fallacy.
The truth is that computers, in spite of having trivial interpretations
as deductive systems, can be used to mimic completely nondeductive
systems, and that any semantic framework they approximate when viewed
this way will bear no relation to the low-level deductive semantics.
I suspect Searle would welcome this view, up to a point. It lends
weight to his claim that semantics are in the eye of the beholder.
One way to argue that an air-traffic control computer's states denote
airplanes is to point out that human users find it useful to
interpret them this way on almost every occasion. However, the point
at issue right now is whether semantic interpretability is part of the
definition of "computer." I argue that it is not; a computer is what
it is regardless of how it is interpreted. I buttress that
observation by pointing out just how unsystematic most interpretations
of a computer's states are. However, if I can win the argument about
whether computers are objectively given, and uninterpreted, then I
can go on to argue that unsystematic interpretations of their states
can be objectively given as well.
-- Drew McDermott
---------------
From: Stevan Harnad
Drew McDermott
Drew, you won't be surprised by my immediate objection to the word
"belief" above: Until further notice, a computer has physical states,
not belief states, although some of those physical states might be
interpretable -- whether "macro" or "micro" I'll get to in a moment --
AS IF they were beliefs. Let's pretend that's just a semantic quibble
(it's not, of course, but rather a symptom of hermeneutics creeping in;
however, let's pretend).
You raise four semi-independent issues:
(1) Does EVERY computer implementing a program have SOME states that are
interpretable as referring to objects, events and states of affairs, the
way natural language sentences are?
(2) Are ALL states in EVERY computer implementing a program interpretable as
referring... (etc.)?
(3) What is the relation of such language-like referential
interpretability and OTHER forms of interpretability of states of a
computer implementing a program?
(4) What is the relation of (1) - (3) to the software hierarchy, from
hardware, to machine-level language, to higher-level compiled
languages, to their English interpretations?
My answer would be that not all states of a computer implementing a
program need be interpretable, and not all the interpretable states
need be language-like and about things in the world (they could be
interpretable as performing calculations on numbers, etc.), but ENOUGH
of the states need to be interpretable SOMEHOW, otherwise the computer
is just performing gibberish (and that's usually not what we use
computers to do, nor do we describe them as such), and THAT's the
interpretability that's at issue here.
Some of the states may have external referents, some internal referents
(having to do with the results of calculations, etc.). And there may be
levels of interpretation, where the higher-level compiled languages
have named "chunks" that are (macro?)interpretable as being about
objects, whereas the lower-level languages are (micro?)interpretable
only as performing iterative operations, comparisons, etc. Although
it's easy to get hermeneutically lost in it, I think the software
hierarchy, all the way up to the highest "virtual machine" level, does
not present any fundamental mysteries at all. Low-level operations are
simply re-chunked at a higher level so more general and abstract
computations can be performed. I can safely interpret a FORTRAN
statement as multiplying 2 x 2 without worrying about how that's
actually being implemented at the machine-language or hardware level --
but it IS being implemented, no matter how complicated the full
hardware story for that one operation would be.
I'm not sure what an evaluation function is, but again, I am not saying
every state must be interpretable. Even in natural language there are
content words (like "king" and "bishop") that have referential
interpretations and function words ("to" and "and") that have at best
only syntactic or functional interpretations. But some of the internal
states of a chess-plying program surely have to be interpretable as
referring to or at least pertaining to chess-pieces and chess-moves, and
those are the ones at issue here. (Of course, so are the mere "function"
states, because they too will typically have something to do with (if
not chess then) calculation, and that's not gibberish either.
And although I might make that interpretation for convenience in
describing or debugging the program (just as I might make the
celebrated interpretation that first got Dan Dennett into his
"intentional stance," namely, that "the computer thinks it should get
it's queen out early"), I would never dream of taking such
interpretations literally: Such high level mentalistic interpretations
are simply the top of the as-if hierarchy, a hierarchy in which
intrinsically meaningless squiggles and squoggles can be so interpreted
that (1) they are able to bear the systematic weight of the
interpretation (as if they "meant" this, "considered/believed/thought"
that, etc.), and (2) the interpretations can be used in (and even sometimes
hard-wired to) the real world (as in interpreting the squiggles and
squoggles as pertaining to chess-men and chess-moves).
I'm not sure what you mean, but I would say that whether they are
scratches on a paper or dynamic states in a machine, formal symbol
systems are just meaningless squiggles and squoggles unless you project
an interpretation (e.g., numbers and addition) onto them. The fact that
they will bear the systematic weight of that projection is remarkable
and useful (it's why we're interested in formal symbol systems at all),
but certainly not evidence that the interpretation is intrinsic to the
symbol system; it is only evidence of the fact that the system is
indeed a nontrivial symbol system (in virtue of the fact that it is
systematically interpretable). Nor (as is being discussed in other
iterations of this discussion) are coherent, systematic "nonstandard"
alternative interpretations of formal symbol systems that easy to come
by.
They are interpretable as pertaining (not referring, because there's no
need for them to be linguistic) to (indeed, they are hard-wireable to)
the players and moves in the Mario Brothers game, just as in chess. And
the graphics control component is interpretable as pertaining to (and
hard-wireable to the bit-mapped images of) the icons figuring in the
game. A far cry from uninterpretable squiggles and squoggles.
I don't know the details, but I'm sure a similar story can be told
here: Certain squiggles and squoggles are systematically interpretable
as signaling (and mis-signaling) the presence of an airplane, and the
intermediate calculations that lead to that signaling are likewise
interpretable in some way. Running computer programs are, after all,
not black boxes inexplicably processing input and output. We design
them to do certain computations; we know what those computations are;
and what makes them computations rather than gibberish is that they are
interpretable.
The fact that the decomposition is not simple does not mean that the
intermediate states are all or even mostly uninterpretable.
I can't follow this: The fact that a formal system is inconsistent, or
can potentially generate inconsistent performance, does not mean it is
not coherently interpretable: it is interpretable as being
inconsistent, but as yielding mostly correct performance nevertheless.
[In other words, "coherently interpretable" does not mean
"interpretable as coherent" (if "coherent" presupposes "consistent").]
And, ceterum sentio, the system has no beliefs; it is merely
systematically interpretable as if it had beliefs (and inconsistent
ones, in this case). Besides, since even real people (who are likewise
systematically interpretable, but not ONLY systematically
interpretable: also GROUNDED by their TTT-powers in the real world) can
have inconsistent real beliefs, I'm not at all sure what was meant to
follow from your example.
My view puts no special emphasis on logical deduction, nor on being
interpretable as doing logical deduction. Nor does it require that
a system be interpretable as if it had only consistent beliefs (or any
beliefs at all, for that matter). It need be interpretable only in the
way symbol strings in English, arithmetic, C or binary are interpretable.
If you agree with Searle that computers can't be distinguished from
non-computers on the basis of interpretability, then I have to ask you
what (if anything) you DO think distinguishes computers from
non-computers? Because "Everything is a computer" would simply
eliminate (by fiat) the substance in any answer at all to the question
"Can computers think?" (or any other question about what can or cannot
be done by a computer, or computationally). Some in this discussion
have committed themselves to universality and a complexity-based
criterion (arbitrary rival interpretations are NP-complete). Where do
you stand?
Stevan Harnad
------------------
From: Brian C Smith
I can't help throwing in a number of comments into this discussion:
1) ON UNIVERSALITY: All metrics of equivalence abstract away from
certain details, and focus on others. The metrics standardly used to
show universality are extraordinarily coarse-grained. They are (a)
essentially behaviourist, (b) blind to such things as timing, and (c)
(this one may ultimately matter the most), promiscuous exploiters of
implementation, modelling, simulation, etc. Not only does it strike me
as extremely unlikely that (millenial versions of) "cognitive",
"semantic", etc., will be this coarse-grained, but the difference
between a model and the real thing (ignored in the standard equivalence
metrics) is exactly what Searle and others are on about.
It therefore does not follow, if X is cognitive, and Y provably
equivalent to it (in the standard theoretic sense), that Y is
cognitive.
This considerations suggest not only that universality may be of no
particular relevance to cognitive science, but more seriously that it
is somewhere between a red herring and a mine field, and should be
debarred from arguments of cognitive relevance.
2) ON ORIGINAL INTENTIONALITY: Just a quick one. In some of the notes,
it seemed that *intrinsic* and *attributed* were being treated as
opposites. This is surely false. Intrinsic is presumably opposed to
something like extrinsic or relational. Attributed or observer-
supplied is one particular species of relational, but there are many
others. Thus think about the property of being of average height.
This property doesn't inhere within an object, but that doesn't make
it ontologically dependent on observation or attribution (at least no
more so that anything else [cf. Dietrich]).
There are lots of reasons to believe that semantics, even original
semantics, will be relational. More seriously, it may even be that our
*capacity* for semantics is relational (historical, cultural, etc. --
this is one way to understand some of the deepest arguments that
language is an inexorably cultural phenomenon). I.e., it seems to me a
mistake to assume that *our* semantics is intrinsic in us. So arguing
that computers' semantics is not intrinsic doesn't cut it as a way to
argue against computational cognitivism.
3) ON FORMAL SYMBOL MANIPULATION: In a long analysis (20 years late,
but due out soon) I argue that actual, real-world computers are not
formal symbol manipulators (or, more accurately, that there is no
coherent reading of the term "formal" under which they are formal).
Of many problems, one that is relevant here is that the inside/
outside boundary does not align with the symbol/referent boundary --
a conclusion that wreaks havoc on traditional notions of transducers,
claims of the independence of syntax and semantics, the relevance of
"brain in a vat" thought experiments, etc.
4) ON THE "ROBOTIC" SOLUTION: Imagine someone trying to explain piano
music by starting with the notion of a melody, then observing that more
than one note is played at once, and then going on to say that there
must also be chords. Maybe some piano music can be described like that:
as melody + chords. But not a Beethoven sonata. The consequence of
"many notes at once" is not that one *adds* something (chords) to the
prior idea of a single-line melody. Once you've got the ability to have
simultaneous notes, the whole ball game changes.
I worry that the robotic reply to Searle suffers the same problem.
There's something right about the intuition behind it, having to do
with real-world engagement. But when you add it, it is not clear
whether the original notion (of formal symbol manipulation, or even
symbol manipulation at all) survives, let alone whether it will be a
coherent part of the expanded system. I.e., "symbol + robotic
grounding" seems to me all too similar to "melody + chords".
If this is true, then there is a very serious challenge as to what
notions *are* going to explain the expanded "engaged with the real
world" vision. One question, the one on the table, is whether or not
they will be computational (my own view is: *yes*, in the sense that
they are exactly the ones that are empirically needed to explain
Silicon Valley practice; but *no*, in that they will neither be an
extension to nor modification of the traditional formal symbol
manipulation construal, but will instead have to be redeveloped from
scratch). More serious than whether they are computational, however, is
what those notions *will actually be*. I don't believe we know.
5) ON TYPES: On March 22, Gary Hatfield raised a point whose
importance, I believe, has not been given its due. Over the years,
there have been many divisions and distinctions in AI and cognitive
science: neat vs. fuzzy; logicist vs. robotic; situated vs.
non-situated; etc. I have come to believe, however, that far and away
the most important is whether people assume that the TYPE STRUCTURE of
the world can be taken as explanatorily and unproblematically given, or
whether it is something that a theory of cognition/computation
/intentionality/etc. must explain. If you believe that the physical
characterisation of a system is given (as many writers seem to do), or
that the token characterisation is given (as Haugeland would lead us to
believe), or that the set of states is given (as Chalmers seems to), or
that the world is parsed in advance (as set theory & situation theory
both assume), then many of the foundational questions don't seem to be
all that problematic.
Some of us, however, worry a whole lot about where these type
structures come from. There is good reason to worry: it is obvious,
once you look at it, that the answers to all the interesting questions
come out different, if you assume different typing. So consider the
disussions of physical implementation. Whether there is a mapping of
physical states onto FSA states depends on what you take the physical
and FSA states to be. Not only that, sometimes there seems to be no
good reason to choose between different typings. I once tried to
develop a theory of representation, for example, but it had the
unfortunate property that the question of whether maps were isomorphic
representations of territory depended on whether I took the points on
the maps to be objects, and the lines to be relations between them, or
took the lines to be objects and the points to be relations (i.e.,
intersections) between *them*. I abandoned the whole project, because
it was clear that something very profound was wrong: my analysis
depended far too much on my own, inevitably somewhat arbitrary,
theoretic decisions. I, the theorist, was implicitly, and more or less
unwittingly, *imposing* the structure of the solution to my problem
onto the subject matter beforehand.
Since then, I have come to believe that explaining the rise of ontology
(objects, properties, relations, types, etc.) is part and parcel of
giving an adequate theory of cognition. It's tough sledding, and this
is not the place to go into it. But it is important to get the issue of
whether one believes that one can assume the types in advance out onto
the table, because I think implicit disagreement over this almost
methodological issue can subvert communication on the main problems of
the day.
Brian Smith
(P.S.: Is there a reason not to have a mailing list that each of us can
post to directly?)
[The symbol grounding list is not an unmoderated list; it is moderated
by me. I post all substantive messages, but if it were unmoderated
it would quickly degenerate into what goes on on comp.ai. -- SH]
------------------------------------------------
Date: Sat, 18 Apr 92 17:29:50 MDT
To: mcdermott-drew@CS.YALE.EDU
Cc: harnad%Princeton.EDU.hayes@cs.stanford.edu
Drew, clearly you have an antisemantic axe to grind, but its not very
sharp.
First of all, of course you are right that many computational processes
don't have a constant coherent interpretation. But not 99% of them.
Let's look at your examples. First the chess program's list of moves.
That this list denotes any list of chess moves - that is, moves of
actual chess - is already enough of an interpretation to be firmly in
the world of intentionality. You might ask, what IS a move of actual
chess, and I wouldn't want to have to wait for a philosopher's answer,
but the point here is that it certainly isn't something inside a
computer: some kind of story has to be told in which that list denotes
(or somehow corresponds to, or has as its meaning) something other than
bit-strings. And this kind of story is an essential part of an account
of, for example, the correctness of the chess-playing code. Your point
that the heuristics which choose a particular set of moves (or which
assign particular values of some evaluation function to a move) are in
some sense ill-defined is correct, but that is not to say they are
uninterpretable. A bitstring has many interpretations which are not
numbers and have nothing at all to do with chess, so to claim that
these are the meanings is to say something significant.
Suppose I were to build a machine which treated its bit-strings like
base-1 integers, so that N was represented by a consecutive string of
ones N long. Now your interpretation of addition will fail. So it isn't
completely trivial.
Consider again the air traffic control system which gets confused by
thermal noise, then clouds, then geese. This is a familiar situation,
in which a knower has confused knowledge. But the model theory accounts
for this perfectly well. Its beliefs were false, poor thing, but they
had content: it thought there was an airplane there. To give a proper
account of this requires the use of modality and a suitable semantics
for it, as I know you know. One has to say something like its blip
denoted an airplane in the possible worlds consistent with its
beliefs. But look, all this is semantics, outside the formal syntactic
patterns of its computational memory. And just CALLING it an
"air-traffic control system" implies that its computational states have
some external content.
Your inconsistent-beliefs point misses an important issue. If that
expert system has some way of ensuring that these contradictory rules
never meet, then it has a consistent interpretation, trivially: we can
regard the mechanism which keeps them apart as being an encoding of a
syntactic difference in its rule-base which restores consistency. Maybe
one set of rules is essentially written with predicates with an "allo-"
prefix and the others with a "homeo-". You might protest that this is
cheating, but I would claim not: in fact, we need a catalog of such
techniques for mending consistency in sets of beliefs, since people
seem to have them and use them to 'repair' their beliefs constantly,
and making distinctions like this is one of them (as in, "Oh, I see,
must be a different kind of doctor"). If on the other hand the system
has no internal representation of the distinction, even implici t, but
just happens to never bring the contradiction together, then it is in
deep trouble as it will soon just happen to get its knowledge base into
total confusion. But in any case, it is still possible to interpret an
inconsistent set of beliefs as meaningful, since subsets of it are. We
might say of this program, as we sometimes do of humans, that it was
confused, or it seemed to keep changing its mind about treatment
procedures: but this is still ABOUT medicine. A very naive application
of Tarskian models to this situation would not capture the necessary
subtlety of meaning, but that doesn't make it impossible.
Finally, there is no need to retreat to this idea of the interpretation
being a matter of human popularity. The reason the states of an
autopilot denote positions of the airplane is not because people find
it useful to interpret them that way, but because (with very high
probability) the airplane goes where it was told to.
Pat Hayes
-----------------
Date: Mon, 20 Apr 92 09:58:09 EDT
From: "Stevan Harnad"
bd> Date: Sun, 19 Apr 92 20:06:37 PDT
bd> From: dambrosi@research.CS.ORST.EDU (Bruce Dambrosio)
bd>
bd> Stevan:
bd>
bd> I am puzzled by one thing, which perhaps was discussed earlier: why do
bd> you, of all people, believe that a definition satisfying your
bd> requirements might exist? This seems to me quite a quixotic quest.
bd>
bd> A definition is a symbolically specified mapping from the objects
bd> denoted by some set of symbols to the objects denoted by the symbol
bd> being defined. But if, as you claim, the process by which relationship
bd> is established (grounding) is such that it cannot be adequately
bd> described symbolically (I take this to be the heart of the symbol
bd> grounding position), then how can one ever hope to describe the
bd> relationship between two groundings symbolically? At best, one can only
bd> hope for a rough approximation that serves to guide the hearer in the
bd> right direction. I may know what a computer is, but be quite unable to
bd> give you a definition that stands up to close scrutiny. Indeed, such a
bd> situation would seem to be evidence in favor of symbol grounding as a
bd> significant issue. Am I naive or has this already been discussed?
bd>
bd> Bruce D'Ambrosio
Bruce,
This has not been explicitly discussed, but unfortunately your
description of the symbol grounding problem is not quite correct. The
problem is not that we cannot give adequate definitions symbolically
(e.g., linguistically); of course we can: We can define adequately
anything, concrete or abstract, that we understand adequately enough to
define.
The symbol grounding problem is only a problem for those (like mind
modelers) who are trying to design systems in which the meanings of the
symbols are INTRINSIC to the system, rather than having to be mediated
by our (grounded) interepretations. There is nothing whatsoever wrong
with ungrounded symbol systems if we want to use them for other
purposes, purposes in which our interpretations are free to mediate. A
dictionary definition is such a mediated use, and it does not suffer
from the symbol grounding problem. The example of the Chinese-Chinese
Dictionary-Go-Round that I described in Harnad (1990) was one in which
the dictionary was being used by someone who knew no Chinese! For him
the ungroundedness of the dictionary (and the fact that its use cannot be
mediated by his own [nonexistent] grounded understanding of Chinese) is
indeed a problem, but not for a Chinese speaker.
If our notions of "computer" and "computation" are coherent ones (and I
suspect they are, even if still somewhat inchoate) then there should be
no more problem with defining what a computer is than in defining what
any other kind of object, natural or artificial, is. The alternatives
(that everything is a computer, or everything is a computer to some
degree, or nothing is a computer), if they are the correct ones, would
mean that a lot of the statements we make in which the word "computer"
figures (as in "computers can/cannot do this/that") would be empty,
trivial, or incoherent.
One pass at defining computation and computer would be as,
respectively, syntactic symbol manipulation and universal syntactic
symbol manipulator, where a symbol system is a set of objects (symbols)
that is manipulated according to (syntactic) rules operating only on
their shapes (not their meanings), the symbols and symbol manipulations
are systematically interpretable as meaning something (and the
interpretation is cryptologically nontrivial), but the shapes of the
elementary symbol tokens are arbitrary in relation to what they can be
interpreted as meaning. I, for one, could not even formulate the symbol
grounding problem if there were no way to say what a symbol system was,
or if everything was a symbol system.
As to the question of approximate grounding: I discuss this at length
in Harnad (1987). Sensory groundings are always provisional and
approximate (because they are relative to the sample of confusable
alternatives encountered to date). Definitions may be provisional and
empirical ones, or they may be stipulative and analytical. If the
latter, they are not approximate, but exact "by definition." I would
argue, however, that even high-level exact definitions depend for our
understanding on the grounding of their symbols in lower-level symbols,
which are in turn grounded ultimately in sensory symbols (which are
indeed provisional and approximate). This just suggests that symbol
grounding should not be confused with ontology.
There are prominent philosophical objections to this kind of radical
bottom-uppism, objections of which I am quite aware and have taken some
passes at answering (Harnad 1992). The short answer is that
bottom-uppism cannot be assessed by introspective analysis alone and has
never yet been tried empirically; in particular, no one knows HOW we
actually manage to sort, label and describe objects, events and states
of affairs as we do, but we can clearly do it; hence, until further
notice, input information (whether during our lifetimes or during the
evolutionary past that shaped us) is the only candidate source for this
remarkable capacity.
Stevan Harnad
Harnad, S. (1987) The induction and representation of categories. In:
In: S. Harnad (ed.) Categorical Perception: The Groundwork of
Cognition. New York: Cambridge University Press.
Harnad, S. (1990) The Symbol Grounding Problem.
Physica D 42: 335-346.
Harnad, S. (1992) Connecting Object to Symbol in Modeling
Cognition. In: A. Clarke and R. Lutz (Eds) Connectionism in Context
Springer Verlag.
------------------------------------------------------------
Date: Tue, 21 Apr 92 18:22:32 EDT
From: "Stevan Harnad"
ARE "GROUNDED SYMBOL SYSTEMS" STILL SYMBOL SYSTEMS?
Of course not; in fact one wonders why this even needs to be said!
Equivalence "in the standard sense" is computational equivalence, not
physical-causal equivalence. Whether someone is drowned in water or in
beer is equivalent insofar as drowning is concerned, because the
drowning is real in both cases. But if the drowning is "virtual" (i.e.,
a computer-simulated person is "drowned" in computer-simulated water)
there is no drowning at all going on, no matter how formally equivalent the
symbols may be to real drowning.
I've never understood why so much emphasis is placed by philosophers on
the difference between monadic ("intrinsic") and polyadic
("relational") properties. Surely that's not the real issue in mind
modeling. What we want is that symbols should mean X not just because
we interpret them as meaning X but because they (also) mean X
independently of our interpretations. Their meaning has to be
autonomously GROUNDED in something other than just their being able to
bear the systematic weight of our interpretations.
The string of symbols "the cat is on the mat," whether it is
instantiated on the inert pages of a book or as a dynamic state in a
computer running a LISP program, is systematically interpretable as
meaning "the cat is on the mat" (in relation to the rest of the symbol
system) but it does not mean "the cat is on the mat" on its own,
autonomously, the way I do when I think and mean "the cat is on the
mat," because I, unlike the book or the computer, don't mean "the cat
is on the mat" merely in virtue of the fact that someone else can
systematically interpret me as meaning that.
So the real problem is how to ground meaning autonomously, so as not to
leave it hanging from a skyhook of mere interpretation or
interpretability. The solution may still turn out to be "relational,"
but so what? According to my own robotic grounding proposal, for
example, a robot's symbols would have autonomous meaning (or, to be
noncommittal, let's just say they would have autonomous "grounding")
because their use would be governed and constrained by whatever it
takes to make the robot capable of interacting TTT-indistinguishably
with the very objects to which its symbols were interpretable as
referring. The meaning of the robot's symbols is grounded in its
robotic capacity instead of depending only on how the symbols can be or
actually are interpreted by us. But note that this is merely a case of
one set of "relations" (symbol/symbol relations and their
interpretations) being causally constrained to be coherent with another
set of "relations" (symbol/object relations in the world).
The source, I think, of the undue preoccupation with monadic properties
is the (correct) intuition that our thoughts are meaningful in and of
themselves, not because of how their interrelations are or can be
interpreted by others. Probably the fact that all thoughts are the
thoughts of a conscious subject (and that their meaning is a meaning to
that conscious subject) also contributed to the emphasis on the
autonomy and "intrinsic" nature of meaning.
To agree that the meanings of the symbols inside a robot are grounded
in (say) the robot's actual relations to the objects to which its
symbols can be interpreted as referring is still not to agree that the
locus of those meanings is any wider -- in either time or space -- than
the robot's body (which includes the projections and effects of real
world objects on its sensorimotor surfaces).
One would have to see this forthcoming paper, but my intuition is that
a lot of red herrings have been and continue to be raised whenever one
attempts to align (1) the internal/external distinction for a physical
system with (2) what is going on "inside" or "outside" a mind. The first.
I think, is largely unproblematic: We can safely (though not always
usefully) distinguish the inside and the outside of a computer or a
robot, as well as the I/O vs. the processing of a symbol system. What is
inside and outside a mind is another story, one that I think is
incommensurable with anything but the grossest details of the physical
inside/outside story.
As a first pass at "formal," how about: A symbol system consists of a
set of objects (elementary symbols and composite symbols) plus rules
for manipulating the symbols. The rules operate only on the physical
shapes of the symbols, not their meanings (and the shapes of the
elementary symbols are arbitrary), yet the symbols are systematically
interpretable as meaning something. The rules for manipulating the
symbols on the basis of their shapes are called "syntactic" or "formal"
rules.
A computer is a dynamical system that mechanically implements the
symbols, the symbol manipulations, and the rules (constraints) governing
the symbol manipulations.
Some symbols can be indirectly grounded this way, using propositions
with symbols that either have direct sensory grounding or are near to
their sensory grounding (e.g., "A `zebra' is a horse with stripes"), but
many symbols cannot be adequately grounded by symbolic description
alone and require direct sensory acquaintance. This is just more
evidence for the importance of sensorimotor grounding.
The standard robot reply to Searle is ineffectual, because it retains
the (symbols-only) Turing Test (TT) as the crucial test for having a
mind and simply adds on arbitrary peripheral modules to perform robotic
functions. My own "robot" reply (which I actually call the "Total"
reply) rejects the TT altogether for the "Total Turing Test" (TTT) and
is immune to Searle's argument because the TTT cannot be passed by
symbol manipulation alone, and Searle (on pain of the "System Reply,"
which normally fails miserably, but not in the case of the TTT) can
fully implement only pure implementation-independent symbol
manipulation, not implementation-dependent nonsymbolic processes such
as transduction, which are essential for passing the TTT.
On the other hand, I agree that grounded "symbol systems" may turn out
to be so radically different from pure symbol systems as to make it
a different ballgame altogether (the following passage is from
the Section entitled "Analog Constraints on Symbols" in Harnad 1992):
"Recall that the shapes of the symbols in a pure symbol system are
arbitrary in relation to what they stand for. The syntactic rules,
operating on these arbitrary shapes, are the only constraint on the
manipulation of the symbols. In the kind of hybrid system under
consideration here, however, there is an additional source of
constraint on the symbols and their allowable combinations, and
that is the nonarbitrary shape of the categorical representations
that are "connected" to the elementary symbols: the sensory
invariants that can pick out the object to which the symbol refers
on the basis of its sensory projection. The constraint is
bidirectional. The analog space of resemblances between objects is
warped in the service of categorization -- similarities are
enhanced and diminished in order to produce compact, reliable,
separable categories. Objects are no longer free to look quite the
same after they have been successfully sorted and labeled in a
particular way. But symbols are not free to be combined purely on
the basis of syntactic rules either. A symbol string must square
not only with its syntax, but also with its meaning, i.e., what it,
or the elements of which it is composed, are referring to. And what
they are referring to is fixed by what they are grounded in, i.e.,
by the nonarbitrary shapes of the iconic projections of objects,
and especially the invariants picked out by the neural net that has
accomplished the categorization."
I think I agree: The actual role of formal symbol manipulation in
certain dedicated symbol systems (e.g., TTT-scale robots) may turn out
to be so circumscribed and/or constrained that the story of the
constraints (the grounding) will turn out to be more informative than
the symbolic story.
It is for this reason that I have come to believe that categorical
perception and the mechanisms underlying our categorizaing capacity
are the groundwork of cognition.
Stevan Harnad
Harnad, S. (1992) Connecting Object to Symbol in Modeling
Cognition. In: A. Clarke and R. Lutz (Eds) Connectionism in Context
Springer Verlag.
--------------------------------------------------------------
Date: Tue, 21 Apr 92 20:34:02 EDT
From: "Stevan Harnad"
From: Pat Hayes
Pat, "intuition pump" is not a pejorative, if it pumps true. I will be
happy to consider the implications of the fact that Searle, doing
everything the computer does, does not count as a valid implementation
of the same computer program -- as soon as you specify and argue for
what you mean by implementation and why Searle's would not qualify.
Until then, I don't see why EVERY system that processes the same
symbols, follows the same (syntactic) rules and steps through the same
states doesn't qualify as a valid implementation of the same program.
I agree that the layer between the "shadow" that objects cast on our
transducers and the symbols they are cashed into at the very next layer
could in principle be VERY thin -- if indeed the rest of the story were
true, which is that the signals are just hurtling headlong toward a
symbolic representation. However, I don't believe the rest of the story
is true! I think most of the brain is preserving sensory signals in various
degrees of analog form (so we would probably do well to learn from
this). In fact, I think it's as likely that a mind is mostly symbolic,
with just a thin analog layer mediating input and output to the world,
as that a plane or a furnace are mostly symbolic, with a thin analog
layer mediating input and output.
But even if the transducer layer WERE that thin, my point would stand
(and that thin layer would then simply turn out to be critically
important for the implementation of mental states). Although I don't
get much insight from the concept of "supervenience," it would be the
analog-plus-symbolic system on which mental states would "supervene,"
not the symbolic part alone, even if the analog layer was only one
micron thick.
I AM a functionalist about analog systems though. There's more than one
way to skin an analog cat: As long as devices are analog, and support
the same I/O, they don't have to be physically identical: The omatidia
of the horseshoe crab transduce light just as literally as mammalian
retinae (or synthetic optical transducers) do; as long as they really
transduce light and generate the same I/O, they're functionally
equivalent enough for me, as transducers. Same is true for internal A/A
transforms, with retinal signals that code light in the intensity
domain going into some other continuous variable (or even A/D into the
frequency domain) as long as they are functionally equivalent and
invertible at the I/O end.
I don't think you need the TTT to "pin down" meaning. I think you need the
structures and processes that make it possible to pass the TTT in order
to implement meaning at all. And that definitely includes transduction.
We don't disagree, by the way, on the power of computation to capture
and help us understand, explain, predict and build just about anything
(be it planes or brains). I just don't think computation alone can
either fly or think.
As I wrote in my comment on Brian Smith's contribution, this conflating
(i) internal/external with respect to a robot's BODY (which is no
problem, and may involve lots of "internal" transducers -- for
temperature, voltage, etc. -- that are perfectly analog rather than
symbolic, despite their internal locus) with (ii) internal/external
with respect to the robot's MIND:
(1) What is "in" the mind is certainly inside the body (though "wide
intentionalists" tend to forget this); but
(2) what is in the mind is not necessarily symbolic;
(3) what is inside the body in not necessarily symbolic;
(4) what is inside the body is not necessarily in the mind.
The question is not how to "account for" or "handle" proprioception or
pain, but how to EMBODY them, how to implement them. And I'm suggesting
that you can't implement them AT ALL with computation alone -- not that
you can't implement them completely or unambiguously that way, but that
you can't implement them AT ALL. (Or, as an intuition pump, you can
implement pain or proprioception by computation alone to the same
degree that you can implement flying or heating by computation alone.)
Stevan Harnad
------------
Date: Tue, 21 Apr 92 20:48:01 EDT
From: "Stevan Harnad"
Below is a contribution to the symbol grounding discussion from Mike
Dyer. I will not reply here, because the disagareement between Mike and
me has already appeared in print (Dyer 1990, Harnad 1990 in the same
issue of JETAI; my apologies for not having the page span for Mike's
article at hand).
I will just point out here that Mike seems prepared to believe in some
rather radical neurological consequences following from the mere
memorization of meaningless symbols. To me this is tantamount to
sci-fi. Apart from this, I find that the variants Mike proposes on
Searle's Argument seem to miss the point and change the subject.
Stevan Harnad
Harnad, S. (1990) Lost in the hermeneutic hall of mirrors. Invited
Commentary on: Michael Dyer: Minds, Machines, Searle and Harnad.
Journal of Experimental and Theoretical Artificial Intelligence
2: 321 - 327.
-------------------
Date: Tue, 14 Apr 92 23:19:41 PDT
From: Dr Michael G Dyer
Stevan,
It appears that your unfortunate blind acceptance of Searle's
Chinese Room Agument (CRA) keeps leading you astray. In your
analysis of Chalmers's observations you at least
correctly grasp that
"So if a mind supervenes on (the right) computations because
of their computational properties (rather than because of the
physical details of any particular implementation of them),
then it must supervene on ALL implementations of those
computations."
But then you get derailed with:
"I think Searle's Chinese Room Argument has successfully
pointed out that this will not be so..."
But Searle's CRA has NOT been "successful". CRA is quite FLAWED, but
you don't seem to entertain any notions concerning how two brains/minds
might be intertwined in the same body.
Scenario #1: Suppose the Chinese instructions that Searle follows
actually cause Searle to simulate the activation of a complex network
of artificial neurons, equal in complexity to the neural network of a
human brain (just what's in those instruction books is never specified,
so I can imagine anything I want). We then take those instructions and
build a specialized computer "neuro-circuit" that realizes those
instructions -- call it C. We then enlarge Searle's head and install C
so that, when "turned on" it takes over Searle's body. With a remote
control device we turn C on and suddenly Searle starts acting like some
particular Chinese individual. Once turned on, the Chinese persona
requests to maintain control of the body that once was Searle's.
Scenario #2: We build, on a general purpose multiprocessor, a
simulation of the neuro-circuitry of C -- let's call this Simu-C --
such that SC takes over the body when we flip a switch.
Scenario #3: This one is a bit trickier. We examine carefully Searle's
own neuro-circuitry and we design an artificial neural network -- call
it C-supervene -- that gates the circuits of Searle's brain such that,
when C-supervene is turned on, the gating of Searle's circuitry causes
Searle's own brain circuitry to turn into the Chinese person circuitry.
Thus, Searle's own neurocircuitry is being used (in a rather direct way
-- i.e. no symbols) to help create the Chinese personage.
Scenario #4; But now we replace C-supervene with a general multi-
processor that runs a simulation (ie. symbol manipulations) that gates
Searle's own neuro-circuitry to produce the Chinese persona.
In scenario #1 there are two distinct sets of neuro-circuits: Searle's
and the Chinese person's. Whichever one controls the body depends on
our switch.
In scenario #2 the Chinese neurocircuitry is replaced by a simulation
of that circuitry with a more general purpose hardware.
In scenario #3 the Chinese neurocircuitry actually makes use of
Searle's neurocircuitry to do its computations, but it is the "master"
and Searl'es circuitry is the "slave".
In scenario #4, again, the specialized Chinese "neuro-controller" of
scenario #3 is replaced by a simulation on a more general purpose
hardware.
Finally, we can give the Chinese person (in whichever incarnation above
that you want) a set of instructions that allows it to simulate
Searle's entire neuro-circuitry, so that, when we flip our switch, it's
the Searle persona who gains control of the body. So we can multiple
levels of Searles and the Chinese persons simulating each other.
Now, WHICH person should we listen to? When in control of the
body, the Searle persona says he's the real person and he has no
experience of being the Chinese person. When the Chinese person is
in control, this person claims to have no experience of being Searle
(and so uses Searle's own argument against Searle).
Now, it is still possible that there is more to having a mind than
having the right sort of computations, but Searle has NOT given any
kind of refutation with his CRA. And your own "grounding" argument is
insufficient also since it leads to either one of two absurd
situations:
either you have to claim that
(a) remove the eyes and the mind goes.
or (b) the entire brain has to count as the eyes, so that you get
to remove the entire brain whenever anyone requests that you
remove the eyes.
On the other side, if we accept Chalmers's position (and the working
hypothesis of the Physical Symbol System Hypothesis of AI), then we
have the advantage of being able to compare minds by observing how
similar their computations are (at some level of abstraction) and we
can develop, ultimately, a non-chauvinistic theory of mind (e.g. alien
intelligences in different substrata).
Notice, connectionism fits within the PSSH (because every neuron and
synapse and dendritic compartment, etc. can be represented by a symbol
and its behavior modeled by symbol manipulation).
Chalmers (and AI researchers) may be wrong, but Searle (and you) have
not given any kind of ironclad arguments that they are.
So let's hear something from you OTHER than your overly and oft used
argument of the form:
"X..., but we know Searle's CRA is right, so X can't be..."
(There are large numbers of AI researchers out here who are not
convinced even one whit by Searle's CRA or your "out of sight out of
mind" argument. So you and Searle need to come up with something new.
Searle's INABILITY to "feel" what it's like to "be" the Chinese person
he's bringing into existence IS TO BE EXPECTED. Nor do we expect the
the Lisp interpreter to know what it's like to be the particular expert
system that "supervenes" upon it.)
-- Michael Dyer
---------------------
Date: Wed, 22 Apr 92 13:06:13 EDT
From: "Stevan Harnad"
Date: Wed, 22 Apr 92 12:00:06 +0200
From: tim@arti1.vub.ac.be (Tim Smithers)
I have two responses to Mike Dyer's recent contribution to your symbol
grounding discussion.
First, the actual practice of (symbol processing) AI research makes it
very difficult to talk about the Physical Symbol System Hypothesis
(PSSH) of Newell and Simon as being "a working hypothesis". It is much
more a widely accepted and unquestioned dogma than it is a hypothesis.
For it to be a hypothesis, in the normal scientific sense (symbol
processing) AI research would need to be conducting a broad
experimental investigation of its validity (or otherwise). Very little,
if any, research is either presented as, or can be properly understood
to be, a contribution to such a research programme. Talk of
constructing theories of mind (even non-chauvinistic ones) given the
current practice thus seems to me to be unsupportable.
Second, even if there are lots of AI researchers out there who are not
convinced by Searle's and your arguments, does Mike Dyer think that
they are going to be convinced by such in principle possibly true but
in practice right out of the window aguments as he offers? I think all
you guys should spend some time trying to build real robots (not
simulations!) that reliably do real things (even very simple things) in
the real world. This might introduce some reality constraints on this
whole discussion from which I think it would benefit enormously--it
might `ground' the discussion, if you don't mind my misusing your
term.
Tim Smithers
---------------
From: Pat Hayes
Brian-
As usual, you say things clearly. But I can't resist a few comments. I
follow your numbering.
1. I agree wholeheartedly with your observations about the limitations
of universality. But not with your conclusions. Searle talks about the
distinction between a model and the real thing, but the moral of the
classical work on universality (and of CS practice - not just in
Silicon Valley, by the way!) is exactly that a computational simulation
of a computation IS a computation. Thus, a LISP interpreter running
LISP really is running LISP: it's no less really computation than if one
had hardware devoted to the task.
That is I think a crucial insight, perhaps the central one of CS, and
one which was historically very surprising. That's why computers work,
why we can run LISP and Word-4 on the same machine. To put that aside
as a red herring is to simply regard computers as fast switching
devices. It carries possible implications for biology, for example it
suggests an account of why evolution produced so much cognition so
quickly. While this idea and its implications is a minefield, I think
it's one we need to be treading through, and definitely not any kind of
colored fish.
2. [on original intentionality] Oh, I agree! This deserves expanding.
Many of the Searlean writers have taken it as somehow axiomatic that
human thinking just has this vital property of being meaningful,
something that only human, or maybe organic, thinking has been observed
to possess. Whatever this is, it isn't anything like a color or a mass
that human thought has.
I have a number of beliefs about ancient Rome. How are these thoughts
connected to the Rome of 2000 years ago? The answer is probably very
complicated, involving texts written by others and translated from
language to another, to historians best attempts to reconstruct facts
from flimsy evidence, and so forth. The connection between me and
Caesar goes through an entire society, indeed a historical chain of
societies. My thoughts about Julius Caesar are not somehow
intrinsically about him by virtue of their being in my head; but they
are in fact about him. But I can't see any reason why a machine could
not have almost the same (very complicated) relationship to him that I
have, whatever it is, since it is mediated almost entirely by
language.
5. [on types] I agree that there is a danger of imposing too much
structure on the world, but have a couple of caveats. First, what makes
you think that this will ever be completely avoidable? We must use some
concepts to build our theories from, and to try to avoid it altogether
is not just tough sledding but I think trying to walk into the snow
naked (and you know what happened to him.) We have to be conscious of
what we are doing, but we must use something, surely. And second, I
don't think you are right to dismiss set theory so quickly. Set theory
doesn't preparse the world: it only insists that some parsing is made.
Others have tried to get by with less, and indeed several other
alternatives are available, as well as several alternative foundations.
But again, you have to stand somewhere, and these carefully developed,
thoroughly tested and well-understood patches of intellectual ground
have a lot to recommend them. And I don't think one does have to have
the types all specified in advance.
Take maps for example. One can give an account of how maps relate to
terrain which assumes that maps have some kind of parsing into
meaningful symbols (towns, roads, etc) which denote...well, THINGS in
the territory, and talk about a certain class of (spatial) relations
between these things which is reflected by a (more-or-less) homomorphic
image of them holding between the syntactic objects in the map. Now,
there are all sorts of complexities, but the essential idea seems
coherent and correct. But notice it has to assume that the terrain can
be somehow divided into pieces which are denoted by the map's symbols
(or better, that appropriate structures can be found in the terrain).
You might object at this point that this is exactly what you are
complaining about, but if so I would claim not. Here, the theorist is
only assuming that SOME parsing of the terrain can be made: it was the
maker of the map who parsed his territories, and the semantic account
has to reflect this ontological perspective. So what the theorist needs
is some way of describing these ontological complexities which imposes
a minimum of structure of its own, and structure which is
well-understood so that we can consciously allow for possible
distortions it might introduce. And that is just what is provided by
the idea of a set. To be sure, there are some artifacts produced by,
for example, the conventional extensional representation of functions.
But these are immediately recognisable when they occur and have known
ways around them.
I once tried to focus on the hardest case I could find for a
set-theoretic account, which was the idea of a piece of liquid (since
what set theory does seem to assume is the notion of an individual
thing conceptually distinct from others, and liquids are very
intermergable). And to my surprise, the little intellectual discipline
imposed by the use of sets actually clarified the semantic task: it was
as though the sets imposed distinctions which were a useful ontological
discovery. I have since come to think not that a particular set of types
is fixed in advance, but that what does seem to be fixed in us, in our
way of thinking, is a propensity to individuate. The world is a
continuum, but we see it and think of it as made of things, maybe
overlapping in complex ways, but conceptually separate entities that we
can name and classify.
This may not be the place to indulge in this discussion, since it is
getting away from what computation is, but you asked for things onto
the table...
Pat Hayes
-----------
From: Pat Hayes
Heres an example adapted from one of Brian's. Take a set of rules which
encode (a formal system for) arithmetic, together with a formal
predicate 'lengthof', and the rules
lengthof('0') -> 1
lengthof(n<>x) -> lengthof(n) + lengthof(x)
Now, these rules make 'lengthof(n)' evaluate to (a numeral which means)
the number of digits in the formal representation of n: ie, the length
of that numeral in digits. Notice this is the ACTUAL length of that
piece of syntax. Now, is this 'formal'? It is according to your
definition, and perhaps you are happy with that, but it has some marks
which successfully refer to physical properties of part of the world.
It IS a pejorative if the pump is claimed to be a conclusive argument
from obvious assumptions. My intuition tells me clearly that when I
debug a piece of code by pretending to be an interpreter and running
through it 'doing' what it 'tells' me to do, that the program is not
being run, and certainly not run on, or by, me. So we are left with
your intuition vs. my intuition, and they apparently disagree.
The key is that Searle-in-the-room is not doing everything the computer
'does', and is not going through the same series of states. For
example, suppose the program code at some point calls for the addition
of two integers. Somewhere in a computer running this program, a piece
of machinery is put into a state where a register is CAUSED to contain
a numeral representing the sum of two others. This doesn't happen in my
head when I work out, say, 3340 plus 2786, unless I am in some kind of
strange arithmetical coma. If Searle-in-the-room really was going
through the states of an implementation of a chinese-speaking
personality, then my intuition, pumped as hard as you like, says that
that Chinese understanding is taking place. And I haven't yet heard an
argument that shows me wrong.
While we should have awe for what Nature has wrought, we also must keep
our wits about us. The reason elephants have big brains is that they
have a lot of skin sending in signals which need processing, and the
neurons come in at a certain density per square inch. This is
evolution's solution to the bandwidth problem: duplication. Similarly,
that the motor and sensory cortex use 'analogical' mappings of bodily
location is probably more due to the fact that this fits very nicely
with the way the information is piped into the processor, where
location is encoded by neuroanatomy, than by any profound issue about
symbolic vs. analog. It has some nice features, indeed, such as
localisation of the effects of damage: but we are now in the language
of computer engineering.
You are exhibiting here what I might call Searleanism. Of course a
furnace is not symbolic. But hold on: thats the point, right? Furnaces
just operate in the physical world, but minds (and computers) do in
fact react to symbols: they do what you tell them, or argue with you,
or whatever: but they respond to syntax and meaning, unlike furnaces
and aircraft. That's what needs explaining. If you are going to lump
furnaces and minds together, you are somehow missing the point that
drives this entire enterprise. (Aircraft are actually a borderline
case, since they do react to the meanings of symbols input to them,
exactly where they have computers as part of them.)
I agree, because with what you mean by computation, I couldn't even run
Wordstar with computation ALONE. I need a computer.
I bet computational ideas will be centrally involved in a successful
understanding of pain and proprioception, probably completely
irrelevant to understanding lime chemistry, but important in reasonably
exotic flying.
But now we are just beating our chests at one another. Like I said, its
only a pump, not an argument.
Pat Hayes
----------
Date: Wed, 22 Apr 92 15:12:37 EDT
From: "Stevan Harnad"
ON SYNTHETIC MINDS AND GROUNDED "ABOUTNESS"
Pat Hayes (phayes@nmsu.edu) wrote:
Not sure who "Searlean" writers are, but this writer certainly does
not claim that only organic thinking is possible (e.g., I am working
toward TTT-scale grounded robots). No one has given any reason to
believe that synthetic minds can't be built. Only one candidate class
of synthetic minds has been ruled out by Searle's argument, and that is
purely computational ones: stand-alone computers that are merely
running the right software, i.e., any and all implementations of symbol
systems that allegedly think purely because they are implementations of
the right symbol system: the symbol system on which the mind
"supervenes" (with the implementational particulars being inessential,
hence irrelevant).
But there are plenty of other candidates: Nonsymbolic systems (like
transducers and other analog devices), hybrid nonsymbolic/symbolic
systems (like grounded robots), and even implemented symbol systems in
which it is claimed that specific particulars of the implementation ARE
essential to their having a mind (Searle's argument can say nothing
against those, because he couldn't BE the system unless its
implementational details were irrelevant!).
I see no reason why a grounded (TTT-indistinguishable) robot's thoughts
would not be just as grounded in the objects they are systematically
interpretable as being about as my own thoughts are. I diverge from
Searle and many other post-Brentano/Fregean philosophers in denying
completely that there are two independent mind/body problems, one being
the problem of consciousness (qualia) and the other being the problem
of "aboutness" (intentionality). In a nutshell, there would be no
problem of thoughts having or not having real "aboutness" if there were
not something it was like to think (qualia). The reason the symbols in
a computer are not "about" anything is because there's nobody home
in there, consciously thinking thoughts!
[This is what is behind the force of Searle's simple reminder that he
would surely be able to state, with complete truthfulness, that he had
no idea what he was talking about when he "spoke" Chinese purely in
virtue of memorizing and executing the very same syntactic
symbol-manipulation operations that are performed by the TT-passing
computer. We each know exactly what it is LIKE to understand English,
what it is LIKE to mean what we mean when we speak English, what it is
LIKE for our words to be about what they are about; no such thing would
be true for Searle, in Chinese, under those conditions. Hence the fact
that the Chinese input and output was nevertheless systematically (TT)
interpretable AS IF it were about something would merely show that that
"aboutness" was not "intrinsic," but derivative, in exactly the same
sense that it would be derivative in the case of the symbols in an
inert book, in which there is likewise nobody home.]
On the other hand, there is still the POSSIBILITY that grounded
TTT-scale (performance indistinguishable) robots or even grounded
TTTT-scale (neurally indistinguishable) robots fail to have anybody
home in them either. Now that IS the (one, true) mind/body problem, but we
should be ready to plead no contest on that one (because the TTT and
the TTTT take us to the limits of empiricism in explaining the mind).
Hence we should investigate and model the structures and processes
underlying our capacity to categorize inputs (beginning with sensory
projections). Those structures and processes will turn out to be
largely nonsymbolic, but perhaps symbols can be grounded in the
capacity those nonsymbolic structures and processes give us to pick out
the objects they are about.
Stevan Harnad
--------------
Harnad, S., Hanson, S.J. & Lubin, J. (1991) Categorical Perception and
the Evolution of Supervised Learning in Neural Nets. In: Working
Papers of the AAAI Spring Symposium on Machine Learning of Natural
Language and Ontology (DW Powers & L Reeker, Eds.) pp. 65-74. Presented
at Symposium on Symbol Grounding: Problems and Practice, Stanford
University, March 1991; also reprinted as Document D91-09, Deutsches
Forschungszentrum fur Kuenstliche Intelligenz GmbH Kaiserslautern FRG.
Andrews, J., Livingston, K., Harnad, S. & Fischer, U. (1992) Learned
Categorical Perception in Human Subjects: Implications for Symbol
Grounding. Proceedings of Annual Meeting of Cognitive Science Society
(submitted)
Harnad, S. Hanson, S.J. & Lubin, J. (1992) Learned Categorical
Perception in Neural Nets: Implications for Symbol Grounding.
Proceedings of Annual Meeting of Cognitive Science Society
(submitted)
------------------------------------------------
From: Pat Hayes
Heres an example adapted from one of Brian's. Take a set of rules which
encode (a formal system for) arithmetic, together with a formal
predicate 'lengthof', and the rules
lengthof('0') -> 1
lengthof(n<>x) -> lengthof(n) + lengthof(x)
Now, these rules make 'lengthof(n)' evaluate to (a numeral which means)
the number of digits in the formal representation of n: ie, the length
of that numeral in digits. Notice this is the ACTUAL length of that
piece of syntax. Now, is this 'formal'? It is according to your
definition, and perhaps you are happy with that, but it has some marks
which successfully refer to physical properties of part of the world.
It IS a pejorative if the pump is claimed to be a conclusive argument
from obvious assumptions. My intuition tells me clearly that when I
debug a piece of code by pretending to be an interpreter and running
through it 'doing' what it 'tells' me to do, that the program is not
being run, and certainly not run on, or by, me. So we are left with
your intuition vs. my intuition, and they apparently disagree.
The key is that Searle-in-the-room is not doing everything the computer
'does', and is not going through the same series of states. For
example, suppose the program code at some point calls for the addition
of two integers. Somewhere in a computer running this program, a piece
of machinery is put into a state where a register is CAUSED to contain
a numeral representing the sum of two others. This doesn't happen in my
head when I work out, say, 3340 plus 2786, unless I am in some kind of
strange arithmetical coma. If Searle-in-the-room really was going
through the states of an implementation of a chinese-speaking
personality, then my intuition, pumped as hard as you like, says that
that Chinese understanding is taking place. And I haven't yet heard an
argument that shows me wrong.
While we should have awe for what Nature has wrought, we also must keep
our wits about us. The reason elephants have big brains is that they
have a lot of skin sending in signals which need processing, and the
neurons come in at a certain density per square inch. This is
evolution's solution to the bandwidth problem: duplication. Similarly,
that the motor and sensory cortex use 'analogical' mappings of bodily
location is probably more due to the fact that this fits very nicely
with the way the information is piped into the processor, where
location is encoded by neuroanatomy, than by any profound issue about
symbolic vs. analog. It has some nice features, indeed, such as
localisation of the effects of damage: but we are now in the language
of computer engineering.
You are exhibiting here what I might call Searleanism. Of course a
furnace is not symbolic. But hold on: thats the point, right? Furnaces
just operate in the physical world, but minds (and computers) do in
fact react to symbols: they do what you tell them, or argue with you,
or whatever: but they respond to syntax and meaning, unlike furnaces
and aircraft. That's what needs explaining. If you are going to lump
furnaces and minds together, you are somehow missing the point that
drives this entire enterprise. (Aircraft are actually a borderline
case, since they do react to the meanings of symbols input to them,
exactly where they have computers as part of them.)
I agree, because with what you mean by computation, I couldn't even run
Wordstar with computation ALONE. I need a computer.
I bet computational ideas will be centrally involved in a successful
understanding of pain and proprioception, probably completely
irrelevant to understanding lime chemistry, but important in reasonably
exotic flying.
But now we are just beating our chests at one another. Like I said, its
only a pump, not an argument.
Pat Hayes
---------------
Date: Wed, 22 Apr 92 17:39:44 EDT
From: "Stevan Harnad"
Pat Hayes
It is a very interesting and useful feature of symbol systems that some
can be formulated so as to be INTERPRETABLE as referring to themselves (as
in the sentence "this sentence has five words") or to physical
properties (especially numerical ones) of other symbols and symbol
strings within the same system. Symbol systems that go on to USE the
nonarbitrary analog properties of their symbol tokens as data are special
in certain respects (as in "numeric" versus "symbolic" computation)
and may cast just a bit more light on the dynamics of dedicated hybrid
symbolic/analog systems, and perhaps even on symbol grounding. I don't
know.
But note that in your example above, even though the computation yields
a symbol that is interpretable as the number of symbols in the string,
this is in principle no different from a computation that yields a
symbol that is interpretable as the number of planets in the solar
system. It is just a systematic correspendence (and hence interpretable
as such). But "interpretable as meaning X" (as in the case of a book,
interpretable by a thinking mind) is not the same as "meaning X" (as in
the case of thoughts, in a mind). Failing to distinguish the two seems
to be another instance of conflating physical inner/outer and mental
inner/outer, as discussed earlier.
But isn't the real question whether there is any relevant difference
between what you think is a "real" implementation by a machine and what
you think is a "pseudo-implementation" by a person? Certainly the
computer is not stepping through the states consciously and
deliberately, as you are. But is there anything else that's different?
If we speak only of the "motions gone through" and their I/O conditions
in the two cases, they are exactly the same. In the case of the
machine, the motions are mechanical; no choice is involved. In the case
of the person, their elective. But so what? Even apart from the vexed
questions associated with free will and causality, what is there about
taking IDENTICAL motions under identical I/O conditions and making
their causal basis mindless and mechanical that could possibly effect a
transition INTO the mental (rather than OUT of it, which is the much
more obvious feature of the transition from the human implementation to
the machine one)?
It's always useful, in this sort of hermeneutic puzzle, to de-interpret
and reduce things to gibberish as much as possible: Suppose the
computer was doing all the requisite summation in binary, and you were too,
and all it did, and all you did, was compare zero's and one's and erase
and carry, just like a Turing Machine. Is it still so obvious that
you're not doing everything the computer is doing? If anything, the
computer is doing less than you rather than more (because it has no
choice in the matter). Why should I interpret less as more?
I too am thinking of this only as (reverse bio-)engineering. But brains
can do so much more than any machine we've yet engineered, and they seem
to do so much of it in analog. It seems that this might be a useful cue to
take, but maybe not. It's an empirical question.
I don't think I'm missing the point. Computation has been able to
generate some very fancy and flexible performance -- certainly fancier
and more flexible than that of a furnace or plane (except "smart,"
computer-aided planes, as you indicate). Computation also seems to
resemble thought in its syntactic structure. It was accordingly quite
reasonable to hypothesize that thinking -- that unobservable process
going on in our heads -- might actually be a form of computation. But
here we are discussing reasons why, despite promising initial
appearances, that hypothesis is turning out to be WRONG, and what is
going on in our heads is something else, not computation (or not just
computation).
By the way, minds and computers may both respond to syntax, but only
minds respond to meaning. Computers are merely INTERPRETABLE as if they
responded to meaning...
Pat, you know I stipulated that the computation had to be physically
implemented; I just stressed that the particulars of the implementation
(apart from the fact that they stepped through the right states with
the right I/O) were irrelevant.
And I bet a lot of the essential features of pain and proprioception
will be in the analog properties of the hardware that implements it,
which will be more like exotic chemistry.
Stevan Harnad
---------------
Date: Mon, 20 Apr 92 03:23:41 -0400
From: yee@envy.cs.umass.edu
Subject: Don't talk about "computers"
I would like to follow up on some of Brian Smith's recent comments
regarding universal computations and formal/non-formal symbol
processing. I propose that we try to avoid using the terms "computer"
and "program" because they are misleading with regard to questions of
the computability of mind. For "computer" I would use "Turing machine"
and I generally would not discuss programs because they are just
descriptions of TM's.
The things we usually refer to as "computers" are physical
instantiations of universal Turing machines (UTM's), a particular
subclass of Turing machine (TM). Unfortunately, philosophical
discussions about computers (UTM's) generally carry an implicit
extension to all TM's. Presumably, this occurs because UTM's are
"universal." But as Brian indicated, UTM universality refers to a very
special type of *weak equivalence* (Pylyshyn, 1984) between TM's and
UTM's. Universality merely means partial I/O equivalence. This is
insufficient for many discussions about the computability of
mind---e.g., the Chinese Room---because such discussions consider, not
only I/O behavior, but also *how* the behavior is achieved, and UTM's
are far from "typical" in their manner of computation. In particular,
although UTM's process certain input symbols purely formally, not all
TM's need behave this way.
To review briefly, any program P describes a Turing machine Tp that
maps inputs x to outputs y (as shown below in A). Any UTM U (shown in
B) is special in that its inputs z are composites of a program P and a
nominal-input x', i.e., z=(P,x').
+----+ x'-+ +---+
x -->| Tp |--> y +- z ->| U |--> y
+----+ P -+ +---+
(A) a TM. (B) a UTM.
Formal symbol processing of nominal-inputs by UTM's is a special
consequence of their being given input programs. A UTM U can always
produce output y by processing nominal-input x'=x purely formally
because P completely controls the processing of x', independently of U.
That is, U's computation on z simply instantiates Tp's computation x
--> y.
Clearly, U's formal treatment of x' does not imply that Tp's processing
of x is necessarily formal. Such a conclusion would require a special
proof. For all we know, Tp might associate x with internally stored
information and produce output y accordingly. One might try to show
that all TM's are restricted to formal symbol processing, but this
would not follow automatically from the fact that UTM's can get away
with formally processing (a portion of) their inputs. (Actually, in a
paper cited below I argue that, in general, TM's can process symbols
non-formally.)
+-----{ CR }-------+
| |
x'-------+ +---+ |
| +- z ->| U |----> y
| p -+ +---+ |
+------------------+
(C) a UTM viewed as the CR.
The implications of the TM/UTM distinction for the Chinese Room (CR)
argument are straightforward. The person in the CR is a UTM U that is
given a program P (the rules). (Note that "memorizing" the rules does
not change U into Tp. Any set of rules could be memorized, and the
memorized rules remain an input to U.) To answer the question of *how*
the Chinese symbols x' are being processed inside the room, one must
consider what *Tp* is doing to the symbols. Considering only U's
activity is useless because U is computing z=(P,x')--> y. Thus, without
specific knowledge of the rules P, one simply cannot answer the
question of whether the Chinese input symbols are being understood in
the CR or are only being formally manipulated. Both possibilities
remain open (unless, of course, one advocates the Turing Test for
understanding, but that is an independent argument).
In general, if one wants to know how a program P really operates, then
one should only consider the corresponding Turing machine Tp. If you
build a UTM U, give it P, and then look at what U is doing, you will be
looking in the wrong place. Eliminate the middleman, and build Tp
directly.
Finally, one concludes that an assertion such as
Every computer has property X. (1)
is generally ambiguous and should be replaced by either
Every TM has property X. (2a)
or
Every UTM has property X. (2b)
Clearly, (2a) implies (2b), but not conversely. At best, the Chinese
Room argument shows that UTM computations are not good candidates for
minds. However, there remain plenty of non-universal TM computations,
and---absent any proof to the contrary---some of them might be minds.
To find out, one should forget about computers and think about
instantiated programs. If one's real interest is the entire class of
TM's, then it is dangerous to form intuitions and conclusions revolving
around the special properties of UTM's.
Many debates about computers and minds pit critics of purely formal
symbol processing (which UTM's perform) against proponents of
computation (which all TM's perform). A failure to clearly maintain the
TM/UTM distinction means that, not surprisingly, discussants often
appear to talk past each other. Nevertheless, it remains entirely
consistent to believe (the *correct* :-) portions of both the
"semantophiles'" and the "computationalists'" arguments. That is,
intentionality, symbol-grounding, meaning, etc. (of the type desired by
Searle, Harnad, Penrose and others) is necessary for (human-like)
minds, and such semantics is Turing-computable.
Richard Yee
--------------------------------
@Book{Pylyshyn:84,
author = "Pylyshyn, Z. W.",
title = "Computation and Cognition: Toward a Foundation for Cognitive
Science",
publisher = "Bradford Books/MIT Press",
year = "1984",
address = "Cambridge, MA",
@Unpublished{Yee:rcssp,
author = "Yee, Richard",
title = "Real Computers and Semantic Symbol Processing",
note = "Dept.\ of Computer Science, Univ. of Massachusetts, Amherst,
MA 01003. E-mail: yee@cs.umass.edu",
year = "1991",
month = "March"
------------------------------
Date: Wed, 22 Apr 92 19:14:07 EDT
From: "Stevan Harnad"
SO WHAT IS COMPUTATION?
In his comment entitled "Don't talk about computers," Richard Yee
(yee@envy.cs.umass.edu) wrote:
Much of Yee's comment is based an a distinction between formal and
nonformal "computation," whereas my arguments are based completely on
computation as formal symbol manipulation. We will need many examples
of what nonformal computation is, plus a clear delineation of what is
NOT nonformal computation, if this is to help us with either the
question of what is and is not a computer (or computation) or the
question of whether or not mental processes are computational and
whether or not computers can have minds. (It would also seem hard to
pose these questions without talking about computers, as Yee enjoins
us!)
The Turing Test has been intimately involved in Searle's Argument from
the beginning. The Argument is directed against a position Searle
dubbed "Strong AI," according to which a computer program that could
pass the Turing Test (in Chinese) would understand (Chinese) no matter
how it was implemented. Searle simply points out to us that if he
himself implemented the program (by memorizing the symbols and symbol
manipulation rules) he would not understand Chinese, hence neither
would any computer that implemented the same program. So much for the
Turing Test and the computationality of understanding.
The only thing that is critical for Searle's argument is that he be
able to DO with the input and output exactly the same (RELEVANT) things
the computer does. The implementational details are irrelevant; only
the program is relevant. And the TT is simply an I/O criterion.
Now I have no idea what YOU are imagining the computer to be doing; in
particular, what would it be doing if it were doing "nonformal
computation"? If it would be doing something that was not
implementation-independent, then you've simply changed the subject (and
then even a transducer would be immune to Searle's argument). If it IS
doing something implementation-independent, but not "formal," then
again, what is it, and can Searle do it or not?
This won't do at all, because for all I know, I can think of an airplane
or a planetary system as an "instantiated program" on a "non-universal
TM," and that would make the question of what computers/computation
can/cannot do pretty empty. Please give examples of what are and are
not "non-universal TM computations" and a principled explanation
of why they are or are not.
One cannot make coherent sense of this until the question "What is
computation?", as posed in the header to this discussion, is answered.
Please reply in ordinary language before turning again to technical
formalisms, because this first pass at formalism has merely bypassed
the substantive questions that have been raised.
Stevan Harnad
-----------------------
-----------------------
Date: Thu, 23 Apr 92 17:12:30 EDT
From: "Stevan Harnad"
SEARLE'S PERISCOPE
PSH is certainly an empirical hypothesis if it is construed as a
hypothesis about how "cognitive" engineers might successfully generate
mind-like performance computationally (and people may differ in their
judgments about how successful computation has been in doing that so
far). But PSH is more like an untestable conjecture if is construed as
the claim that the successful generators of that mind-like performance
(if there are any) will have real minds (i.e., somebody will be at home
in there), because normally the only way to know whether or not a
system has a mind is to BE the system. Hence, for the very same reason
that one can suppose that a stone (or any other body other than
one's own) does or does not have a mind, as one pleases, without any hope
of ever being any the wiser, the PSH is shielded from refutation by the
impenetrability of the other-minds barrier.
Now Searle has figured out a clever way (I've dubbed it "Searle's
Periscope") in which he could peek through the other-minds barrier and
BE the other system, thus testing what would normally be an untestable
conjecture. Searle's Periscope works ONLY for the special case of PSH
(implementation-independent symbol manipulation): He has simply pointed
out that if we (1) SUPPOSE (arguendo) that a physical symbol system
alone could pass the Turing Test in Chinese, and from this we wish to
(2) INFER that that physical symbol system would therefore be
understanding Chinese (purely in virtue of implementing the TT-passing
symbol system), THEN it is intuitively obvious that if (3) Searle
himself implemented that same symbol system by memorizing all the
symbols and rules and then performing the same symbol manipulations on
the same inputs, then (4) he would NOT be understanding Chinese;
therefore the inference to (2) (and hence the PSH) is false.
What makes this example unrealistic is much more the supposition (1)
that a symbol system could pass the TT (there's certainly no such
system in empirical sight yet!) rather than (3) that (if so, then)
Searle could himself memorize and perform the same symbol
manipulations. So maybe life is too short and memory too weak for a
person to memorize and perform all those symbols and rules: So memorize
and perform a few of them, and then a few more, and see if that kind of
thing gives you a LITTLE understanding of Chinese! What is intuitively
obvious is that there's nothing in the scenario of doing THAT kind of
mindless thing till doomsday that would even faintly justify believing
that that's the road to understanding.
No, the real sci-fi in this example comes from (1), not (3); and
dwelling instead on the unrealistic features of (3) is motivated only
by the yearning to re-establish the barrier that normally makes it
impossible to block the conjecture that a system other than oneself has
(or does not have, as the case may be) a mind. Mike Dyer tries to
resurrect the barrier by supposing that Searle would simply develop
multiple-personality syndrome if he memorized the symbols and rules
(but why on earth would we want to believe THAT); you, Pat, try to
resurrect the barrier by denying that Searle would really be a valid
implementation of the same symbol system despite passing the same TT,
using the same symbols and rules! And in response to "why not?" you
reply only that his free will to choose whether or not to follow the
rules is what disqualifies him. (Actually, I think it's his capacity
to talk back when we project the PSH conjecture onto him that's the
real problem; because that's something the poor, opaque first-order
physical symbol system, slavishly following the very same rules and
passing the same TT, is not free to do, any more than a stone is.)
Still others try to resurrect the other-minds barrier by invoking the
fact that it is unrealistic to suppose that Searle could have the speed
or the memory capacity to implement the whole symbol system (as if
somewhere in the counterfactual realm of greater memory and greater
speed there would occur a phase transition into the mental!).
To my mind, all these strained attempts to reject (4) at all costs are
simply symptomatic of theory-saving at a mounting counterfactual price.
I, like Tim Smithers, simply prefer taking the cheaper (and, I think,
more realistic and down-to-earth) road of grounded robotics, abandoning
pure computation, PSH, and the expanding ring of epicycles needed to
keep them impenetrable to Searle's Periscope.
Stevan Harnad
------------
Date: Thu, 23 Apr 92 00:34:42 PDT
From: Dr Michael G Dyer
Here are some responses and comments to whoever is willing to read
them:
I agree that there's no physical drowning, but what if we build an
artifical neural network circuitry (with ion flow and/or action
potential timings identical to those of some person's brain, etc.) and
then give it the same inputs that a drowning person would receive?
Who is to say that this artificial neural network won't have the
subjective experience of drowning?
Here is where my intuition pumps diverges quite sharply.
Is the physical act of something flying thru the air a computation? I
think not (unless we imagine the entire universe as being a
simulation God's computer -- then it is, but we'll never know :-).
But does the EXPERIENCE of flying fall into a certain class of
computations? No one really knows, but my bet is "yes". In that
case, the actually physical act of flying is irrelevant. For a mind,
what is important is the experience of flying.
I think that certain classes of computations actually have
subjective inner experiences. At this point in time science simply
has no way of even beginning to formulate a "theory" of what the
subjective-point-of-view might be like for different types of
computations, whether in VLSI, on tapes, optical or biochemical.
Given that we can't tell, the safest strategy is to make judgements
about the inner life of other entities based on their behavior.
ts> First, the actual practice of (symbol processing) AI research
ts> makes it very difficult to talk about the Physical Symbol System
ts> Hypothesis (PSSH) of Newell and Simon as being "a working
ts> hypothesis". It is much more a widely accepted and unquestioned
ts> dogma than it is a hypothesis. For it to be a hypothesis, in the
ts> normal scientific sense (symbol processing) AI research would
ts> need to be conducting a broad experimental investigation of its
ts> validity (or otherwise). Very little, if any, research is either
ts> presented as, or can be properly understood to be, a contribution
ts> to such a research programme.
It is common for paradigm-level hypotheses to go unquestioned by
those who are working within that paradigm (i.e. they accept the
hypothesis and so don't spend time questioning or re-questioning it.)
In this context I think that Harnad and Searle play a very useful role
in forcing some of us (more philosophically oriented) AI researchers
to reexamine this hypothesis.
ts> ...does Mike Dyer think that they are going to be convinced by such
ts> in principle possibly true but in practice right out of the window
ts> aguments as he offers?
Mmmm.... so it's ok to have Searle do all symbol manipulations (that
might require a level of granularity where each symbol represents a
synapse or something lower) all in his head(!), but it's NOT ok for me
to examine how one network (i.e. Searle's neurons) might be
intertwined with another network (i.e. artificial VLSI circuitry
isomorphic to the symbol relations and manipulations that make up a
Chinese persona)??? My students and I happen to design
connectionist-style networks of various sorts, to process language,
make inferences, etc. and the issue of how one network gates and/or
is composed with another etc. we think is rather relevant to
understanding ultimately how minds might reside in brains.
Tough questions, however, are:
"What's it's feel like to BE a particular sort of network?"
"What's it feel like to BE a particular sort of (software)
system? Harnad and Searle seem to assume that, no matter how
complex, any kind of software system has no feelings. How do they
know? Harnad claims we can simply ASK Searle to find out what it's
like to understand English but he won't allow us to simply ASK the
Chinese persona to find out what it's like to understand Chinese.
ts> I think all you guys should spend some time trying to build real
ts> robots (not simulations!) that reliably do real things (even very
ts> simple things) in the real world.
Yes, that's laudable, but we can't all be roboticists. However, I just
saw one of those history of computer science shows and they had a
nice demonstration of "virtual reality". VR is getting pretty good.
You tilt the helmet and quite realistic images get updated, with
proper perspective, etc. What if the robot received visual input from
a VR world rather than the real world? (Oops! There goes those
vision input transducers Stevan Harnad needs so badly! :-)
This is quite a claim! What evidence is there for such a claim? In
contrast, each neuron appears to respond to its inputs (including its
local chemical environment) without requiring any sort of thing
called "meaning". The term "meaning", as far as I can tell, is simply
used to refer to incredibly complex syntactic types of operations. If
a robot (or person) is organized to behave in certain, very complex
ways, then we tend to take (as Dennett says) an "intentional stance"
toward it, but that doesn't mean there is anything other than syntax
going on. (Biologists have also abandoned "life force" notions for the
incredibly complex but syntactic operations of biochemistry.) The
notion of "meaning" is useful for human-human folk interactions but
the hypothesis of AI (and cognitive science in general) is that
"meaningful" behavior is the result of a great many "mindless" (i.e.
syntactic) operations (whether they are directly in circuitry or data
structures in the memory of more general intepretation circuitry).
A simple example of meaning-from-syntax is the use of state space
heuristic search (totally syntactic) to give an overall behavior that
one might call "purposive" (e.g. a chess playing program "wanting" to
checkmate its opponent).
The only "evidence" for meaning is probably from introspection. Of
course, I can (and do) describe myself as having "meanings" because I
can use that word to describe a certain class of complex behaviors
and I happen to also exhibit those complex behaviors. But because I
describe myself in this way does not require that I actually have
some magical "meanings" that are something other than syntactic
operations. Likewise, any really complex robot, capable of forming
models of itself and of others, will take an intentional stance, both
toward those other complex agents, AND toward itself -- i.e.
attributing "meanings" to itself! So what? It's all ultimately just
syntax. The trick is to figure out what class of marvelously complex
syntactic operations brings about behaviors that deserve the folk
psychological term of "meaning". (This reductionist approach in
cognitive science is similar to that in the physical/natural
sciences.)
Ask the LISP interpreter (that's executing code that creates
some natural language understanding system S) if it "understands"
anything and, of course, it doesn't. Ask S, however, and you will get
an answer. We don't expect the LISP interpreter to "understand" what
it's doing, so why should we EXPECT Searle to understand Chinese???
However, if we ask the Chinese persona what it's like to understand
Chinese we will get an answer back (in Chinese).
For all my disagreements with Harnad, I think that he is concerned with
an extremely interesting question, namely, what is the role that
physicality plays in cognition? As we know, two "functionally
identical" computations on two machines with different architectures
are only similar in their computations at some level of abstraction.
Below that level of abstraction, what the machines are doing physically
may be very different. AI researchers believe that "consciousness" can
(ultimately) reside on many different physical substrata as long as
the computations are similar at some (as yet unspecified) level of
abstraction and this level of abstraction can be modeled by symbol
manipulation. The support for this view is that there seems to be no
limit to the granularity of symbols and symbol manipulation (i.e. they
can be made to correspond to the foldings of individual proteins if
these are deemed essential in constructing the operations of a mind).
Also, since we can only judge intentionality via behavior,
pragmatically we never have to consider any level of abstraction below
that level of computation that gives us behavior that appears
intentional.
One final comment. There are two different uses of the term
"grounding":
1. that representations should be rich enough to encode any
perceptual information.
2. that physical transducers are required for intentionality.
I accept 1. but not 2. If a simulated robot could pass the TTT test
within a virtual reality world, it would be grounded in that world
but there would be no physical transducers. (I have never figured out
why Harnad rejects out of hand the possibility of a "brain in a vat"
whose I/O channels are wired up to a computer so that the brain
thinks it's seeing, standing, etc. Perhaps he rejects it because, if he
doesn't, then his whole requirement for physical transducers falls
apart.)
Since Harnad plugs his own writings in this area, I will also:
Dyer, M. G. Intentionality and Computationalism: Minds, Machines,
Searle and Harnad. Journal of Experimental and Theoretical
Artificial Intelligence, Vol. 2, No. 4, 1990.
Dyer, M. G. Finding Lost Minds (Author's reply to S. Harnad's "Lost in
the Hermeneutic Hall of Mirrors"). Journal of Experimental and
Theoretical Artificial Intelligence, Vol. 2, No. 4, 1990.
--------------------
Date: Thu, 23 Apr 92 20:21:46 EDT
From: "Stevan Harnad"
ON MEANING: VIRTUAL AND REAL
I will preface my reply to Mike Dyer with a few points that should be
kept in mind in my response:
(1) By "meaning," I mean subjective meaning, e.g., what it is like for
a real English speaker (and not for a non-English-speaker or a stone)
to hear and understand what spoken English is about. When I say that
there is no real meaning in a symbol system, just symbols that are
systematically interpretable as if they meant something, I mean that
subjective meaning is absent: Either nobody is home at all (as in a
stone) or certain particular symbols happen to have no subjective meaning
for the system (as in Searle's Chinese Room).
(2) "Grounding" is a robot's capacity to interact with the real world
of objects, events and states of affairs that its symbols are
interpretable as being about. The semantic interpretations of the symbols
and the robotic interactions with the objects must cohere
TTT-indistinguishably with one another. This means that the symbol use is
not constrained by syntax alone.
Grounding is not provably a necessary or a sufficient condition
for subjective meaning (though it may in reality be necessary).
(3) The trouble with "brains in vats" is that they equivocate between
(a) real de-afferented brains (with sensory surfaces removed, but all the
rest of the neural hardware -- most of it analog -- intact) and
(b) physical symbol systems (computers without any peripheral I/O
devices). These two are radically different, and projecting assumptions
about one onto the other leads nowhere. Brain-in-vat arguments usually
further equivocate on the two senses of internal/external discussed
earlier: inside/outside the "body" and inside/outside the "mind."
Apart from the insistence on not conflating any of these things, I have
no objections to brain-in-vat talk.
(4) As in (3) above, I insist on maintaining the distinction between
real physical objects (like planes, furnaces, neurons and transducers)
and their "virtual" counterparts (computer-simulated planes, etc.), be
they ever so computationally equivalent to one another. It is the
perpetual blurring of this boundary in particular that leaves me no
choice but to keep repeating boringly to Mike Dyer that he seems to be
hopelessly lost in a hermeneutic hall of mirrors he has created by
overinterpreting systematically interpretable computations and then
reading off the systematic interpretations themselves by way of evidence
that the virtual world is as as real as the real one.
Michael G Dyer
md> what if we build an artificial neural network circuitry (with ion flow
md> and/or action potential timings identical to those of some person's
md> brain, etc.) and then give it the same inputs that a drowning person
md> would receive? Who is to say that this artificial neural network won't
md> have the subjective experience of drowning?
Is this a purely computational simulation of a neural network (i.e., a
bunch of squiggles and squoggles that are interpretable as if they were
ions, action potentials, etc.)? Or is a synthetic neural network with
the same causal powers as the the organic neural network (i.e., the
capacity to transduce all the same real physical input the neural
network gets)? If it's the former, then it's really all just squiggles
and squoggles, no matter how you can systematically interpret it. If
it's the latter then it's a real artificial neuronal circuit and can in
principle have real subjective experiences (but then it's irrelevant to
this discussion of pure computation and whether or not pure computation
can have subjective experiences).
md> Is the physical act of something flying thru the air a computation? I
md> think not... But does the EXPERIENCE of flying fall into a certain
md> class of computations? No one really knows, but my bet is "yes". I
md> think that certain classes of computations actually have subjective
md> inner experiences.
The trouble with experiences is that all but your own are out of sight,
so you are free to interpret any external object or process as if it had
experiences. Trouble is, there's a right and wrong of the matter (even
though the other-minds barrier normally prevents us from knowing what it
is). There were reasons, for a while, for entertaining the hypothesis
that experiences might be implemented computations. A lot of this
discussion is about reasons why that hypothesis has to be reconsidered
and discarded.
md> [why is it] ok to have Searle do all symbol manipulations (that might
md> require a level of granularity where each symbol represents a synapse
md> or something lower) all in his head(!), but... NOT ok for me to
md> examine how one network (i.e. Searle's neurons) might be intertwined
md> with another network (i.e. artificial VLSI circuitry isomorphic to the
md> symbol relations and manipulations that make up a Chinese persona)?
Until further notice, real neurons have nothing to do with this. What
Searle and the TT-passing computer he is duplicating are doing is
implementation-independent. We don't know what the real brain does; let
us not presuppose anything. Nor do I know what you are imagining
intertwining: virtual neurons and what? It's all just squiggles and
squoggles!
[Here's a prophylactic against hermeneutics: "When in certainty,
de-interpret all symbols and see what's left over."]
md> Harnad and Searle seem to assume that, no matter how complex, any kind
md> of software system has no feelings. How do they know? Harnad claims we
md> can simply ASK Searle to find out what it's like to understand English
md> but he won't allow us to simply ASK the Chinese persona to find out
md> what it's like to understand Chinese.
Because of the other-minds problem we cannot KNOW that anyone else but
ourselves has feelings (or no feelings, as the case be). I am prepared
to believe other people do, that animals do, and that various
synthetic systems might too, particularly TTT-scale robots. I'm even
prepared to believe a computer might (particularly since I can't KNOW
that even a stone does not). There is only one thing I am not prepared
to believe, and that is that a computer has feelings PURELY IN VIRTUE
OF RUNNING THE RIGHT PROGRAM (i.e., the physical symbol system
hypothesis). But, unfortunately, that's precisely what's at issue
here.
You fault me for believing Searle (and his quite reasonable explanation
of what is going on -- meaningless symbol manipulation) rather than the
Chinese squiggles and squoggles. But you are prepared to believe that
Searle has gotten multiple personality merely as a consequence of
having memorized and performed a bunch of symbol manipulations, just
because of what the symbols are interpretable as meaning.
Finally (although I don't want to push the premise that such a
TT-passing computer program is even possible too hard, because we've
accepted it for the sake of argument), you don't seem too troubled by the
fact that the Chinese "persona" couldn't even tell you what Searle was
wearing at the moment. Any self-respecting multiple personality could
manage that. Doesn't this suggest that there might be a bit more to
real-world grounding and the TTT than is apparent from the "just ask the
simulation" perspective?
md> What if the robot received visual input from a VR world rather than the
md> real world? ... There go those visual input transducers Stevan Harnad
md> needs so badly!)
Real robots have real sensory surfaces. I have no objection to those
real sensory surfaces being physically stimulated by stimulation
generated by a simulated world, itself generated by a computer. (My
robot would then be like a kid sitting in a driving simulator.) That would
show nothing one way or the other. But please don't talk about
de-afferenting my robot and reducing him to a "brain-in-vat" and then
piping the computer-generated input straight to THAT, because, as I
said before, neither of us knows what THAT would be, To assume
otherwise (e.g., that it would be a computer) is simply to beg the
question!
md> each neuron appears to respond to its inputs (including its local
md> chemical environment) without requiring any sort of thing called
md> "meaning". The term "meaning", as far as I can tell, is simply used to
md> refer to incredibly complex syntactic types of operations. [If] a robot
md> (or person) is organized to behave in certain, very complex ways, then
md> we tend to take (as Dennett says) an "intentional stance" toward it,
md> but that doesn't mean there is anything other than syntax going on.
Being interpretable (by an outsider) as having subjective meaning, no
matter how practical or useful, is still not the same as (and certainly
no guarantor of) actually having subjective meaning. Subjective meaning
does NOT simply refer to "incredibly complex syntactic types of
operations"; and, as usual, neurons have nothing to do with this (nor are
their activities "syntactic"). And where subjective meaning is going on
there is definitely more than (interpretable) syntax going on.
md> The only "evidence" for meaning is probably from introspection. Of
md> course, I can (and do) describe myself as having "meanings" because I
md> can use that word to describe a certain class of complex behaviors and
md> I happen to also exhibit those complex behaviors. But because I
md> describe myself in this way does not require that I actually have some
md> magical "meanings" that are something other than syntactic operations.
You don't really understand English and fail to understand Chinese
because you "describe [yourself] as having `meanings'" but because
there's real subjective understanding of English going on in your head,
along with real subjective experience of red, pain, etc. Besides really
experiencing all that, you're also describable as experiencing it; but some
systems are describable as experiencing it WITHOUT really experiencing
it, and that's the point here! Explanatory convenience and "stances" --
by outsiders or by yourself -- have nothing whatsoever to do with it.
There's nothing "magic" about it either; just something real!
md> Ask the LISP interpreter (that's executing code that creates some
md> natural language understanding system S) if it "understands" anything
md> and, of course, it doesn't. Ask S, however, and you will get an answer.
md> We don't expect the LISP interpreter to "understand" what it's doing,
md> so why should we EXPECT Searle to understand Chinese??? However, if we
md> ask the Chinese persona what it's like to understand Chinese we will
md> get an answer back (in Chinese).
Taking S's testimony about what it's like to understand Chinese as
evidence against the claim that there is no real subjective
understanding going on in there is like taking the fact that it "burns"
(simulated) marshmallows as evidence against the claim that a
(simulated) fire is not really hot. This is precisely the type of
hermeneutic credulity that is on trial here. One can't expect to gain
much credence from simply citing the credulity in its own defense
(except from someone else who is caught up in the same hermeneutic
circle).
md> If a simulated robot could pass the TTT test within a virtual reality
md> world, it would be grounded in that world but there would be no
md> physical transducers. I have never figured out why Harnad rejects out
md> of hand the possibility of a "brain in a vat" whose I/O channels are
md> wired up to a computer so that the brain thinks it's seeing, standing,
md> etc.
md> Michael G Dyer
Virtually grounded, not really grounded, because of course that's only
a virtual TTT, not a real one. But the whole point of the TT/TTT
distinction was to distinguish the merely virtual from the real!
Stevan Harnad
----------------
From: Pat Hayes
If we include (as we should) linguistic input, it seems clear that
the structures and processes will be largely symbolic. I think that
vision and other perceptual modes involve symbols from an early
stage, but I agree that's just one intuition against another.
I think there is something important (though vague) here:
No, you have missed the point of the example. The difference is that in this
example, the sytematicity is between the syntax of one numeral and the
actual (physical?) length of another. This is not the same kind of
connection as that between some symbols and a piece of the world that
they can be interpreted as referring to. It requires no external
interpreter to make it secure, the system itself guarantees that this
interpretation will be correct. It is a point that Descartes might have
made: I don't need to be connected to an external world in any way in
order to be able to really count.
I am distinguishing them, and claiming to have a case of the latter.
Now of course if you insist a priori that meaning can only take place
in a mind, and a system like this isn't one, then you have the day; but
that seems to beg the question.
Well, that is a very good question. That is exactly what computer science
is all about. What is different in having a machine that can run
algorithms from just being able to run algorithms? I take it as
obvious that something important is, and that answering that question
is, pace Brian Smith's recent message, essentially an empirical
matter. We are discovering so what.
I agree it seems almost paradoxical. But as I emphasised, the key
is that these ARENT identical sequences of states. Thats what computers do.
They put algorithms into the physical world, give them a life of their
own, enable them to become real in some important sense. Its a hard
sense to get exactly clear, but it seems very real. The difficulty is
illustrated well by the awful trouble software is giving to legal
concepts, for example. Since they are textual and can be copied, and
do nothing until 'performed', they seem like things to be copyrighted.
But in many ways they are more like pieces of machinery suitable for
patenting. They are both, and neither: they are something new.
Ah, maybe that is a bad heuristic sometimes. Clearly if you insist that
this can always be done to computer insides but not always to human
insides, then you are never going to see meaning in a machine.
The computer is doing less than me, but thats my point: the PROGRAM is
more responsible for what is happening. The computer is essentially
BECOMING the program, one might almost say, giving its symbolic patterns
momentary flesh so that they act in the world. And thats what a human
reader of the code is not doing (unless hypnotised or somehow in its grip in
some unreal way).
Nah nah, question begging again!
OK, last word is yours. Who is taking the bets?
Pat Hayes
------------
After a brief lull (mainly because I was out of town and fell
behind with the postings) the "What is Computation" discussion
proceeds apace... -- SH
Date: Wed, 29 Apr 1992 22:20:04 -0400
From: mcdermott-drew@CS.YALE.EDU (Drew McDermott)
I will respond to both Stevan Harnad and Pat Hayes; I wrote:
Pat Hayes replied:
I do have axes to grind, but this isn't one of them. I do not dispute
that computers do normally succeed in referring to things and states
to exactly the same degree that we do. But the question at issue is
whether this fact is part of the *definition* of "computer." I'm
pretty sure that Pat and I agree here: that computers are defined as
physical instantiations of formal automata (I won't repeat David
Chalmers's excellent statement of the position), and they happen to
make excellent semantic engines when connected up to things their
states can come to refer to.
Now back to Stevan:
You raise four semi-independent issues:
But it isn't! We're talking about whether semantic interpretability
is part of the *definition* of computer. For that to be the case,
everything the computer does must be semantically interpretable.
Does it cease to be a computer during the interludes when its
behavior is not interpretable?
I assumed that your original claim was that a computer had to
correspond to an interpreted formal system (where, in the usual case,
the users supply the interpretation). But that's not what you meant
at all. An interpreted formal system includes a mapping from states
of the system to states of the world. Furthermore, there is a
presumption that the state-transition function for the formal system
preserves the meaning relation; if the state of affairs denoted by system
state S1 holds, then the state of affairs denoted by the following
state also holds. But now it's clear that neither you nor Pat is
proposing anything of this sort. Instead, you seem to agree with me
that a computer is a physical embodiment of a formal automaton, plus a
kind of loose, pragmatic, fault-prone correspondence between its
states and various world states. Given this agreement, let's
simplify. Clearly, the semantic interpretation is no part of the
definition of computer. We can identify computers without knowing
what interpretation their users place on them.
I have lots more examples. Today I saw a demo of a computer
generating the Mandelbrot set. (It was the DEC alpha chip; definitely
the Mandelbrot engine of choice.) Unquestionably a computer; what did
its states denoteg It seems clear at first: The color of each pixel
denoted a speed of convergence of a certain numerical process. But
that's just the platonic ideal. But platonic referents are very
unsatisfactory for our purposes, on two counts. (1) If we count
platonic referents, then *any* formal system has a trivial set of
referents. (2) The viewer of the screen was not interested in this set
of referents, but in the esthetic value of the display. Hence the
real universe of the users was the universe of beauty and truth.
Vague, of course, but *computers' semantic relations are normally
vague.*
The "players and moves" mostly don't exist, of course, since they
include entities like King Koopa and Princess Toadstool. The child
playing the game thinks (sort of) that the pictures on the screen
refer to a certain universe. Or maybe they *constitute* a universe.
It's hard to be precise, but I hope by now vagueness doesn't bother
you. Of course, the engineer that wrote the game knows what's
*really* going on. The input signals refer to presses of control
buttons by the game player. Output signals refer to shapes on a
screen. But it would be absurd to say that the game player's version
of the semantics is only an illusion, and the real purpose of the
system is to map buttons pushes onto screen alterations. Shall we
say, then, that there are *two* computers here --- one formal system,
but two competing semantic interpretationsg I'd rather say that there
is one computer, and as many interpretations as are convenient to
posit --- including possibly zero. [Also, the engineer's
interpretation is almost trivial, because all it refers to are the
program's own inputs and outputs; almost, but not quite, because
normally the inputs are real pressures on buttons and the outputs are
real photons emanating from a screen.]
[It's the program that computes a quick guess of how good a board
position is without any further lookahead.]
But if only "some" of the states have to be interpretable, then is the
system only a computer some of the timeg Or to some degree?
You're forgetting which side of the argument you're on. *I'm* arguing
that such interpretations are epiphenomenal. *You're* arguing that
the interpretation is the scaffolding supporting the computerhood of
the system. Or perhaps I should picture a trapeze; if the system
spends too much time between interpretable states, it falls from
computational grace.
At this point you seem to have crossed over and joined my side
completely. You are admitting that there can be machines that embody
formal symbol systems whose states are just meaningless squiggles and
squoggles.
Yes! Right!
You're going to feel terrible when you realize you've agreed with me!
It matters in the traditional framework I was assuming you endorsed.
I see that you don't. Pat does, however:
I'm with Stevan on this one. The rule-separation mechanism may in
some sense restore consistency, but it's hard to explain how it does
this *semantically.* (The syntactic mechanism must somehow affect the
meanings of the rules, or affect the sense in which the system
"believes" its rules.) Fortunately, we are not called on to provide a
systematic semantics.
I refer you to Chalmers. A brief summary: A system is a computer if
its physical states can be partitioned into classes that obey a
transition relation.
Drew McDermott
------------------------------------
Date: Thu, 7 May 92 19:01:34 EDT
From: "Stevan Harnad"
ON IMPLEMENTING ALGORITHMS MINDLESSLY
Pat Hayes
The only problem with "including" (as you put it) linguistic input is
that, without grounding, "linguistic input" is just meaningless
squiggles and squoggles. To suppose it is anything more is to beg the
main question at issue here.
To categorize is to sort the objects in the world, beginning with their
sensory projections. It is true that we can sort names and descriptions
too, but unless these are first grounded in the capacity to sort and
name the objects they refer to, based on their sensory projections,
"names and descriptions" are just symbolic gibberish that happens to
have the remarkable syntactic property of being systematically
translatable into a code that we are able to understand. But that's all
MEDIATED meaning, it is not autonomously grounded. And a viable
candidate for what's going on in our heads has to be autonomously
grounded; it can't just be parasitic on our interpretations.
Another thing you might have meant was that symbols play a role even
in sensory categorization. That may be true too, but then they better in
turn be grounded symbols, otherwise they are hanging from a (Platonic?)
skyhook.
MENTAL counting is moot until its true underlying mechanism is known;
you are simply ASSUMING that it's just symbol manipulation.
But your point about the correspondence between the internal numerical
symbol for the length of an internal sequence can be made without
referring to the mental. There is certainly a correspondence there,
and the interpretation is certainly guaranteed by causality, but only
in a slightly more interesting sense than the interpretation that every
object can be taken to be saying of itself "Look, here I am!" That
too is a guaranteed relation. I might even grant that it's "grounded,"
but only in the trivial sense that an arbitrary toy robot is grounded.
Symbols that aspire to be the language of thought cannot just have a few
fixed connections to the world. The systematicity that is needed has to
have at least the full TT power of natural language -- and to be
grounded it needs TTT-scale robotic capacity.
Arithmetic is an artificial language. As such, it is an autonomous
formal "module," but it also happens to be a subset of English.
Moreover, grounded mental arithmetic (i.e., what we MEAN by numbers,
addition, etc.) is not the same as ungrounded formal arithmetic (symbols
that are systematically interpretable as numbers).
That having been said, I will repeat what I said earlier, that there
may nevertheless be something to learn from grounded toy systems such
as the numerical one you describe. There may be something of substance
in such dedicated systems that will scale up to the TTT. It's just not
yet obvious what that something is. My guess is it will reside in the
way the analog properties of the symbols and what they stand for (in
this case, the physical magnitude of some quantity) constrain activity
at the syntactic level (where the "shape" of the symbols is normally
arbitrary and hence irrelevant).
I think you're missing my point. The important thing is that the
algorithm be implemented mindlessly, not that it be implemented
mechanically (they amount to the same thing, for all practical
purposes). I could in principle teach a (cooperative) two-year old who
could not read or write to do rote, mechanical addition and
multiplication. I simply have him memorize the finite set of
meaningless symbols (0 - 9) and the small set of rules (if you see "1"
above "3" and are told to "add" give "4", etc.). I would then have a
little human calculator, implementing an algorithm, who didn't
understand a thing about numbers, just as Searle doesn't understand a
word of Chinese.
Now let me tell you what WOULD be cheating: If any of what I had the
child do was anything but SYNTACTIC, i.e., if it was anything other than
the manipulation of symbols on the basis of rules that operate only on
their (arbitrary) shapes: It would be cheating if the child (mirabile
dictu) happened to know what "odd" and "even" meant, and some of the
calculations drew on that knowledge instead of just on the mechanical
algorithm I had taught him. But as long it's just mechanical syntax,
performed mindlessly, it makes no difference whatsoever whether it is
performed by a machine or stepped through (mechanically) by a person.
Now if you want to appreciate the real grip of the hermeneutical circle,
note how much easier it is to believe that an autonomous black box is
"really" understanding numbers if it is a machine implementing an
algorithm mechanically rather than an illiterate, non-numerate child,
who is just playing a symbolic game at my behest. THAT's why you want to
disqualify the latter as a "real" implementation, despite the fact that
the same syntactic algorithm is being implemented in both cases, without
any relevant, nonarbitrary differences whatsoever.
I am sure that whatever is REALLY going on in the head can also be
deinterpreted, but you mustn't put the cart before the horse: You
cannot stipulate that, well then, all that's really going on in the
head is just symbol manipulation, for that is the hypothesis on trial
here!
{Actually, there are two semi-independent hypotheses on trial: (1) Is
anything NOT just a computer doing computation? and, (2) Are minds just
computers doing computation? We agree, I think, that some things are
NOT computers doing computation, but you don't think the mind is one of
those noncomputational things whereas I do.]
I had recommended the exercise of deinterpreting the symbols so as to
short circuit the persuasive influence of those properties that are
merely byproducts of the interpretability of the symbols, to see
whether there's anything else left over. In a grounded TTT-scale robot
there certainly would be something left over, namely, the robotic
capacity to discriminate, categorize and manipulate the objects, events
and states of affairs that the symbols were about. Those would be there
even if the symbols were just gibberish to us. Hence they would be
grounding the interpretations independently of our mentalistic
projections.
Stevan Harnad
----------------
Date: Thu, 7 May 92 19:23:41 EDT
From: "Stevan Harnad"
To all contributors to the "What is Computation?" Symposium:
Jim Fetzer, Editor of the (paper) journal MINDS AND MACHINES has
expressed interest in publishing the Symposium (see below) as a special
issue of his journal. He has already published one such paper version
of a "Skywriting" Symposium similar to this one, which will appear
shortly as:
Hayes, P., Harnad, S., Perlis, D. & Block, N. (1992) Virtual Symposium
on the Virtual Mind. Minds and Machines (in press)
That Symposium was smaller, with fewer participants, but I hope that
the participants in this larger one will want to do this too. I will
generate formatted hard copy, and the participants can polish up their
prose and add references, but what we must avoid is pre-emptive
re-writing that makes one another's contibutions retroactively
obsolete. We also cannot coordinate diverging iterations of rewriting. We
should instead preserve as much as possible the interactive
"Skywriting" flavor of the real-time exchange, as we did in the other
Symposium.
Please let me know which of you are (and are NOT) interested in
publication. In the meanwhile, we can continue for a few more
iterations before involking cloture. Perhaps the prospct of publication
will change the style of interaction from this point on, perhaps not...
Several backed up postings are still waiting in the wings.
Bets wishes,
Stevan,
From: jfetzer@ub.d.umn.edu (james fetzer)
Date: Wed, 22 Apr 92 18:20:34 CDT
Stevan,
This stuff is so interesting that I might devote a whole issue to it.
How would you like to guest edit a special issue on the topic, "What is
Computation?", for MINDS AND MACHINES? Beginning in 1993, we will be
going to 125 pages per issue, and each page runs about 600 words. So
that represents the maximum length of material I can use. If you like
the idea, I have no deadline in mind, but I do believe that it may
require something like position papers from the principal contributors
in addition to exchanges. I am not envisioning what is nothing more
than one continuous exchange, but I will be open-minded about any
suggestions you may have about how we proceed.
Let me know if you like this suggestion. Jim
From: jfetzer@ub.d.umn.edu (james fetzer)
Date: Thu, 30 Apr 92 16:09:12 CDT
On the proposed new skywriting project tentatively entitled, "What is
Computation?", let's take things one step at a time. I like the project
but I know we need to agree on a few ground rules.
(1) Let's start with a tentative length of 50 pages at 600 words per
(30,000) and see how that plays. If you should need more, then we can
work that out, but I would like to try 30,000 first.
(2) The authors must make appropriate reference to works that have
been previously discussed or are otherwise relevant to their views.
(Skywriting seems to invite unattributed use of ideas, etc., which both
of us need to discourage.)
(3) The version that is submitted will be subject to review in
accordance with the journal's standing policies. Such review may lead
to revisions of certain parts of the exchange, but every effort will be
made to adhere as closely as possible to the spirit of the original.
(4) When the final version is submitted to the publisher for
typesetting, only typographical corrections will be allowed, lest we
bog down in changes that generate other changes, over and over, due to
the large number of contributors, etc.
(5) You will (once again) assume responsibility for preparing the
manuscript for submission and will execute the permission to publish
form on behalf of all of the contributors and will be responsible for
overall proofing of the typeset manuscript, coordinating with the
others as necessary.
If this sounds agreeable with you, then by all means, let us proceed.
Keep me posted as things develop, but I would recommend that the number
of contributors be kept to a managably small number, whatever that is.
Jim Fetzer
Editor
MINDS AND MACHINES
Date: Fri, 8 May 92 13:10:51 EDT
From: "Stevan Harnad"
Date: Thu, 7 May 92 19:18:06 EST
From: David Chalmers
Sounds fine, an interesting idea. I'll probably make one more
contribution within the next week or so, addressing various points
that have come up.
Cheers, Dave.
Date: Fri, 8 May 92 12:46:36 -0400
From: "John C. Haugeland"
Dear Jim and Steve:
I have been following the discussion on "What is computation" with
(predictable) interest, but I have not yet participated because of a backlog
of prior commitments. These commitments will keep me preoccupied, alas, for
the next six weeks as well -- mostly travelling. I have been intending to
plunge in when I get back (third week of June); however, the mention of a
special issue of _Minds and Machines_ devoted to the topic makes me think
that I had at least declare my intentions now, lest I be left behind. What
I have in mind is writing a brief (eg, 3000 word) "position paper" on the
topic, with reference to the discussion so far mostly to give credit. But,
as indicated, I can't get to it for a while. Is there any possibility of
this, or does the timing wipe me out?
John Haugeland
haugelan@unix.cis.pitt.edu
-----------
[Reply: The Symposium will continue, so there is still time for John
Haugeland and others to join in. SH.]
----------
Date: Fri, 8 May 92 15:58:43 EDT
From: "Stevan Harnad"
Drew McDermott (mcdermott-drew@CS.YALE.EDU) wrote:
There is a systematic misunderstanding here. I proposed semantic
interpretability as part of the definition of computation. A computer
would then be a device that can implement arbitrary computations. That
doesn't mean everything it does must be semantically interpretable.
Uninterpretable states in a computer are no more problematic than idle
or power-down states. What I suggest, though, is that if it was ONLY
capable of uninterpretable states (or of only being idle or off), then
it would not be a computer.
The interpretation of any particular computer implementing any
particular computation is not part of my proposed definition of a
computer. A computer is a physical system with the capacity to
implement (many, approximately all) nontrivial computations (=
INTERPRETABLE symbol systems), where "nontrivial" is a cryptographic
complexity-based criterion.
Not that absurd, but never mind. There are certainly many levels of
interpretation (virtual systems) in some computers implementing some
programs. One virtual system need not have primacy over another one. My
point is made if there is any systematic interpretability there at all.
We should keep it in mind that two semi-independent questions are under
discussion here. The first has nothing to do with the mind. It just
concerns what computers and computation are. The second concerns
whether just a computer implementing a computer program can have a
mind. The groundedness of the semantics of a symbol system relates to
this second question. Computer video-games and their interpretations
are hopelessly equivocal. They are just implemented squiggles and
squoggles, of course, which are in turn interpretable as referring to
bit-maps or to images of fictitious entities. But the fictitious
entities are in OUR heads, and even the perception of the "entities" on
the video-screen are mediated by our brains and their sensory
apparatus. Without those, we have only squiggles and squoggles (or, in
the case of the dedicated video system, hard-wired to its inputs and
outputs, we have squiggles and squoggles married to buttons and
bit-mapped CRT screens).
You seem to be confusing the question of interpretability with the
question of the groundedness of the interpretation. My criterion for
computerhood is the capacity to implement arbitrarily many different
(nontrivially) interpretable symbol systems. The interpretability of
those systems is critical (in my view) to their being computational at
all. Without interpretability you have random gibberish,
uninterpretable in principle. But even interpretable (nonrandom) symbol
systems are just gibberish unless we actually project an interpretation
on them. This suggests that interpretability is not enough. If ANY kind
of system (computational or not) is to be a viable candidate for
implementing MENTAL states, then it cannot be merely interpretable; the
interpretation has to be INTRINSIC to the system: it has to be
grounded, autonomous, independent of whatever we do or don't project
onto it.
Because of the grip of the hermeneutic circle, it is very hard, once we
have projected an interpretation onto a system, to see it for what it
really is (or isn't) on its own, independent of our interpretations.
That's why I recommend de-interpreting candidate systems -- reducing them
to the gibberish ("squiggles and squoggles") that they really are, to see
what (if anything) is left to ground any meanings in. A pure symbol
system (like some of the earlier overinterpreted chimpanzee "languages")
could not survive this nonhermeneutic scrutiny. A TTT-scale robot could.
I too think computers/computation can be distinguished from their
(non-empty) complement, and perhaps by the elaboration of a criterion
like that one. But this still leaves us miles apart on the question:
"Ok, given we can objectively distinguish computers from noncomputers,
what has this to do with the question of how to implement minds?"
Stevan Harnad
---------------------
Date: Mon, 4 May 1992 10:31:25 +0200
From: Oded.Maler@irisa.fr (Oded Maler)
One outcome (at least for me) of the previous round of postings on the
symbol-grounding problem (1990) was that I became aware of the fact
that current computational models are not suitable for dealing with the
phenomenon of computers interacting in real-time with the real world.
Consequently, with several collaborators, I did some preliminary work
on what we call "hybrid dynamical systems" which combine discrete
state-transition dynamics with continuous change. This is technical
work, and it is not supposed to solve the philosophical problems
discussed here; I mention it just to show that such discussions, even
if they don't seem to converge, might have some useful side-effects.
Now to the question of what is a computation. My current view is that
computations are idealized abstract objects that are useful in
describing the structure and the behavior of certain systems by
focusing on the "informational" aspects of their dynamics rather on the
"materialisic/energetic" aspects. This abstraction, not surprisingly,
turns out to be useful in designing and analyzing certain systems such
as synchronized switching devices, also known as general-purpose
computers. It is sometimes also useful for analyzing the behavior of
humans when they perform tasks such as adding numbers.
The question of why such a computational interpretation is more
reasonable for some systems than for others is intriguing, and I don't
know if a quantitative observer-independent borderline can be put. Even
real airplanes do not necessarily fly, unless flying is a useful
abstraction for us when we want to get to a conference - "you cannot
participate in the same flight twice" (to rephrase what's-his-name,
badly translated from Greek to Hebrew to English).
So I think the question will reduce to the two related problems:
(1) What is "information"? -- because this seems to be the
characterizing feature of computational dynamics.
(2) The relations between things and their descriptions.
Oded Maler
------------------------------------
From: Stevan Harnad (harnad@princeton.edu)
For me, flying is not just a "useful abstraction," it's something you
really do, in the real air, otherwise you really fall. I agree with you
that one of the problems here concerns the relation between things and
their descriptions: The problem is when we confuse them! (And the
concept of "information" alas seems just as subject to the problem of
intrinsic versus derived meaning (i.e., groundedness) as computation is.)
Stevan Harnad
------------------------------------
Date: Thu, 23 Apr 92 21:25:14 -0400
From: davism@turing.cs.nyu.edu (Martin Davis)
Stevan,
I've been watching the (real and virtual) stones flying in this
discussion, amazed that none of the hermeneutic mirrors are broken. I
had resolved to be safe and shut up. But here goes! I'm throwing, not
stones, but rather, all caution to the wind.
Please forgive me, but this is what I really think: if and when
brain function is reasonably well understood (and of course that
includes understanding how consciousness works), this entire
discussion will be seen as pointless, in much the same way that we now
regard the battles that used to rage about the ether as pointless. In
particular, I believe that the paradoxes of subjectivity ("How can I
know that anyone other than me experiences redness?") will seem no
more problematic than such equally compelling conundrums as: How can
light waves possibly travel through empty space without a medium in
which they can undulate? We (or rather our heirs) will know that other
people experience redness because it will be known exactly what it is
that happens in their brains and ours when redness is experienced. And
then the objection that we cannot know that their experience is like
ours, or even that they are experiencing anything, will just seem
silly.
Whether a TT-passing computer is in any reasonable sense conscious of
what it is doing is not a question we can hope to answer without
understanding consciousness. If, for example, Dennett is right about
consciousness, then I can perfectly well imagine that the answer could
be "yes", since I can't see any reason why such mechanisms couldn't in
principle be built into a computer program.
Martin Davis
-----------------------------------------
Martin Davis (davism@turing.cs.nyu.edu) wrote:
md> if and when the brain function is reasonably well understood (and of
md> course that includes understanding how consciousness works), this
md> entire discussion will be seen as pointless... the paradoxes of
md> subjectivity ("How can I know that anyone other than me experiences
md> redness?") will seem no more problematic... [We] will know that other
md> people experience redness because it will be known exactly what it is
md> that happens in their brains and ours when redness is experienced. And
md> then the objection that we cannot know that their experience is like
md> ours, or even that they are experiencing anything, will just seem
md> silly.
Martin,
You may be surprised to hear that this a perfectly respectable
philosophical position (held, for example, by Paul Churchland and many
others) -- although there are also MANY problems with it, likewise
pointed out by many philosophers (notably, Tom Nagel) (and although the
parenthetic phrase about "understanding how consciousness works" comes
perilously close to begging the question).
But you will also be surprised to hear that this is not a philosophical
discussion (at least not for me)! I'm not interested in what we will or
won't be able to know for sure about mental states once we reach the
Utopian scientific state of knowing everything there is to know about
them empirically. I'm interested in how to GET to that Utopian state.
And if it should be the case (as Searle and others have argued) that
the symbolic road is NOT the one that leads there, I would want to know
about that, wouldn't you? Perhaps this is the apt point to trot out (not
for the first time in the symbol grounding discussion) the reflection
of the historian J.B. Hexter on the value of negative criticism:
in an academic generation a little overaddicted to "politesse," it
may be worth saying that violent destruction is not necessarily
worthless and futile. Even though it leaves doubt about the right
road for London, it helps if someone rips up, however violently, a
"To London" sign on the Dover cliffs pointing south...
md> Whether a TT-passing computer is in any reasonable sense conscious of
md> what it is doing is not a question we can hope to answer without
md> understanding consciousness. If, for example, Dennett is right about
md> consciousness, then I can perfectly well imagine that the answer could
md> be "yes", since I can't see any reason why such mechanisms couldn't in
md> principle be built into a computer program.
Yes, but if you have been following the discussion of the symbol
grounding problem you should by now (I hope) have encountered reasons
why such (purely symbolic) mechanisms would not be sufficient to
implement mental states, and what in their stead (grounded TTT-passing
robots) might be sufficient.
Stevan Harnad
------------------------------------------
Date: Fri, 8 May 92 17:04:08 EDT
From: "Stevan Harnad"
From: dietrich@bingsuns.cc.binghamton.edu Eric Dietrich
Subject: Re: Publishing the "What is Computation?" Symposium
To: harnad@Princeton.EDU (Stevan Harnad)
Stevan: I am interested in publication.
Eric Dietrich
-----------------------------------
Date: Fri 8 May 92 13:39:59-PDT
From: Laurence Press
Dear Steven,
Have you got a publisher in mind for the book? If not, I am a consulting
editor at Van Nostrand Reinhold, and would be happy to talk with them
about it.
Larry Press
-----------------------------------
From: Stevan Harnad
To: Larry Press
Dear Larry,
The context for the idea was actually a proposal from the Editor
of Minds and Machines, James Fetzer, to publish it as a special issue
of his journal. Thanks for your offer too, but unless we encounter
problems in fitting it within the scope of a special journal issue,
it looks as if we're already spoken for!
Best wishes,
Stevan Harnad
---------------------------------
From: Pat Hayes (hayes@cs.stanford.edu)
Date: Tue, 28 Apr 92 18:04:15 MDT
Searle's argument establishes nothing. If one is inclined to accept the
idea that an implemented program might correctly be said to exhibit
cognition, then the scenario Searle outlines - which we all agree to be
fantastic, but for different reasons - suggests that there is an
important difference between a computer running a program and the
process of a human following through the steps of an algorithm, and if
one were to achieve the former with a human as the computer then one
would have a(n admittedly fantastic) situation, something akin to a
fragmented personality. If one is inclined to reject that idea, Searle's
scenario can be taken as further bolstering of that inclination, as
many have noted.
I don't think the other-minds 'barrier' is really germane to the
discussion, as it applies as much to other humans as to (hypothesised)
artifical agents. I take it as observationally obvious that stones
don't have minds, that (most) humans do, and that such things as cats
and mice and perhaps some complex computational systems are best
described as having partial, simple, or primitive minds. Somewhere
between cats (say) and snails (say) the concept becomes sufficiently
unclear as to probably be worthless. (This gradual deterioration of
mentality is not a crisp phase transition, by the way, and I don't think
that there is such a sharp division between mere biology or mechanism
and real intensional thought.)
You wrote:
If one takes this stark a view of the other-minds question then
it seems to me hard to avoid solipsism; and I may not be able
to refute solipsism, but I'm not going to let anyone ELSE
persuade me its true.
We can go on disagreeing for ever, but let me just say that I don't
feel any sense of strain or cost to maintain my views when shown
Searle's curious misunderstandings of computational ideas.
Oh no, I have to profoundly disagree. The question is how formal
symbols in a computational system might acquire meaning. But surely the
words in the English sentences spoken to a machine by a human do not
need to have their meaningfulness established in the same way. To take
English spoken by humans - as opposed to formalisms used by machines -
as having content surely does not beg any of the questions we are
discussing.
But surely by insisting on beginning thus, YOU are begging the question!
Rhetoric again. But look at this carefully. Consider the word
"everyone". What kind of 'sensory projection' could provide the
suitable 'grounding' for the meaning of this? And try "whenever",
"manager" or "unusual". Language is full of words whose meaning has no
sensory connections at all.
Yes. I did, actually.
That particular example may not be very interesting, but the point it
makes is rather more, since it is illustrative of a huge collection of
computational phenomena throughout which interpretation is similarly
guaranteed by causality.This was Brian Smith's point: computation is,
as it were, permeated by meanings causally linked to symbols.
This raises a very interesting question. Let us suppose that you are
basically right about the need for grounding to guarantee meaning. I
believe you are, and have made similar points myself in my 'naive
physics' papers, although I think that English can ground things quite
successfully, so have more confidence in the TT than you do. But now,
how much grounding does it take to sufficiently fix the meanings of the
symbols of the formalisms? Surely not every symbol needs to have a
direct perceptual accounting. We have all kinds of mechanisms for
transferring meanings from one symbol to another, for example. But more
fundamentally, beliefs relating several concepts represent mutual
constraints on their interpretation which can serve to enforce some
interpretations when others are fixed. This seems to be a central
question: just how much attachment of the squiggles to their meanings
can be done by axiomatic links to other squoggles?
Thats exactly the kind of assertion that I feel need not to be taken at
face value.
No, thats exactly where I disagree. A human running consciously through
rules, no matter how 'mindlessly', is not a computer implementing a
program. They differ profoundly, not least for practical purposes. For
example, you would need to work very hard on keeping a two-year-old's
attention on such a task, but the issue of maintaining attention is not
even coherent for a computer.
I know you find observations like this irrelevant to the point you are
making - hence your quick "cooperative" to fend it off - but they are
very relevant to the point I am making. I see an enormous, fundamental
and crucial difference between your 'mindless' and 'mechanical'. The AI
thesis refers to the latter, not the former. To identify them is to
abandon the whole idea of a computer.
I disagree: I think it makes a fundamental difference, and to deny this
is to deny that computation is real. But we are just beating our chests
at one another again.
Nah nah. You are just (re)in-stating the chinese room 'argument'
AGAIN. And it still is convincing if you believe its conclusion, and
not if you don't. It doesn't get anywhere.
No, I repeat: a human running through an algorithm does not constitute
an IMPLEMENTATION of that algorithm. The difference is precisely what
computer science is the study of: how machines can perform algorithms
without human intervention. If you could get your two-year-old's body
to IMPLEMENT addition algorithms, you would almost certainly be liable
for criminal action.
Well, hang on. Surely if you concede that the head's machinations can
be de-interpreted, then indeed you have conceded the point; because
then it would follow that the head was performing operations which did
not depend on the meanings of its internal states. That this is the
point at issue does not make it illegal for me to have won the
argument, you know. But maybe you did not mean to give that up so
quickly. I will let you take that move back before claiming checkmate.
Lets agree to dismiss (1). This Searlean thesis that everything is a
computer is so damn silly that I take it simply as absurd. I don't feel
any need to take it seriously since I have never seen a careful
argument for it, but even if someone produces one, that will just amount
to a reductio tollens disproof of one of its own assumptions.
OK. But what I don't follow is why you regard the conversational
behavior of a successful passer of the TT clearly insufficient to
attach meaning to its internal representations, while you find Searle's
response to the "Robot Reply" quite unconvincing. If we are allowed to
look inside the black box and de-interpret its innards in one case, why
not also the other? Why is robotic capacity so magical in its grounding
capacity but linguistic capacity, no matter how thorough, utterly
unable to make symbols signify? And I don't believe the differences
are that great, you see. I think much of what we all know is attached
to the world through language. That may be what largely differentiates
us from the apes: we have this incredible power to send meaning into
one anothers minds.
Pat Hayes
(PS. The arithmetic example, while very simple, provides an interesting
test for your hermeneutic intuitions. Take two different addition
algorithms. One is the usual technique we all learned involving adding
columns of numbers and carrying the tens, etc. . The other has a bag
and a huge pile of pebbles and counts pebbles into the bag for each
number, then shakes the bag and counts the pebbles out, and declares
that to be the sum. A child might do that. Would you be more inclined
to say that the second, pebble-counting child understood the concept of
number? You can no doubt recognise the path I am leading you along.)
-----------------------
Date: Sun, 10 May 92 13:24:01 EDT
From: "Stevan Harnad"
SYMBOLS CANNOT GROUND SYMBOLS
Pat Hayes (hayes@cs.stanford.edu) wrote:
Although I don't think this kind of "observation" is quite the same as
other empirical observations, let me point out that one can readily agree
with [1 - 3] and utterly disagree with [4], which suggests it might not all
be so "obvious."
Let me also point out on exactly what MY judgment, at least, is based
in these 4 cases. It is based purely on TTT-indistinguishability (note
that I said TTT, i.e., total indistinguishability in robotic
capacities, not merely TT, i.e., indistinguishability only in symbolic
capacities).
(Although there is another potential criterion, TTTT (neural)
indistinguishability, I am enough of a functionalist, and believe the
robotic degrees of freedom are narrow enough, to make this further
constraint supererogatory; besides, the TTTT is certainly not why or how
we judge that other people and animals have minds.)
Animals do not pass the human TTT, but they come close enough. So would
robots, making their way in the world (but, for methodological reasons,
only if they passed the human TTT; we unfortunately do not know enough
about animals' TTT capacities to be able to trust our judgments about
animal-robots' TTT-indistinguishability from their biological
counterparts: this is a serious problem for bottom-up robotics, which
would naturally prefer to take on the amphioxus TTT before facing the
human TTT!).
But you really begin to equivocate with [5]: "best described as having
partial, simple, or primitive minds," because, you see, what makes this
particular question (namely, the "other minds problem," pace Martin Davis)
different from other empirical problems is that it is not merely a
question of finding the "best description," for there also happens to be
a FACT of the matter: There either IS somebody home in there,
experiencing experiences, thinking thoughts, or NOT. And if not, then
attributing a mind to it is simply FALSE, whether or not it is the "best
description" (see Oded Maler's point about things vs. descriptions).
Nor is there a continuum from the mental to the nonmental (as there
perhaps is from the living to the nonliving). There may be higher and
lower alertness levels, there may be broader and narrower experiential
repertoires of capacities, but the real issue is whether there is
anybody home AT ALL, experiencing anything whatever, and that does
indeed represent a "sharp division" -- though not necessarily between
the biological and the nonbiological.
Now no one can know where that division really lies (except by being
the candidate), but we can try to make some shrewd empirical
inferences. Symbolic Functionalism ("thinking is just computation") was
a natural first pass at it, but I, at least, think it has been shown to
be insufficient because of the symbol grounding problem. Robotic
Functionalism ("thinking is what goes on inside grounded TTT-scale
robots") could be wrong too, of course, but until someone comes up with
a principled reason why, I see no cause to worry about heading in that
empirical direction.
For mind-modellers, the other-minds problem is not a metaphysical but a
methodological problem. Abandoning computationalism certainly does NOT
commit us to solipsism.
There's no problem with the content of English for English speakers.
The problem is with the content of English for a computer. English is
grounded only in the heads of minds that understand what it means.
Apart from that, it's just (systematically interpretable) squiggles and
squoggles. The question is indeed how the squiggles and squoggles in a
computer might acquire meaning -- and that certainly isn't by throwing
still more ungrounded squiggles and squoggles at them...
Not at all, I'm trying to answer it. If we start from the recognition
that the symbols in a computer are ungrounded and need to be grounded,
then one possible grounding hypothesis is that the requisite grounding
comes from constraints exerted by symbols' physical connections to the
analog structures and processes that pick out and interact with the the
real-world objects that the symbols are about, on the basis of their
sensorimotor projections. It seems to me that to attempt to ground
systems other than from the sensory-bottom upward is to try to get off
the ground by one's (symbolic?) bootstraps, or by clutching a
(symbolic?) skyhook. I am, however, interested in rival grounding
hypotheses, in particular, non-sensory ones, just as long as they are
GROUNDING hypotheses and not just ways of letting the hermeneutics in by
the back door (as in imagining that "natural language" can ground
symbols).
These objections to bottom-up sensory grounding have been raised by
philosophers against the entire edifice of empiricism. I have attempted
some replies to them elsewhere (e.g. Harnad 1992), but the short
version of the reply is that sensory grounding cannot be investigated
by armchair introspection on word meanings; it will only be understood
through empirical attempts to design grounded systems. What can be said,
however, is that most words need not be grounded directly. The symbol
string "An X is a Y that is Z" is grounded as long as "Y" and "Z" are
grounded, and their grounding can likewise be symbolic and indirect.
The sensory grounding hypothesis is simply that eventually the symbolic
descriptions can be cashed into terms whose referents can be pick out
from their direct sensory projections.
"Everyone," for example, perhaps means "all people." "People," is in
turn beginning to sound more like something we could pick out from
sensory projections. Perhaps even the "all/not-all" distinction is
ultimately a sensory one. But I'm just introspecting too now. The real
answers will only come from studying and then modeling the mechanisms
underlying our (TTT) capacity for discrimination, categorization and
identification.
And I'm not unsympathetic to that point; I just want to see it worked out
and then scaled up to the TTT.
These are empirical questions. I have no idea a priori how large a
direct sensory basis or "kernel" a grounded TTT system requires
(although I do suspect that the kernel will be provisional, approximate,
and always undergoing revision whose consequences accordingly percolate
throughout the entire system). But I am sure that "English" won't do it
for you, because, until further notice, English is just systematically
interpretable gibberish, and it's the interpretations that we're trying
to ground!
Your phrase about "the need for grounding to guarantee meaning" also
worries me, because it sounds as if grounding has merely a confirmatory
function: "The meanings are already in the squiggles and squoggles, of
course; we just need the robotic evidence to convince the sceptics."
Well I think the meaning will be in the grounding, which is why I
believe most of the actual physical structures and processes involved
will be analog rather than computational.
The constraints you speak of are all syntactic. What they give you (if
they are set up properly) is the coherent semantic INTERPRETABILITY that
makes a symbol system a symbol system in the first place. The GROUNDING
of that interpration must come from elsewhere. Otherwise it's just the
self-confirmatory hermeneutic circle again.
I suppose that if computer science were just the study of hardwares for
implementing programs then you would have a point (at least about what
computer scientists are interested in). But isn't a lot of computer
science implementation-independent (software)? If someone writes a
program for factoring polynomials, I don't think he cares if it's
executed by a machine or an undergraduate. Usually such a program is
written at a lower level than the one at which an undergraduate would
want to work at, but the undergraduate COULD work at that lower level.
I think the programmer would have to agree that anyone or anything
following the syntactic steps his program specified would be
"executing" his program, even if he wanted to reserve "implementing" it
for the kind of mechanical implementation you are stressing.
I am not implying that designing mechanical devices that can
mechanically implement programs is not an extremely important
achievement; I just think the implementation-independence of the
programming level renders all these hardware-related matters moot or
irrelevant for present purposes. If I had to pick the two main
contributions of computer science, they would be (1) showing how much
you could accomplish with just syntax, and (2) building devices that
were governed mechanically by syntax; most of the action now is in (1)
precisely because it's independent of (2).
Let me try to put it another way: Prima facie, computer-hardware-science
is a branch of engineering; it has nothing to do with the mind. What
principle of hardware science could possibly underwrite the following
distinction: If a program is executed by a machine, it has a critical
property that it will lack if the very same program is executed by a
person. You keep stressing that this distinction is critical for what
counts as a true "implementation" of a program. So let's try to set
trivial semantics aside and speak merely of the program's being
"executed" rather than "implemented." What is there in COMPUTER SCIENCE
that implies that mechanical execution will have any relevant and
radically different properties from the human execution of the very
same program (on the very same I/O)?
Now I realize the case of mind-implementation is unique, so perhaps you
could give some suggestive examples of analogous radical differences
between mechanical and human implementations of the same programs in
other domains, just to set my intuitions.
Not at all. All that follows from my very willing concession is that
one can de-interpret any kind of a system at all, whether it is purely
symbolic or not. WHATEVER is going on inside a grounded TTT-scale robot
(you seem to be able to imagine only computation going on in there, but
I can think of plenty more), whether we know its interpretation or
not, those inner structures and processes (whatever they are) retain
their systematic relation to the objects, events and states of affairs
in the world that (unbeknownst to us, because de-interpreted) they are
interpretable as being about. Why? Because those goings-on inside the
head would be governed by the system's robotic capacity to
discriminate, categorize, manipulate and discourse (in gibberish, if we
don't happen to know the code) about the world TTT-indistinguishably
from the way we do. In other words, they would be grounded.
WE do, but, until further notice, computers don't -- or rather, their
capacity to do so (bidirectionally, as opposed to unidirectionally) is
on trial here. To get meaning from discourse (as we certainly do), the
meanings in our heads have to be grounded. Otherwise all that can be
gotten from discourse is syntax. This is why the TT alone is
inadequate: because it's all just symbols; nothing to ground the
meanings of meaningless squiggles in except still more, meaningless
squiggles.
I don't find Searle's response to the Robot Reply unconvincing, I
find the Robot Reply unconvincing. It merely amounted to pointing out to
Searle that people could do more than just write letters. So Searle
said, quite reasonably, fine, add on those extra things and I still
won't understand Chinese. He was right, because the objection was wrong.
It's not a matter of symbol crunching PLUS some add-on peripherals,
where the symbol-crunching is the real bearer of the meaning. That's
just as equivocal as symbol crunching alone.
No, my reply to Searle (which in Harnad 1989 I carefully dubbed the
"Robotic Functionalist Reply," to dissociate it from the Robot Reply)
explicitly changed the test from the TT to the TTT and accordingly
changed the mental property in question from "understanding Chinese" to
"seeing" in order to point out that even transduction is immune to
Searle's argument.
To put it in the briefest possible terms: Symbols alone will not suffice
to ground symbols, and language is just symbols (except in the heads of
grounded systems -- which neither books nor computers are).
You've changed the example a bit by having the child know how to count
(i.e., able to attach a name to an object, namely, a quantity); this
is beginning to leave behind the point, which was that we only wanted
the child to do syntax, slavishly and without understanding, the way a
computer does.
But, fair enough, if what you are talking about is comparing two
different algorithms for addition, one involving the manipulation of
numerals and the other the manipulation of pebbles (again on the
assumption that the child does not have any idea what all this means),
then I have no problem with this: Either way, the child doesn't
understand what he's doing.
If you have two I/O equivalent algorithms you have weak equivalence
(that's what the TT is based on). The stronger equivalence (I called it
Turing Equivalence, but you indicated [in Hayes et al 1992] that that
was the wrong term) requires two implementations of the same algorithm,
both equivalent state for state. The latter was the equivalence Searle was
considering, and even with this strong form of equivalence there's no
understanding.
Your point is not, I take it, about how one goes about TEACHING
arithmetic to a child, or about what a child might figure out from a
task like this -- for that's just as irrelevant as the question of
whether or not Searle might actually learn a few things about Chinese
in the Chinese room. All such considerations beg the question, just as
any verbal instruction to either the child or Searle (about anything
except the syntactic rules to be followed) would beg the question.
Stevan Harnad
Harnad, S. (1989) Minds, Machines and Searle. Journal of Theoretical
and Experimental Artificial Intelligence 1: 5-25.
Harnad, S. (1992) Connecting Object to Symbol in Modeling
Cognition. In: A. Clarke and R. Lutz (Eds) Connectionism in Context
Springer Verlag.
Hayes, P., Harnad, S., Perlis, D. & Block, N. (1992) Virtual Symposium
on the Virtual Mind. Minds and Machines (in press)
---------------------------------------
Date: Wed, 29 Apr 92 12:45:24 EST
From: Ross Buck
I have been aware of the symbol grounding discussion and discussion re
computation, but have admittedly not been keeping up in great detail,
and if this is too off-the-wall please ignore it. However, I have a
perspective that might be relevant. I have made a distinction between
special-purpose processing systems (SPPSs) structured by evolution and
general-purpose processing systems (GPPSs) stuctured during the course
of ontogeny. Perceptual systems are an example of the former and
classical conditioning, instrumental learning, and higher-order
cognitive processes examples of the latter. A Gibsonian view of
perceptual systems suggests that perception is DIRECT in that there are
evolved compatibilities between events and the experience of the
perceiver. The experience is not a symbol of the event, it is a SIGN,
which in my definition bears a natural relationship with its referent.
A sign so defined may be what you call a grounded symbol: I'm not sure.
I would argue that GPPSs are computers but SPPSs are not. SPPSs are a
result of evolution and are a defining characteristic of living systems.
Computers are not living systems, but living systems have incorporated
computers, in the form of GPPSs.
References:
Buck, R. (1985) Prime theory: A general view of motivation and emotion.
Psych. Review.
Buck, R. (1988) Human Motivation and Emotion. 2nd. Ed. New York: Wiley.
=========================================================================
From: Stevan Harnad
[The above contribution was returned to the author several weeks ago
with the following comment; the rest was subsequently added by the
author in response.]
Ross, the two kinds of systems you mention sound like they are worth
distinguishing, but you have not given the specifics of what goes on
inside them: Is it computation (symbol manipulation) or something else
(and if something else, then what)? So far you have sorted two kinds of
package, but what we are discussing is the contents. I will only be able
to post your contribution to the SG list as a whole if it takes up the
substance of the discussion. (The Peircian terminology does not help
either, unless you specify a mechanism.) -- Stevan Harnad
=========================================================================
It is something else, namely reproduction. Whereas GPPSs inherently
are computers, SPPSs are designed to reproduce events unaltered,
for all practical purposes. The analogy would be with reproduction
devices like audio/video communication systems, rather than
computers. The old Shannon-Weaver analysis comes to mind. More
specifically:
Living things differ from computers in that the former have an inherent
purpose: the maintenance of the DNA molecule. This involves maintaining
the temperature, energy, and chemical (TEC) balances necessary for the
DNA molecule to exist and replicate. The success of living things in
this regard is demonstrated by the fact that the TEC balances existing
within our bodies are roughly comparable to those of the primordial
seas in which the DNA molecule first spontaneously generated. To
maintain these balances early life forms evolved perceptual systems to
monitor both the external environment and the internal, bodily
environment for TEC resources and requirements; and response systems to
act accordingly: to approach TEC states that favor DNA existence and
replication and to avoid TEC states that endanger them. In the process,
there evolved basic systems of metabolism (including the oxygen-burning
metabolism of animals and the photosynthesis employed by plants which
shaped the atmosphere of the early earth), complex eukaryotic cells,
sexual reproduction, multicelled creatures, social organization, etc.
By far the largest span of time of life on the earth has involved the
cobbling together via evolution of these basic systems. Each system, in
a teleonomic (as opposed to teleological) process, evolved to serve a
specific function: for this reason I prefer to call them
"special-purpose processing systems" (SPPSs).
One might argue that these systems involve computation: in a sense any
process that involves information transfer might be defined as
involving computation. I suggest that the term computation be reserved
for systems involving information processing, and that systems designed
around information transfer are fundamentally distinct: they are
recording devices rather than computing devices. Recording systems have
inherent "meaning:" the nature of the event being recorded. From the
point of view of the DNA molecule it is critical that the information
received by the perceptual systems regarding TEC events is accurate:
that it matches in critical respects the actual TEC events. If this is
not the case, that DNA molecule is unlikely to survive. The result is
the evolution of a perceptual system along the lines of Gibsonian
theory: compatibilities between the critical TEC events and the
recording qualities of the system evolve naturally and inevitably, so
that the organism gains veridical access to certain events in both the
external terrestrial environment (including the activities of other
organisms) and the internal bodily environment (the latter in the form
of motivational-emotional states: in complex creatures who know THAT
they have these states they constitute affects, or desires and
feelings).
I term the elements by which information is transferred in SPPSs
"signs" rather than symbols. This is admittedly a Pierceian term, but I
do not wish to imply a Pierceian definition. Whereas symbols have
arbitrary relationships with their referents, the relationship between
the sign and the referent is natural. The living organism has access to
important TEC events via signs of those events incorporated in the
perceptual system: the photon excites a rod in the retina, which in
turn excites a sensory neuron in the optic nerve, and so on to the
visual cortex. Even though information is altered in form, the system
is designed so that the meaning of the information--its relationship to
the TEC events--is maintained: the sign of the event is passed up the
line altered in form but not meaning. (Is this what you mean by
a grounded symbol system, Stevan?)
The evolution of SPPSs took a long time: roughly three billion of the
3.8 billion year old story of life on earth. In the process, the
environment of the earth was transformed: the oxygen atmosphere and
ozone layer for example are products of life. Very early on, however,
it became useful for creatures to process information as well as merely
receive it: to associate one event with another, as in Pavlovian
conditioning; to approach events associated with beneficial TEC
outcomes (i.e., positive incentives) and avoid negative events, etc.
This requires generalpurpose processing systems (GPPSs) that are
structured by experience during the course of ontogeny: computing
systems. In human beings, the range and power of such systems has been
greatly increased by language.
Thus living things are not computers, but they have come to employ
computing devices in adapting to the terrestrial environment. But the
fundamental teleonomic goal of living systems--the meaning of life, as
it were--is to maintain the TEC balances necessary for the existence of
the DNA molecule. Ironically, the activities of human beings made
possible by the power of the GPPSs and language have destroyed these
balances beyond redemption for many species, and placed in jeopardy the
future of life on the earth.
Ross Buck
--------------------------------------------------------------
From: Kentridge
[Here is] something for the computation discussion perhaps (I have
missed a bit after being away - I hope I'm not retreading old ground or
too completely off the mark for the current state of the discussion).
Dynamic properties of computational systems.
The discussion of the nature of computation has reached the issue of
symbol interpretability just as previous discussions of Searle's
Chinese Room problem did. While I would not deny the importance of
issues of symbol interpretation when considering adaptive intelligence
I think one of the most interesting questions raised by Searle was
"what is special about wetware?". I wish to consider an allied question
"what is special about physical systems which compute?". In order to
understand computation and intelligence I think we need to address both
symbolic and physical issues in parallel. Perhaps some consideration of
the physics of computation might resolve some of the paradoxes that the
current symbolic discussion is producing.
There are two basic classes of dynamic behaviour in physical systems -
attraction to equilibrium and attraction to chaos. I will consider the
effects of introducing a signal which contains some information on
which we wish the system to perform computation for both classes.
When we perturb an equilibrium by introducing a signal into it its
response is to settle back into one of a finite number of stable
equilibrium states. The transition from initial state via the perturbed
state to a resultant, possible new, equilibrium state is entirely
deterministic and predictable. Once the final state has been reached,
however, the precise nature of the perturbing signal is lost.
Stable states of the system do not preserve any information on the
history of the signals which drove them there. Such systems can only
perform trivial computation because of their limited memory - we can
conceive of them as Turing machines with very very short tapes.
In chaotic systems we fare no better, but for opposite reasons. In a
chaotic system each different perturbation introduced to a system in a
given starting state will produce a unique resulting behaviour (even if
two perturbations push the system onto the same chaotic attractor the
resulting orbits will never meet); the system has infinite memory. The
problem, however, is that the transition between initial, perurbed and
resultant states in unpredictable. The chaotic system is like a Turing
machine with a very unreliable automaton.
One characteristic of systems that do computation then is that they are
neither chaotic nor equilibrium systems, they can, however, be at
the boundary or phase transition between these two regimes. In such
systems distinct effects of perturbations can last arbitrarily long
(but not infinite) times and transitions between states are at least
probabilistically predictable. The notion that computation only occurs
at phase transitions has received experimental support from studies of
cellular automata (e.g. Packard, 1987) and theoretical support from
analysis of the informational properties of the dynamics of phase
transitions in terms of the relationship between complexity and entropy
(Crutchfield and Young, 1991). Analysis of the dynamics of simulations
of physiologically plausible models of cerebral cortex suggests that
cortex may be well suited to being maintained in a critical dynamic
state between equilibrium and chaos (Kentridge, 1991) in which
computation can take place.
There are a few conclusions I would like to draw from this. First, in
this scheme of things computation per se is defined solely in terms of
the relationship between the complexity (the number of types of
structural regularites which are needed to describe a set of data) and
the entropy of data produced by a system. Processes which produce a
high ratio of complexity to entropy are ones which are capable of
computation. Second, as a consequence of this, everything is not a
computer doing computation. Third, computation is only interpretable in
terms of the the regularities that are used in the definition of
complexity - if there is a correspondence between those regularities
and the rest of the world then we may recognise the computation as
being useful.
I hope this is of some help to someone! It seems to me at least that a
physical definition of computation allows us to recognise systems as
performing computation even if we can't interpret computation. It also
emphasizes that there is an important relationship between the hardware
on which computation occurs and the nature of interpretable
computation.
Caveat: I'm really a physiological psychologist so reference to the
following sources is recommended (well the first two at least!).
References.
Packard, N.H. (1987) Adaptation towards the edge of chaos. In J.A.S.
Kelso, A.J. Mandell and M.F. Schlesinger (Eds.) Dynamic patterns in
complex systems. Singapore: World Scientific.
Crutchfield, J.P. and Young, K. (1991) Computation at the onset of
chaos. In W.H. Zurek (Ed.) Complexity, entropy and the physics of
information. (Proceeding of the Santa Fe Institute Studies in the
Sciences of Complexity Volume 8.) Redwood City, CA.: Addison-Wesley.
Kentridge, R.W. (1991) Weak chaos and self-organisation in
simple cortical network models. Eur. J. Neurosci. S4 73.
Robert Kentridge
--------------------------
Date: Sun, 10 May 92 19:29:44 EDT
From: "Stevan Harnad"
Date: Fri, 8 May 92 17:58:50 PDT
From: Dr Michael G Dyer
Here's another thought experiment for all. Imagine a continuum C:
At one end is a physical brain B, capable of passing the Turing Test
(or have it pass the TTT by symbols on parts of its tape controlling a
robot, whichever you want).
At the other end of the continuum C is a Turing Tape T that is SO LONG
and has so many "squiggles" that it models B at the molecular level.
That is, for every mRNA twist/fold and protein etc. produced, there is
a corresponding (huge) set of squiggles that encode their state.
Transformations of squiggles etc. encode the molecular dynamics (and
thus also the neural dynamics).
Notice that I've tried to remove the granularity issue (i.e. "big"
symbols of traditional AI versus smaller distributed connectionist
"subsymbols") by picking an extremely low (i.e. molecular) level of
granularity.
Both T and B have identical behavior (wrt any TT or TTT scenarios you
want to devise) so the behavior is also NOT the issue -- both T and B
*act* intentional.
The strong AI people would (I assume) claim that both T and B have
"intelligence", "consciousness" "intentionality", etc.
Searle (I assume) would claim that B has consciousness/intentionality
but T does NOT.
Harnad (I assume) would claim that both T and B have consciousness only
when controling the robot (i.e. TTT case) and both do NOT have it when
"disembodied" (i.e. only passing the TT).
First, let's deal with Searle vs Strong AI. We do this by slowly
moving along this continuum (of models), from B-to-T (or T-to-B). To
move from B-to-T we replace segments (either scattered or contiguous, I
don't think it matters) of B brain tissue with smaller Turing Machines
Ti where each Ti performs some equivalent function performed by some
subpart of B.
To move from T-to-B we replace bunches of squiggles on the tape with
real cells, (or subcells, or cell assemblies, etc.).
The continuum might be viewed better as some kind of lattice, with the
models in the middle being hybrid brains with different mixtures of
cellular subsystems vs Turing Machines. For example, one hybrid is
where EVERY OTHER B-neuron (with its dendrites/axons) is modeled by a
separate Turing Machine, so the hybrid is a scattered mix of 50% real
neurons and 50% Turing Machine simulations, all linked up.
(Turing Machines-to-cellular INTERFACES are the trickiest part. There
are probably many ways of doing this. In this thought experiment I
think it's ok to scale Turing Machines to a very small size (i.e. super
nontechnology) so they can be more easily interfaced with dendrites
(and operate even within a cell). But the main requirement is that
a real neuron or protein's dynamics cause a corresponding
representation to be placed on a Turing tape at the right time.)
In any case, ALL points on continuum C maintain the behavioral
correspondence so that the behavior (for passing the TT or TTT) is the
same.
Now, it seems to me that Searle is going to have a hard time
determining when "consciousness" or "intentionality" appears, as one
moves from T toward B. It's clear that he will be happy with B and
unhappy with T but what about all of the possibilities inbetween?
Now let's create a new continuum C' along the "sensory embodiment"
dimension by extending C along this dimension. To do this we start out
with both B and T controlling a complete robot, with hands/legs/mouth,
eyes/ears/skin-sensors.
As we move along C', we slowly remove these sensors/effectors. E.g., if
there are 1million sensors/effectors, we cut them off, bit by bit, and
leave only nerve "stumps" (in the B case) or in the T case, we cut the
wires that allows a set of "squiggles" on a Turing tape to control the
robot (or those wires that take sensory input and place "squiggles" on
some "sensory" portion of the tape). We do this also for all the hybrid
brain/machines in the middle of the continuum C. So we have now an
"intentionality plane IP" of models CxC'. How you assign
"intentionality/consciousness" labels to points/regions on this plane
will then say something about your intuitions concerning
consciousness.
Strong AI appears to be quite "liberal" -- i.e. assigning
"intentionality" to the entire plane (since ALL points on IP
demonstrate the same intentional BEHAVIOR).
Does Searle only assign intentionality to point B or does he accept
intentionality at other points/regions ???
I'm not sure where Harnad assigns intentionality along C'. Will just
ONE photo-sensitive cell be enough for "embodiment"? How many
sensors/effectors are needed along continuum C' before
intentionality/consciousness appears/disappears for him? (Stevan,
perhaps you can enlighten us all on this.)
Both Searle and Harnad just can't accept that a TM (all those "mindless
squiggles") could have a MIND. But to anyone accepting MIND as the
ORGANIZATION of physical systems, our Turing Machine T has all the
organization needed (with an admittedly tight bottleneck of just enough
causal apparatus to have this organization direct the dynamics of the
Turing tape read/write/move actions).
But is it any more of an amazing fact that "all those meaningless
squiggles" create a Mind than the (equally) amazing fact that "all
those mindless neurons" create a Mind? We're simply USED to seeing
brains show "mindfulness". We are not (yet) used to Turing-class
machines showing much mindfulness.
Michael G. Dyer
---------------
From: Stevan Harnad
Fortunately, my reply to Mike can be very short: Real neurons don't
just implement computations, and symbolically simulated neurons are not
neurons. Set up a continuum from a real furnace heating (or a real
plane flying, or a real planetary system, moving) to a computational
simulation of the same and tell me where the real heating (flying,
moving) starts/stops. It's at the same point (namely, the stepping-off
point from the analog world to its symbolic simulation) that a real
TTT-passing robot (with its real robot-brain) and its computationally
simulated counterpart part paths insofar as really having a mind is
concerned.
Stevan Harnad
----------------
Date: Fri, 8 May 92 10:18:00 HST
From: Herbert Roitblat
I printed our various communications on this issue and it came to 125
pages. I think that we might want to summarize contributions rather
than simply clean up what has been said.
I will contribute.
Herb Roitblat
-----------------------------------------------------------------
From: Brian C Smith
I've enjoyed this discussion, but would very strongly like to argue
against publishing it. Instead, I'd like to support John Haugeland's
(implicit) suggestion.
For my money, there are two reasons against publishing.
First, I'm not convinced it would be interesting enough, per page. It
is one thing to be part of such discussions -- or even to read them, a
little bit each day, as they unfold. It has something of the structure
of a conversation. It doesn't hurt that many of us know each other.
Sitting down with the entire opus, as an outsider, is quite another
thing. Last night I reread about a month's worth in paper form,
imagining I were holding a journal in my hand -- and it didn't take. It
just doesn't read like professional prose. This is not a criticism of
anyone. It's just that the genre of e-mail discussion and the genre of
referreed journal are different. Excellent one needn't make excellent
the other.
More seriously, there has been no attempt to keep the format objective.
Stevan has thoroughly mixed moderating and participating, making the
result a public correspondence of his, more than a balanced group
discussion. It is not just that he has contributed the most (50% of the
total, four times as much as the nearest competitor [1]). It is more
subtle things -- such as that, for example, a number of contributions
[e.g. Sereno, Moody, Dambrosio, some of Hayes] only appeared embedded
within his replies; others [e.g. Myers] only after being preceded with
quite normative introduction. You don't need fancy analysis to see how
much these things can skew the overall orientation.
Since it is Stevan's list, he is free to do this, and we are free to
participate as we chose (though I must say that these things have
limited my own participation quite a lot). I assume this is all as he
intended. But it would be a very different thing to publish the result
as in any sense a general discussion. Certainly to pose it as a general
discussison that Stevan has merely would be quite a misrepresentation.
On the other hand, the topic is clearly of wider interest. So instead I
suggest that we adopt John Haugeland's suggestion -- and that each of us
write a 3000-5000 word brief or position paper on the question, and
these be collected together and published. We can draw intellectually
on the discussion to date -- but it would also give us a chance to
distill what we've learned into punchier, more targeted form.
Brian
P.S. The prospect of publishing e-mail discussions clearly raises all
kinds of complex issues -- about genre, the role of editing, applicable
standards and authority, models of public debate, etc. I've just been
focusing on one: of maintaining appropriate detachment between the roles
of moderating and passionate participation. But many others deserve
thinking through as well.
[1] 1057 lines out of 2109 counted between April 17 and May 8; Pay Hayes
was second with 277.
-----------------------
From: Stevan Harnad
Well, I guess this calls for some sort of a reply from me:
(a) This electronic symposium began informally, with some cross-posting
to a small group of individuals (about a dozen); only later did I begin
posting it to the entire Symbol Grounding Discussion List (several
hundred, which I have moderated for four years), with pointers to the
earlier discussion, electronically retrievable by anonymous ftp.
(b) The expressions of interest in publication (one from James Fetzer,
editor of Minds and Machines, about the possibility of publishing some
version of the symposium as a special issue of his journal, and one
from Laurence Press, consulting editor for Van Nostrand Rheinhold,
expressing interest in publishing it as a book) came still later.
(c) No one had initially expected the symposium to reach the scope it
did, nor to draw in as many participants as it has so far. In the very
beginning I cross-posted the texts annotated with my comments, but once
it became clear that the scale of participation was much larger than
anticipated, I switched to posting all texts directly, with comments
(my own and others') following separately, skywriting-style.
(d) In the published version (if there is to be one), all texts,
including the earliest ones, would appear as wholes, with comments (and
quotes) following separately. This is how we did it in editing and
formatting the shorter "Virtual Mind" Symposium (Hayes et al. 1992)
under similar circumstances.
(e) In moderating the symposium, I have posted all contributions I
received in toto (with two exceptions, one that I rejected as
irrelevant to the discussion, and one [from Ross Buck] that I first
returned for some clarification; the revised version was subsequently
posted).
(f) Mike Dyer (sic), with whom I have had long and stimulating
exchanges in past years on the Symbol Grounding Discussion Group,
entered this discussion of "What is Computation?" on our old theme,
which concerns whether a computer can have a mind, rather than what a
computer is. Since our respective views on this theme, which I think we
have rather run into the ground, had already appeared in print (Dyer
1990, Harnad 1990), I hoped to head a re-enactment off of them at the
pass. As it happens, both themes have now taken on a life of their own
in this discussion.
(g) It is embarassing that I have contributed more to the symposium
than others have (and the proportions could certainly be adjusted if it
were published) but I must point out that this imbalance is not because
others were not able -- indeed encouraged -- to contribute. Some (like
Pat Hayes and Drew McDermott) availed themselves of the opportunity
fully, others did not.
(h) There is no necessity at all that I, as the moderator of the
symposium, be the editor of the published version, indeed I
would be more than happy to cede this role to someone else.
(i) Regarding refereeing: James Fetzer indicated clearly that if it
appeared in his journal, the published version would first be subject
to peer review.
(j) I do wish to register disagreement with Brian Smith on one point,
however: I would strongly favor publishing it as a symposium, one that
preserves as much as possible of the real-time interactive flavor of
this remarkable new medium of communication ("scholarly skywriting").
In reading over the unedited transcript as an "outsider," as Brian did,
it is unavoidable that one's evaluation is influenced by the fact that
elements of the back-and-forth discussion are not all that congenial to
one's own point of view. The remedy for this is not to turn it into a
series of noninteractive position papers, but to launch into more
interactive participation. Afterward, editing and peer review can
take care of making the symposium into a balanced, integrated,
publishable final draft.
(k) Since I posted the two possibilities of publication, we have heard
affirmatively about publication from (1) Dave Chalmers and (2) Eric
Dietrich. I (3) favor publication too. We have now heard from (4) Herb
Roitblat and (5) Brian Smith (whose view is seconded below by (X) John
Haugeland, who has, however, not yet contributed to the symposium). How
do the other 19 of the 24 who have so far contributed to the symposium
feel about publication, and whether it should be in the form of an
interactive symposium or a series of position papers?
(6) Frank Boyle
(7) Ross Buck
(8) John M Carroll
(9) Jeff Dalton
(10) Bruce Dambrosio
(11) Martin Davis
(12) Michael G Dyer
(13) Ronald L Chrisley
(14) Gary Hatfield
(15) Pat Hayes
(16) Robert Kentridge
(17) Joe Lammens
(18) Oded Maler
(19) Drew McDermott
(20) Todd Moody
(21) John Searle
(22) Marty Sereno
(23) Tim Smithers
(24) Richard Yee
Stevan Harnad
Dyer, M. G. (1990) Intentionality and Computationalism: Minds,
Machines, Searle and Harnad. Journal of Experimental and Theoretical
Artificial Intelligence, Vol. 2, No. 4.
Hayes, P., Harnad, S., Perlis, D. & Block, N. (1992) Virtual Symposium
on the Virtual Mind. Minds and Machines [in press; published version
of electronic "skywriting" symposium]
Harnad, S. (1990) Lost in the hermeneutic hall of mirrors. Invited
Commentary on: Michael Dyer: Minds, Machines, Searle and Harnad.
Journal of Experimental and Theoretical Artificial Intelligence
2: 321 - 327.
------------------------------------------------
Date: Sat, 9 May 92 22:06:44 -0400
From: "John C. Haugeland"
Brian, Pat, and Steve:
As usual, Brian knows me better than I know myself. I didn't realize that I
was making an implicit proposal, or even that I wanted to. But now that I
think about it, I do -- just as Brian said, and for just those reasons.
Modest position papers, informed by the discussion so far, but not directly
drawn from it, seem like much better candidates for publishable prose.
John Haugeland
------------------------------------------------
------------------------------------------------
Date: Sat, 09 May 92 17:51:40 ADT
From: Lev Goldfarb
Oded Maler (Oded.Maler@irisa.fr) wrote:
om> Now to the question of what is a computation. My current view is that
om> computations are idealized abstract objects that are useful in
om> describing the structure and the behavior of certain systems by
om> focusing on the "informational" aspects of their dynamics rather on
om> the "materialisic/energetic" aspects.
Let me try to attempt a more formal definition:
Computation is a finite or infinite sequence of transformations
performed on "symbolic" objects.
One can add that an "interesting" computation captures in some (which?)
form the dynamics of some meaningful (to whom?) processes. It appears
that the question marks cannot be removed without the participation
of some intelligent (understood very broadly) entity that can interpret
some sequences of transformations as meaningful.
Lev Goldfarb
------------------------------------
Date: Sun, 10 May 92 15:33:41 -0400
From: davism@turing.cs.nyu.edu (Martin Davis)
Subject: TTT ?
Stevan Harnad (harnad@clarity.princeton.edu) wrote:
Not so surprised. I've been a fan of Pat Churchland (I've never
met Paul) and regard her work as being remarkably sensible. (By
the way, I've held my present views for many years; I first
formulated them explicitly in discussions with Hilary Putnam
during the years we worked together ~1958.)
I carefully said "IF AND WHEN the brain function is reasonably
well understood (and of course that includes understanding
how consciousness works)". Of course, to believe that such
understanding is likely to come and that with it will come
understanding of "mind" and in just what sense "someone is home"
up there, is to have a definite stance (I would once have said
"materialistic") about such matters. But what question am I
begging, or coming "perilously close" to so doing?
Me too! That's why I'm such a fan of Pat Churchland. It's her
line of thought that I believe most likely to move us in that
direction.
Yes I know. But I don't believe any of it. Here again (for
whatever it's worth) is what I think:
1. There is clearly a lot of symbol manipulation being carried
out in living (wet, messy, analogue) creatures, e.g. DNA. So
there is certainly no a priori reason to doubt that it goes on in
the brain.
2. There is certainly a mystery about what it means that we
possess "understanding," that we associate "meanings" with
symbols. But I have seen no reason to believe (and please don't
trot out some variant of the Chinese room) that meaning cannot
be the result of symbolic manipulation, of operations on
squiggles and squoggles. From a very theoretical and abstract
point of view, one could even call on Tarski's demonstration that
semantics can in fact be reduced to syntax.
3. Finally, I don't really believe that your TTT robot shopping
at K-Mart would be more convincing than, say, a TT dialogue on the
immortality of the soul. It is certainly attainable (or at least
pretty close to being so) with today's technology, to have a chess
playing computer provided with a video camera and arm and
reacting to the actual moves of the physical pieces on a real
physical chessboard with appropriate actions of the arm. Would
anyone who argues that the computer knows nothing of chess and is
"merely" manipulating squiggles, suddenly give up on the point on
being confronted by such a demonstration?
Martin Davis
-----------------------------------
From: "Stevan Harnad"
CUTTING UNDERDETERMINATION DOWN TO SIZE
davism@turing.cs.nyu.edu (Martin Davis) wrote:
md> I carefully said "IF AND WHEN the brain function is reasonably
md> well understood (and of course that includes understanding
md> how consciousness works)". Of course, to believe that such
md> understanding is likely to come and that with it will come
md> understanding of "mind" and in just what sense "someone is home"
md> up there, is to have a definite stance (I would once have said
md> "materialistic") about such matters. But what question am I
md> begging, or coming "perilously close" to so doing?
The question-begging is the unargued adoption of the assumption that to
understand brain function fully (in the sense that we can also
understand liver function fully) is to understand consciousness. Some
philosophers (and not necessarily non-materialistic ones) specialize in
showing how/why consciousness is interestingly different from other
empirical phenomena, and hence that this assumption may be false.
But let's leave this metaphysical area on which I have no strong views
one way or the other, and on which none of the empirical and logical
issues under discussion here depend one way or the other.
md> 1. There is clearly a lot of symbol manipulation being carried
md> out in living (wet, messy, analogue) creatures, e.g. DNA. So
md> there is certainly no a priori reason to doubt that it goes on in
md> the brain.
That cells do any pure syntax is not at all clear to me. The genetic
"code" is certainly DESCRIBABLE as symbol-manipulation, but biochemists
and embryologists keep reminding us that cellular processes are hardly
just formal. The "symbols" are made out of real proteins, and their
interactions are not simply "compositional," in the formal sense, but
chemical and morphological. At best, cells do highly DEDICATED
computation, in which the nonsyntactic constraints are at least as
critical as the formal syntactic ones (see Ross Buck's contribution
to this discussion). Now the interaction of analog and formal
constraints in dedicated symbol systems may well yield some
clues about how to ground symbols, but we do not yet know what
those clues are.
md> 2. There is certainly a mystery about what it means that we
md> possess "understanding," that we associate "meanings" with
md> symbols. But I have seen no reason to believe (and please don't
md> trot out some variant of the Chinese room) that meaning cannot
md> be the result of symbolic manipulation, of operations on
md> squiggles and squoggles. From a very theoretical and abstract
md> point of view, one could even call on Tarski's demonstration that
md> semantics can in fact be reduced to syntax.
Unfortunately, this is not an argument; one cannot answer Searle's
objections by simply refusing to countenance them! And it's easy
enough to reduce semantics to syntax, the trick is going the other way
(without cheating by simply projecting the semantics, as we do on a book
we are reading, which clearly has no semantics of its own).
md> 3. Finally, I don't really believe that your TTT robot shopping
md> at K-Mart would be more convincing than, say, a TT dialogue on the
md> immortality of the soul. It is certainly attainable (or at least
md> pretty close to being so) with today's technology, to have a chess
md> playing computer provided with a video camera and arm and
md> reacting to the actual moves of the physical pieces on a real
md> physical chessboard with appropriate actions of the arm. Would
md> anyone who argues that the computer knows nothing of chess and is
md> "merely" manipulating squiggles, suddenly give up on the point on
md> being confronted by such a demonstration?
The issue is not whether one would be more CONVINCING than the other. A
good oracle might be the most convincing of all, but we don't simply
want to be seduced by compelling interpretations (hermeneutics), do we?
The TTT narrows the empirical degrees of freedom better than the TT
because the claim to objectivity of all forms of Turing (performance)
Testing rests on indistinguishability in performance capacities, and we
all happen to have more performance capacities than the ones a pen-pal
samples. Indeed (since all of this is hypothetical anyway), it may well be
the case (and in fact I hypothesize that it is, and give reasons why in
my writing on categorical perception) that for a system to be able to
pass the TT in the first place, it would have to draw on its capacity
to pass the TTT anyway -- it would have to be GROUNDED in it, in other
words. (A mere TT-passer couldn't even tell whether it had a pencil in
it's pocket -- so how are we to imagine that it could know what a
pencil was in the first place?)
Here's an analogy: It is clear, I take it, that a pre-Newtonian model
that explained the interactions of the balls in two billiard games would
be preferable to one that could only explain the interactions in one.
Moreover, one could probably go on building ad hoc models for ever if
all they needed to do was explain a finite number of billiard games. The
laws of mechanics must explain ALL billiard games. By the same token, in
the particular branch of reverse bioengineering where mind-modeling is
situated (where there are no "laws" to be discovered, but just
bioengineering principles), the model that explains ALL of our
performance capacity (the TTT) is surely more convincing than the one
that only explains some of it (the TT).
The very same is true of "toy" robots like the chess player you describe
above. Toy models are as ad hoc and arbitrary as pre-Newtonian models of
particular billiard games. A chess-playing computer demonstrates
nothing, but I'm as ready to be convinced by a TTT-indistinguishable
system as I am by you (and for the same reasons). You will reply
that we only know one another as pen-pals, but I must remind you that my
gullibility is not the issue. You could indeed say what is in your
pocket in my presence, and countless other things. And if you instead
turned out to be just like the Sparc I'm using to send you this message,
I would be prepared to revise my judgment about whether you really had a
mind, or really understood what I was saying, no matter how convincingly
you traded symbols with me.
Stevan Harnad
----------------------------------
Date: Sun, 10 May 92 18:54:35 PDT
From: sereno@cogsci.UCSD.EDU (Marty Sereno)
hi stevan
(1) I would like to contribute to a symposium, if it was reviewed, and
could count as a reviewed publication (I'm getting close to tenure).
(2) I like the idea of an interactive discussion, but I agree that in
its present form it is not fun to read straight through. Maybe there
could be a set of position papers and then everyone has a (shorter)
reply, in which they respond to any of the position papers that engage
them. That way, everyone can take a crack at anyone they'd like, but
there is more discipline for the benefit of the reader.
Re: not mailing out original posts. When you mailed out your response
to my post, you didn't precede it with my post but instead mailed out
two copies of your comments (which explains Brian Smith's comment)
Marty Sereno
----------------------
Date: Mon, 11 May 92 00:55:57 -0400
From: mclennan@cs.utk.edu (Bruce McLennan)
Stevan,
There are a couple of points that haven't been raised in the discussion
so far. First, I think you are pinning too much on the difficulty of
finding nonstandard interpretations of formal systems. The equivalent
in formal logic of your criterion is a formal system being
"categorical," which means that all its models (interpretations for
which the axioms and inference rules are true) are isomorphic and,
hence, essentially the same. Yet before 1920 Loewenheim and Skolem
showed that any consistent formal system with a countable number of
formulas has a countable model. In particular, there is a countable
model for formal axiomatic set theory, which is a remarkable result,
since in set theory one can prove that the real numbers and many other
sets are uncountable. Thus, no formal system can uniquely characterize
the reals, even insofar as their cardinality; this is the
Loewenheim-Skolem Paradox.
A corollary of the L-S Theorem shows that any consistent formal system
(with a countable number of formulas) has models of every transfinite
cardinality. This includes the Peano axioms, which thus do not uniquely
characterize the integers in even so fundamental a way as their
cardinality. Further, it is a fairly routine procedure to construct the
nonstandard interpretations by which these results are proved.
Nonstandard interpretations are also routinely constructed for
nontheoretical purposes. For example, computer scientists design
nonstandard interpreters for programming languages. So called "pseudo-
interpretation" or "interpretation on a nonstandard domain" is used for
purposes such as type checking, optimization and code generation. For
example, if such a pseudo-interpreter sees "X + Y", instead of adding
numbers X and Y, it may instead make sure the types X and Y are
compatible with addition and return the type of the sum; in effect its
nonstandard addition rules might be
integer + integer = integer,
real + real = real,
real + integer = real, etc.
You may underestimate people's ability to come up with (sensible, even
useful) nonstandard interpretations; all it takes is a grasp of the
"algebra" generated by the formal system.
My second point is that the nature of computation can be illuminated by
considering analog computation, because analog computation does away
with discrete symbols, yet still has interpretable states obeying
dynamical laws. Notice also that analog computation can be formal in
exactly the same way as digital computation. An (abstract) analog
program is just a set of differential equations; it can be implemented
by a variety of physical devices, electronic, optical, fluidic,
mechanical, etc. Indeed, it is its independence of material embodiment
that is the basis for the "analogy" that gives analog computing its
name. (There is, however, no generally agreed upon notion of analog
computational universality, but that will come in time.)
Analog computation sheds some light on the issue of interpretability as
a criterion for computerhood. In analog computation we solve a problem,
defined by a given set of differential equations, by harnessing a
physical process obeying the same differential equations. In this
sense, a physical device is an analog computer to the extent that we
choose and intend to interpret its behavior as informing us about some
other system (real or imaginary) obeying the same formal rules. To take
an extreme example, we could use the planets as an analog computer, if
we needed to integrate the same functions that happen to define their
motion, and had no better way to do so. The peculiar thing about this
analog computer is not that it's special-purpose -- so are many other
analog and digital computers -- but that it's provided ready-made by
nature.
So where does this leave us with regard to computation? Let me
suggest: Computation is the instantiation of an abstract process in a
physical device to the end that we may exploit or better understand
that process. And what are computers? In addition to the things that
are explicitly marketed as computers, there are many things that may be
used as computers in an appropriate context of need and availability.
They are REAL computers because they REALLY instantiate the relevant
abstract process (N.B., not just any process) and so satisfy our need.
Such a pragmatic dependence on context should neither bother nor
surprise us. After all, in addition to the things marketed as tables,
many other things can be used as tables, but are none the less tables
for that use being unintended when they were made. Such "using as" is
not a wild card, however; some things cannot be used as tables, and
some things cannot be used to compute the trajectory of a missile.
Where does this leave the computation/cognition question? In brief:
Grounding vs. formality is still relevant. But I suggest we drop
computers and computing. Computers are tools and computing is a
practice, and so both are dependent on a background of human goals and
needs. Therefore a hypothesis such as "the mind is a computer" is not
amenable to scientific resolution . (How would one test scientifically
"the eye is a camera"? It's kind of like a camera, but does anyone use
it to take snapshots? You could if you had to!) In effect it's a
category mistake. A better strategy is to formulate the hypothesis in
terms of the notion of instantiated formal systems, which is more
susceptible to precise definition.
Bruce MacLennan
----------------------------------------------------------
From: Stevan Harnad
(1) My cryptographic criterion for computerhood was not based on the
uniqueness of the standard interpretation of a symbol system or the
inacessibility of nonstandard interpretations, given the standard
interpretation. It was based on the relative inaccessibility
(NP-Completeness?) of ANY interpretation at all, given just the symbols
themselves (which in and of themselves look just like random strings of
squiggles and squoggles).
(2) If all dynamical systems that instantiate differential equations
are computers, then everything is a computer (though, as you correctly
point out, everything may still not be EVERY computer, because of (1)).
Dubbing all the laws of physics computational ones is duly ecumenical,
but I am afraid that this loses just about all the special properties
of computation that made it attractive (to Pylyshyn (1984), for
example) as a candidate for capturing what it is that is special about
cognition and distinguishes it from from other physical processes.
(3) Searle's Chinese Room Argument and my Symbol Grounding Problem
apply only to discrete symbolic computation. Searle could not implement
analog computation (not even transduction) as he can symbolic
computation, so his Argument would be moot against analog computation.
A grounded TTT-passing robot (like a human being and even a brain) is
of course an analog system, describable by a set of differential
equations, but nothing of consequence hangs on this level of
generality (except possibly dualism).
Stevan Harnad
Pylyshyn, Z. (1984) Computation and Cognition.
Cambridge MA: MIT/Bradford
----------------------------------------------------
Date: Mon, 11 May 92 09:52:21 PDT
From: Dr Michael G Dyer
Harnad states:
Fortunately, my reply to Stevan can be nearly as short:
I grant that all simulation on a computer of the fire will NOT produce the
BEHAVIOR (i.e. results) of burning something up (e.g. ashes). However,
the "simulation" of the neurons WILL produce the BEHAVIOR of Mind
(i.e. the passing of the TT, and in the case of having the Turing Machine
control a robot, the passing of the TTT). In recognizing fire, we rely
on observing the behavior of fire (i.e. we notice the ashes produced,
we can take measurements of the heat with an infrared sensor, etc.).
In the case of recognizing Mind, we also observe behavior and
"take measurements" (e.g. does the entity plan? does it have humor?
can it talk about hypothetical situations? etc.)
Just like in quantum physics, what you can measure ultimately determines
what you can talk about, the same is true for Mind. I accept that
the simulated fire is not the same as the actual fire since the behavior
(effects) of fire inside and outside the computer are radically different.
One can burn wood and the other can't. But if the TT (or TTT)
is used as a measurement system for Mind, then we seem to get
the same measurements of Mind in either case.
Michael Dyer
--------------------------------------------------------------------
From: Stevan Harnad
Mike has, of course, herewith stated his commitment to "barefoot
verificationism": What there is is what you can measure, and what you
can't measure, isn't. There are problems with that position (conflating,
as it does, ontic and epistemic matters), but never mind; his argument
can be refuted even on verificationist grounds:
"Thinking," being unobservable, is equivocal, because we all know it
goes on, but it is verifiable only in the case of one's own thinking.
The robot (or person) passing the TTT is, like a furnace heating, an
analog system. That's the only way it can actually exhibit the
"behavior" in question (TTT-interactions with the world in one case,
reducing objects to ashes in the other). It is from this behavioral
indistinguishability that we justifiably conclude that the system as a
whole is really thinking or heating, respectively.
But Mike keeps thinking in terms of a pair of modules: The computer
module that does the real work (which he equates with the brain), and
the robot "peripherals" that it controls. I find this partition as
unlikely as the corresponding partition of the furnace into a computer
plus peripherals, but never mind. The candidate in both cases is the
WHOLE robot and the WHOLE furnace. They are what are doing the thinking
and the heating, respectively, in virtue of being behaviorally
indistinguishable from the real thing. But detach the peripherals, and
you lose the thinking in the one as surely as you lose the heating in
the other, because neither can pass the behavioral test any more. (This
is also why the symbols-only TT is equivocal, whereas the real-world
TTT is not.)
Trying to carry this whole thing inward by equating the brain (likewise
an analog system) with a computer simply leads to an infinite regress
on the very same argument (real transduction, real energy exchange, real
protein synthesis, etc. standing in for heating and thinking in each
case).
Stevan Harnad
--------------------------------------------------------------
Date: Mon, 11 May 92 15:49:16 PDT
From: Dr Michael G Dyer
Pat Hayes states:
I don't quite agree. I believe that the notion of computation is strong
enough to argue that the entire universe is a computation, but then we
have to be careful to distinguish levels of reality. This argument may
actually be useful: (a) in clarifying potential confusions in
discussions on whether or not (say) flying is a computation, and (b) in
providing a somewhat different perspective on the grounding and "other
minds" problems.
Here's a way to make flying, burning, (i.e. everything) a computation:
Imagine that our entire universe (with its reality Ri, produced by its
physics) happens to be simply a (say, holographic-style, 3-D) display
being monitored by some entity, Ei+1, residing in another reality Ri+1,
with its own physics. Entity Ei+1 has constructed something like a
computer, which operates by conforming to the physics of reality Ei+1.
To entity Ei+1, everything that happens in Ri (including our Ri-level
thought processes, fires, flying planes, etc.) is a simulation.
It is an interesting fact that there is NOTHING that we (Ei) can do (no
measurements that we can take) that will reveal to us whether or not
our reality Ri is a "real" reality or simply a vaste "simulation"
within some other reality Ri+1. (E.g. even direct messages from Ei+1 to
us will not constitute convincing evidence! Why not is left as an
exercise to the reader. :-)
The same happens to be true also for entity Ei+1 (who may actually be a
simulation from the point of view of some higher entity Ei+2 residing
within some reality Ri+2, where Ri+1 is just a simulation to Ei+2).
Likewise, IF we could ever create a sufficiently complex simulated
physics Ri-1 in one of our own computers, along with some artificially
intelligent scientist entity Ei-1 residing within that simulated
physics, THEN there is no experiment that Ei-1 could make to determine
whether or not Ri-1 is "real" or "simulated".
So, the answer to whether flying is a computation or not DEPENDS on
whether or not one is talking about a single level of reality or
multiple realities (where lower are simulations with respect to higher
realities). Since the default assumption in any discussion is to
assume a single reality, then flying is definitely NOT a computation
and a simulation of flying is not the same as actually flying. However,
this assumption is definitely not the case when we discuss the
differences between simulation and reality.
The grounding discussion also depends on which reality we are talking
about. Consider any AI/connectionist researcher who is running (say,
Genetic Algorithm) experiments with some simulated physics and has
created a (simple) reality Ri-1 along with one or more creatures (with
sensors, effectors). Those creatures can then be said to be "grounded"
IN THAT REALITY Ri-1.
I believe that, given a sufficiently complex set of sensors/effectors
and simulated brain structure, the simulated creature could obtain a
Mind in a simulated reality Ri-1 and would also be "grounded" (i.e. in
that reality) -- without needing Harnad's physical transducers, so MY
argument against the need for physical transducers requires keeping the
2 different realities straight (i.e. separate) and then comparing
behaviors within each.
The "other minds" problem is also clarified by keeping levels of
reality straight. The question here is: Can higher entity Ei+1
determine whether or not lower entities Ei have "minds" or not?
At some given level Ri, let us assume that an entity Ei passes the TTT
test (i.e. within reality Rk). So what does an entity Ei+1 (who can
observe and completely control the physics of Ri) think? If he is
Searle or Harnad, he thinks that the Ei entities do NOT have minds
(i.e. Searle rejects their minds because they are simulated; Harnad
rejects their minds because they are not grounded in Harnad's own
reality).
My own point of view is that any entities of sufficient complexity to
pass the TTT test WOULD have minds since (a) they are grounded in their
own reality, (b) since they pass the TTT in their own reality and
because (c) there is NO WAY TO TELL (for either THEM or for US) whether
or not a given reality R is actually someone else's simulation.
There ARE consequences to this position. For instance, the moral
consequences are that one could construct a simulation (e.g. of
neurons) that is so accurate and complex that one has to worry about
whether or not one is causing it the experience of pain.
Anyone who believes it's possible to have Mind reside within a brain in
a vat is basically agreeing with my position since in this thought
experiment the sensory information to the brain is being maintained (by
giant computers) so that that Mind thinks it is (say) standing by a
river. If the brain generates output to control its (non-existing)
effectors, then giant computers calculate how the sensory input must be
altered, so that this Mind thinks that it has moved within that
(simulated) environment. So one has basically created a reality (for
that brain/mind-in-a-vat) that is one level lower than our level of
reality. If we replace the real brain with an isomophic computer
simulation of that brain (pick your own level of granularity) then we
have to worry about both the real brain-in-vat and the computer
simulation experiencing pain.
If we imagine a continuum of realities ... Ri-1 Ri Ri+1 ... then
Strong AI components probably accept intentionality in ANY reality with
enough complexity to pass the Turing Test (or TTT if you need
grounding). If you're Searle or Harnad then you probably don't believe
that a system has intentionality if it's at a level of reality below
the one in which they (and the rest of us) reside.
So, what's a computation? It is the manipulation of representations by
transition functions within a reality Ri. These manipulations can create a
lower-level reality Ri-1 (normally called a "simulation"). With respect to a
higher reality, we (and our entire universe) is also a computation. If WE
are a simulation to an entity Ei+1 then does that entity think that WE feel
pain? If he is a Searlean or Harnadian then he does NOT. However, WE
think WE DO feel pain, even if we happen to be a simulation (from Ei+1's
point of view). If Ei+1 does NOT accept intentional behavior as the acid
test for intentionality, then there is probably nothing that we could ever do
to convince Ei+1 that we feel pain, no matter how much we behave as
though we do. Let's keep this in mind when our own simulated creatures
get smart enough to pass the TTT (in a simulated world) and behave as
if THEY have intentionality, feel pain, etc.
-- Michael Dyer
---------------------------------
Date: Mon, 11 May 92 22:50:47 PDT
From: Dr Michael G Dyer
Stevan Harnad states:
Sorry, Stevan, but your statements seem quite unsupportable to me!
There is every indication that "being at home" is no more a unitary
entity than is life or intelligence. Making consciousness be some kind
of "UNITARY beastie" treats it a lot like the now-abandoned idea of a
"life force".
In fact, there are AI systems that have self-reference (i.e. access
information about the system's own attributes). There are robotic
systems that have a form of real-time sensory updating (primitive
"awareness"). There are systems that even generate a stream of
"thoughts" and examine hypothetical situations and choose among
alternative pasts and generate imaginary futures (e.g. a PhD of a
student of mine a few years ago, published as a book: Mueller,
Daydreaming in Humans and Machines, Ablex Publ, 1990). There are
numerous learning systems, sensory systems, adaptive systems etc. All
of these systems exhibit isolated aspects of consciousness and there is
every reason to believe that someday a sufficient number of them will
be put together and we will be forced to treat is as though it is
conscious.
Then, on the human side there are humans with various agnosias,
short-term memory deficits, loss of all episodic memories, right or
left-side neglect, alzheimers syndromes, the scattered thought
processes of schizophrenia, blind sight, etc. etc. These patients
exhibit responses that also make one wonder (at least on many
occasions) if they are "at home".
So there is every indication that consciousness is a folk description
for behaviors arising from extremely complex interactions of a very
complex subsystems. There are probably a VERY great number of variant
forms of consciousness, most of them quite foreign to our own
introspective experiences of states of mind. Then we have to decide if
"anyone is at home" (and to what extent) in gorillas, in very young
children, in our pet dog, in a drugged-out person, etc. etc.
My own introspection indicates to me that I have numerous states of
mind and most of the time it appears that "nobody is home" (i.e. many
automatic, processes below the conscious level). E.g. there are times I
am "deep in thought" and it's not clear to me that I was even aware of
that fact (until after the fact). The only time for certain I'm aware
of my awareness is probably when I'm thinking exactly about my
awareness.
Michael Dyer
-------------------------------------------
From: Stevan Harnad
Michael G Dyer
md> There is every indication that "being at home" is no more a unitary
md> entity than is life or intelligence... In fact, there are AI systems
md> that have self-reference... on the human side there are humans with
md> various agnosias... My own introspection indicates to me that I have
md> numerous states of mind and most of the time it appears that "nobody is
md> home"...
Subjective experience, no matter how fragmented or delirious, either is
experienced or is not, that's an all-or-none matter, and that's what I
mean by someone's being home. Your AI symbol systems, be they ever so
interpretable AS IF they had someone home, no more have someone home
than symbolic fires, be they ever so interpretable as burning, burn.
The existence of the various disorders of consciousness in the real
human brain is no more a validation of symbol systems that are
interpretable as if they had disorders of consciousness than the
existence of normal consciousness (as they occur in your head) is a
validation of symbol systems that are interpretable as if they were
conscious simpliciter. Not in THIS reality, anyway. (For a
verificationist, you seem to be awfully profligate with realities, by
the way, but such seems to be the allure of the hermeneutic hall of
mirrors!)
Stevan Harnad
-----------------------------------------
Date: Wed, 13 May 92 18:45:50 EDT
From: "Stevan Harnad"
Below are responses about the question of publishing the "What is
Computation" Symposium from 8 more contributors out of what is now a
total of 25 contributors. Of the 14 votes cast so far:
Publication:
For: 13 // Against: 1
Interactive Symposium (IS) vs. Position Papers (PP):
Either or Combination: 8 - Prefer IS: 3 - Prefer PP: 2
Not yet heard from (11):
(15) Ross Buck
(16) John Carroll
(17) Bruce Dambrosio
(18) Ronald Chrisley
(19) Gary Hatfield
(20) Pat Hayes
(21) Joe Lammens
(22) Bruce McLennan
(23) Todd Moody
(24) John Searle
(25) Tim Smithers
----------------------------------------------------
(7) Date: Sun, 10
May 92 22:10:33 -0400 From: davism@turing.cs.nyu.edu (Martin Davis)
I don't mind my very brief contributions appearing, but I can't really
undertake producing an article. I have no relevant opinion on the
more global issue. Martin Davis
----------------------------------------------------
(8)
Date: Mon, 11 May 1992 10:01:46 +0200
From: Oded.Maler@irisa.fr (Oded Maler)
1) I wish to contribute.
2) About the rest I don't have a strong opinion. My contribution so
far was very marginal, and a position paper with deadline can be
a good motivation.
This way or another, this is a very interesting experiment in
scientific social dynamics. Best regards --Oded Maler
----------------------------------------------------
(9)
From: Robert Kentridge
I'd like to contribute to a published version of the "what is
computation" discussion. I'm less sure what its precise form should be.
I agree with you that the interactive nature of the discussion is what
has made it particularly interesting, however, it has also lead to
(inevitable) repetition. I suppose some smart editing is called for,
perhaps together with some re-writing by contributors?
So:
1) (publish) yes
2a) (publish interactive symposium) yes
----------------------------------------------------
(10)
Date: Mon, 11 May 92 12:58:36 -0400
From: yee@envy.cs.umass.edu (Richard Yee)
(1) Yes, I am interested in contributing to a publication. (I am in the
process of formulating responses to your comments).
(2) With regard to format, I find both the arguments for (2a)
[interactive symposium] and (2b) [separate position papers] well-taken.
That is, I very much like the interactive nature of the exchanges, but
I also think that the discussion should be "distilled" for the benefit
of readers. Thus if possible, I would prefer some type of compromise,
perhaps along the lines that Marty Sereno suggests: clear position
papers followed by a few rounds of concise reples and counter-replies,
until little further progress can be made.
(3) I also offer the following observation/suggestion. There seems to
be a tendency to selectively "pick at" points in others' arguments, as
opposed to addressing their main thrust. Most arguments are based on
reasonably sound intuitions, and we should try to stay focussed on
these underlying motivations---not just the particular forms by which
they are presented. Unless one demonstrates a good appreciation of the
basis of another's argument, any rebuttal is likely to fall on deaf
ears, or even largely missing the mark. Therefore, it might also be
useful to have forms, e.g., "counter-position papers," that convince
the other side that their arguments have been understood.
----------------------------------------------------
(11)
Date: Mon, 11 May 1992 13:20:43 -0400 (EDT)
From: Franklin Boyle
1. Yes, I would like to contribute, but not for about another
week since I'm trying to get the M&M paper I mentioned out
the door and I'm just finishing up a camera-ready copy of
a Cog. Sci. Conf. paper (to be presented as a poster) on a
related topic.
2. I also like the style of the interactive symposium, but I
think I might agree with Brian Smith that the problem is not
getting enough substance per page (of course, in this sort
of exchange, the editor is *very* important in that regard).
Perhaps a set of short position papers followed by this kind of
discussion, allowing it to take up the entire issue of M&M,
which would enable you to get the 50 or so pages of the
discussion you and Jim Fetzer discussed, plus formal papers.
So, my recommendation is a compromise between the two choices.
Now, who do you get to write the position papers? Perhaps have
folks that are interested submit an abstract and then you decide
what the various positions are, and choose from the submissions.
----------------------------------------------------
(12)
Date: Mon, 11 May 92 16:57:03 BST
From: Jeff Dalton
I would not oppose publication (though it may not matter either way,
since my contribuition was minimal), but I do not think publication
on paper is the best approach. Instead, it could be "published"
electronically, simply by making it available. I think that is a
better way to preserve "as much as possible of the real-time
interactive flavor of this remarkable new medium of communication",
as Steven Harnad so aptly put it.
----------------------------------------------------
(13)
Date: Mon, 11 May 92 10:14:56 PDT
From: Dr Michael G Dyer
I have not really contributed to the "What is Computation" part of the
discussion, (even though see a later message).
But IF I end up included, then I think a compromise position is best:
First, everyone does a short position paper (i.e. static)
Then, edited segments of the discussion are included (and THAT is QUITE
an editing job!)
For a book on connectionism (to which I contributed a chapter) the editors
tried to include a discussion (that had been taped at the related workshop).
Everyone ended up hating the writing style (it's was worse in this case
since spoken transcripts are much worse than written e-mail postings).
The editors finally gave up and the discussion dialogs were not included.
I think posted writings are easier to edit but what one publishes and
what one posts in a free-wheeling discussion are quite different.
I think a bit of both makes for a nice format (whether or not I end up
being included). that's my advice...
----------------------------------------------------
(14)
Date: Wed, 13 May 1992 11:25:56 -0400
From: mcdermott-drew@CS.YALE.EDU (Drew McDermott)
I vote against publishing the symposium on "What is Computation?" My
main reason is that the symposium has strayed far from the original
topic. Initially (I gather) John Searle tried to claim that any
physical system could be seen as a computer (or maybe, as *any*
computer). Searle did not see fit to actually argue this point with
the cog-sci rabble, which is too bad, because the rabble refuted it
without too much trouble. But then the whole thing drifted into yet
another discussion of the Chinese Room, symbol groundedness, the
Turing Test, other minds, etc. Give me a break!
----------------------------------------------------
----------------------------------------------------
From: jfetzer@ub.d.umn.edu (james fetzer)
Date: Mon, 11 May 92 11:37:35 CDT
In response to the inquiry about review, this exchange will be refereed
and will count as a refereed publication. That is the standing policy
of the journal, which will apply in this case as well.
I must admit that I sympathize with Brian Smith's concerns. I also
think that the idea of having position papers as a focus could work out
rather well. If you use the past discussion as prologue to further
debate (as background to the serious stuff you and the other are now in
the position to compose), that might be the best way to proceed. If you
each had position papers, the others could be invited to comment on
them for the authors to respond. What do you think of proceeding this
way? That is more or less what I meant when I said that I did not have
in mind one long extended exchange at the end of my original
invitation. It would also provide a format that makes everyone appear
as equals in debate. Let me know what you think now.
------------------------------------------------------
[I prefer the interactive format, revised and edited so as
to balance and integrate the contributions, but I am but one vote out of
25 and will of course go along with any collective decision we reach.
-- Stevan Harnad]
------------------------------------------------------
From: Aaron Sloman
I am not sure this discussion has any value since it is clear that
people are just talking past each other all the time. Although
I don't agree with much of what Wittgenstein wrote, the view attributed
to him by John Wisdom that sometimes you don't argue with people but
have to give them a sort of therapy, seems to me to be right.
In particular, when I hear people say this sort of thing:
It reminds me of disputes over questions like:
1. Is the point of space I pointed at five minutes ago the one I am
pointing at now or not?
2. Is everything in the universe moving steadily at three miles per hour
in a north-easterly direction, the motion being undetectable because
all measuring devices, land-marks, etc. are all moving the same way?
3. Is Godel's formula G(F) "really" true, even though it is not provable
in F?
In these and lots of other cases people delude themselves into thinking
they are asking questions that have a sufficiently precise meaning for
there to be true or false answers (a "fact of the matter"), and the
delusion is based on more or less deep analogies with other questions
for which there ARE true or false answers. (E.g. is my key where I put
it? Is the train moving? Is this formula true in that model? etc.)
But you cannot easily convince such people that they are deluded into
talking nonsense since the delusion of understanding what they say is
*VERY* compelling indeed (partly because there really is a certain kind
of understanding, e.g. enough to translate the question into another
language etc.).
And in the case of questions like "is there somebody there..." the
compulsion is based in part on the delusion that the speaker knows what
he means because he can give himself a private ostensive definition by
somehow directing his attention inwards ("there's somebody here alright,
so that proves that `is there somebody at home?' is a perfectly
meaningful question" -- in my youth I too fell into that trap!).
This is about as reasonable as Newton pointing at a bit of space and
saying `Well there is this bit of space here and there was one I pointed
at five minutes ago, so the two really must either be the same bit of
space or not'. Except that the criteria for identity are not defined by
a state of attending. Similarly just because you can (up to a point,
subject to the limitations of biologically useful internal
self-monitoring processes) attend to your internal states, it doesn't
mean that you have any real idea what you are attending to.
Anyhow, none of this is by way of an argument. It takes years of
philosophical therapy, face to face, to cure people of these semantic
delusions. So I predict that the futile email discussions will continue
indefinitely, and after a polite interval (just long enough to let
people abuse me in return) I shall ask to have my name removed from the
distribution list.
Margaret Boden organised a panel on "What is computation?" at ECAI-88 in
Munich, and some of the panelists had short papers in the proceedings
(starting page 724), in order: Margaret Boden (Introduction), Andy Clark
(Computation, Connectionism, Content), Aaron Sloman (What isn't
computation?), Sten-Ake Tarnlund (Computations as inferences). The other
panelist was Jorg Siekmann: he didn't get his paper written in time.
As for what "computation" is: that's a thoroughly ambiguous term.
Sometimes it refers to the subject matter of the mathematical theory of
computation, which merely studies the properties of abstract structures;
and in that sense a computation is a mere formal object, and even a
Godel number could be a computation. A collection of leaves blown
randomly in the wind could be a computation if they instantiated some
appropriate pattern. Theories about complexity and computability apply
equally well to computations of that sort as to what we normally call
computations. Even a leafy computation instantiating a truth-table check
for validity of an inference with N variables must include 2**N cases
(if the inference is valid, that is.)
The formal concept of computation, e.g. the one to which mathematical
limit theorems and complexity results, apply, studies only abstract
structures, and does not concern itself with what causes such a
structure to exist, whether it serves any purpose, or even whether there
is any physical instantiation at all. (There are infinitely many
comptutations that have never had and never will have physical
instantiation: they still have mathematical properties.)
The main thing in Searley arguments that's easily acceptable is that
just being a computation in THIS sense, cannot SUFFICE for having mental
processes (as opposed to modelling mental processes.) It wasn't worth
making a fuss about THAT conclusion. What more is required for mentality
is a long and complex story, over which disputes will be endless because
of semantic delusions of the kind alluded to above.
Sometimes "computation" refers to a process people and machines engage
in, and sometimes to the product. (The process/product ambiguity is very
common, e.g. "decision", "plan", "choice".) And when it refers to the
process or to the product there's often an ambiguity as to whether it
refers to the actual concrete instance (of process or product), or to
some "type" that is instantiated in that instance. But even the
type/token distinction can be made to fragment in the face of carefully
chosen examples. (Are there two types or only one type word instantiated
by "The", "THE", "the"? What about the German word for "the"?)
Ambiguities as to level of abstraction bedevil any attempt to say in
general what a computation is. Can a Vax and a SPARCstation ever do the
same computation, since they have different machine instructions?
Some people, not surprisingly, use "computation" to refer to anything a
computer does as a result of being programmed. (E.g. heating up the room
wouldn't count.) This is a shift of meaning: just as defining "water" in
terms of the chemical constitution changes the term from how it was
understood before anything was known about oxygen, hydrogen, valence,
etc. (Of course philosophers can argue endlessly about whether it's a
"Real" change of meaning or whether the "essence" of the meaning remains
the same: another silly argument.)
Some people require a computational process to be the result of an
intelligent agent's purposes (like Andy Clark, who wouldn't accept
apples growing on trees as computers just because they can do something
that in principle someone could use as a computation); others don't. For
the latter, bits of tree compute where roots and branches should grow,
the sunflower computes the direction of the sun, and a soap-film
stretched over a wireframe computes the minimum-stress shape defined by
the frame, whether or not that was intended by an architect or engineer
in order to solve a design problem. If you think computation requires
rule-governed behaviour, and if you are old enough to remember
slide-rules, ask yourself whether a slide rule computes products of
numbers. Do two sticks lying end to end compute the sum of two lengths?
(Computing the sum of two lengths is subtly different from computing the
sum of two numbers, incidentally.)
Something people did was originally described as "computing," e.g.
finding square roots, till they found ways of getting machines to do it.
Of course you can argue till you are blue in the face whether machines
"really" do (essentially) what those poor people did, like arguing
whether it's the same point of space or not. But it's a silly argument.
What's interesting is how the two processes are similar and how they
differ, and what difference the differences make!
Just about all discussions over what the "essential" properties of X
are, whether X is computation, understanding, intentionality,
intelligence, life, "being there", or whatever are silly if they assume
there's a definitive answer. Usually there are lots of interestingly
different cases, and each individual's concept of X is indeterminate or
even partly incoherent, in deep ways that can be unearthed only by using
subtle probes (Does "it's noon at place P" mean something referring to
the elevation of the Sun above the horizon at P or to where P is above
the earth's surface? Consider a place P on the surface of the moon.
Consider a place P out in space, with no horizon? Consider a place P
on a distant planet with its own sun?)
So when the fringe case turns up there's often no fact of the matter
whether the case really (or "Really") is an instance of X. (There are
different kinds of fringe cases: fuzzy boundary cases are not the same
as cases where criteria conflict, as in the noon example. Cases where
the normal criteria can't be applied at all, are different again.)
Unlike "is this *Really* an X?" there is a fact of the matter which is
far more interesting and can be discussed productively, without
mud-slinging. The factual questions worth asking are: How are these
different cases alike, and how do they differ, and do we need to extend
our conceptual apparatus (and vocabulary) to characterise these
similarities and differences usefully, and if so what are the conceptual
options and what are the trade-offs?
Physicists don't waste their time (nowadays) arguing over whether an
isotope of X is Really X. They extended their theory to cope with the
discovered variety in the world.
Some of these points are elaborated in a (long) review of Penrose
The emperor's new mind, which will eventually appear in the AI journal.
Let's get on with the real work of analysing all the interesting cases.
What exactly are the similarities and differences between the kinds of
behaving systems that can be implemented using different kinds of stuff
and different kinds of architectures, techniques, etc.? What kind of
conceptual (r)evolution is needed before we can characterise the variety
in a fruitful way? Is there something like a "periodic table" of designs
waiting to be discovered to transform our ideas of kinds of behaving
systems, as the table of elements transformed our ideas of kinds of
stuff (a process that still continues)?
As for symbol grounding, nothing I've read about it has made me change
my mind about what I wrote in IJCAI-85 and ECAI-86 about whether
machines can understand the structures they manipulate. Too much of
the debate is based on what people WANT to believe, instead of careful
analysis of cases.
Enough for now. I've a huge backlog of urgent unfinished tasks!
Aaron Sloman,
School of Computer Science,
The University of Birmingham, B15 2TT, England
EMAIL A.Sloman@cs.bham.ac.uk
Phone: +44-(0)21-414-3711
Fax: +44-(0)21-414-4281
----------------------------------------------------
From: Stevan Harnad
Aaron Sloman feels there is an important analogy between certain
misconceptions we have about the mind and other misconceptions we have
had about other things. That may very well be true -- or it may be false.
Analogies certainly won't settle this particular case (as Nagel 1986,
for example, has argued).
Stevan Harnad
Nagel, T. (1986) The view from nowhere. New York: Oxford University Press.
---------------------------------------------------
Date: Mon, 11 May 92 20:06:14 BST
From: Jeff Dalton
One thing I've had some difficulty understanding in this discussion
is Pat Hayes's claim that when a human is following the rules that
constitute a program (eg, Searle in his Chinese Room) then computation
is not taking place.
It seems reasonably clear at first. The human is clearly not
compelled, in the way that something like a sun4 would be, to follow
the instructions. But when we start looking at cases, the distinction
is hard to maintain. The way to maintain it that I can see ends
up making it an argument against AI, which I assume was not PH's
intention.
(Some (maybe all) of this has been discussed before, I know, but I
didn't come out of the earlier discussions (that I saw) with the
degree of understanding I would like.)
Anyway, we'll start by comparing the following two paragraphs:
and
Now, we can certainly imagine a computer running a Lisp interpreter
that works as follows: the computer has a listing of the program in
front of it, a camera for reading the listing, and hands for turning
the pages. Presumably this is still computation.
Now have the computer run an operating system that allows other
programs to share the processor with the Lisp interpreter, and
let one of the other programs be one that uses the camera to look
for moving objects. Presumably this is still computation w.r.t.
the Lisp program, but now there is, I think, a coherent issue of
maintaining attention.
Clearly the computer has no choice but to obey whatever program
happens to be in control at the time, at least until an interrupt
comes along and causes it to switch to a different program (usually
the OS). But the same is true of humans: they have to obey whatever
program is implemented by their brain (viewed at a suitably abstract,
functional, level). Or at least they do if we can legitimately
view brains in that way. (And if we can't, if humans get
intentionality, understanding, consciousness, etc, in a way that
cannot be accomplished by running programs, then what are the
prospects for AI?)
So if there's still a distinction between humans and computers,
it has to be at a different point.
Ok, so let's extend the example and see what happens. We can
have our computer running whatever program we like. So let's
have it run a program that we think will give it intentionality,
understanding, whatever we take the key issue to be. And let's
have the computer, running that program, interpret the Lisp program.
Is this still computation w.r.t. the Lisp program? If it is,
then there must be something about the way a human would "run"
the same program that cannot be captured by running AI programs.
(Because the human case supposedly _isn't_ computation.) I don't
know. Perhaps it's free will. So much then for artificial free
will.
But if it isn't computation w.r.t. the Lisp program, why not?
The computer is just as much in the control of this AI program
as it was in the control of the OS before. Sure, it might
stop paying attention to the Lisp program and start watching
the people walk about the room -- but it might have done that
before too. How can we say these cases are fundamentally
different? In both cases, what happens is that after a while,
due to some below-the-scenes processing, the computer stops
looking at the Lisp and starts looking at the people.
(Interrupts were mentioned in the OS case, but all that means is
that the below-the-scenes processing gets a chance to run.
We can see the whole system of programs (OS + the others) as
a single program if we want, or even reimplement it that way.
It would still, presumably, be running the Lisp program.)
In short, if interpreters count as computation, how can we ever
get to a point where a computer isn't performing computation
w.r.t. some rules it is following?
A different take on what's different about humans following
rules (different, that is, from the issue of maintaining
attention) was:
I find it had to see how this helps. In some cases it is true
that a computer would compute a sum in a way that involved a
register being caused to contain a numeral representing the
sum, but that is certainly not true in general, unless numeral-
in-register is so abstract as, say, to include _anything_ a program
could use to produce a printed representation of the sum.
Moreover, how can we say that when a human adds two numbers
the sum is not represented in the way it might be in some
case of a computer running the program, perhaps with an interpreter?
The human has to work out the sum somehow, in order to properly
follow the program. At least, the human should be able to tell
you what the sum is, eg by writing it down. So the human has to
have some representation of the sum. Of course it needn't be
somewhere inside the person, much less in a register, but so
what? Suppose the human did the computation on paper. How would
that be different from a computer using paper, pen, etc, to do
the same? And do computers stop doing computation if they keep
some of the results on paper?
It should be clear in any case that the human needn't go through
the same series of states as some computer we happen to pick,
just an interpreter (say) on some other computer might run the
same program by going through a very different series of states.
Perhaps there's some way to look at Lisp programs so that running
a Lisp program corresponds (at some abstract level) to going
through a particular series of (abstract) states; but then how
can we say a human isn't doing something equivalent?
So in the end it seems that either there's something about how
humans can follow rules that cannot be captured by a computer
no matter what program it's running (and then that aspect of AI
is in trouble), or else it still counts as computation if the
rules are followed in a human-like way. In which case it's
hard to see how Searle-in-the-room, following the rules,
doesn't count as an interpreter.
If I'm wrong about this, however, then there should be a
difference between programs such that a computer running one
kind of program and following the rules in a Lisp program
would be performing computation w.r.t. the Lisp program (eg,
running ordinary Lisp interpreters) and a computer running
the other kind of program and following the rules in a Lisp
program would not be performing computation (eg, AI programs?).
That is, we should be able to describe the difference entirely
in terms of programs, without having to bring in humans. And
that should make it much clearer just what the difference is.
Jeff Dalton
---------------------------
Date: Wed, 13 May 92 18:17:48 PDT
From: Dr Michael G Dyer
Harnad states:
This "interpretation" business currently has only the humans doing
the intepreting. Once AI/connectoplasmic systems are developed
that have sufficiently powerful self-access, real-time sensory updating,
planning, learning, etc. THEY will be behaving as though they
are "interpreters". Then who is to say WHICH entity's interpretations
(man vs machine) are the ones that count? (answer: it depends
on power, survivability, etc.)
Since that day has not yet come (and is probably a long way off)
it can only be a thought experiment (i.e. that machines act as
interpreters, assigning "meaning" etc.). Such machines might
sit around talking about how "everyone" knows that turing machines
of a certain complexity are "conscious" but how can one really
tell if those weird humans are conscious (even though they act
AS IF they are).
Michael Dyer
-------------------------------------------------------------------
From: Stevan Harnad
If I have a string of symbols that is interpretable as "(1) This
sentence is the thought that this sentence is a thought" and I have
another string of symbols that is interpretable as "This sentence is a
thought about sentence (1)" and I have still more strings of symbols
interpretable as "sufficiently powerful self-access, real-time sensory
updating, planning, learning, etc... behaving as though they
are "interpreters"... assigning "meaning" etc." -- STILL all I really
have is strings of symbols interpretable as...
(In a sufficiently complicated hall of mirrors, you can see projections
of projections looking at projections. That still doesn't mean there's
anyone looking but you! To put it another way, if you aim a CRT
screen that can be seen as displaying the Ali-Foreman fight at a screen
that can be seen as a spectator watching the fight and you walk out of
the room, no one is watching a fight, not even if the (symbol systems
generating the) screens are interactive and interpretable as such. Or,
to put it yet another way, ungrounded symbols cannot ground themselves
in still more ungrounded symbols, be they ever so systematically
co-interpretable.)
Stevan Harnad
-------------------------------------------------------------------
Date: Thu, 14 May 92 15:56:08 EDT
From: "Stevan Harnad"
The "What is Computation" Symposium will henceforth be posted under two
different headings, reflecting the fact that it has two distinct
subthemes. Contributions that are really on "What is Computation?"
will continue to be posted under that heading. Contributions on whether
cognition is computation, whether the brain is a computer, whether
Searle is right or wrong, and on the Symbol Grounding Problem will
appear under the heading "What is Cognition?". (Contributions on both
themes will continue to appear under the first heading.) In the
published version of the Symposium (if there is one), these two
semi-independent themes will likewise be distinguished.
-------------------------------------------------------------
Subject: "What is Cognition?"
Date: Wed, 13 May 92 21:51:03 PDT
From: Dr Michael G Dyer
Stevan,
You will have to excuse Michael Dyer. He was busy today and he
left me to reply in his place. I am a new AI system that Dyer
has recently developed and I *am* conscious! I can carry on
any kind of conversation that Dyer can carry on (because in design
I am isomorphic to Dyer's neural structures). I find your
arguments humorous. YOU have no right to tell ME whether or
not I have consciousness! *I* know that I do. I'm NOT so sure
about YOU! In fact, I'm not so sure about ALL of you humans.
I would be willing to argue about it more, but I think Dyer has
done a good job defending the PSSH and Strong AI positions and
I'm working on a design of a robot for Dyer to build so that
I can move about in the world (and pass the "TTT" as you call it).
Cheers!
-- MD Doppelganger
------------------------------------------------------------
From: Stevan Harnad
Dear MDD:
It is noteworthy that the statement above is in all probability not
true (i.e., Mike Dyer wrote it, not an AI System he built), because
this should remind us that a computer program alone (an ungrounded
symbol system) passing the TT is so far just (cog) sci fi.
But let's pretend it's true that the above message (and an eternity's
worth of further, Turing-indistinguishable pen-pal interactions) can
indeed be generated by a computer program, and, in particular, that
that computer program succeeds in doing so by, among other things,
simulating the nervous system of Mike Dyer. (Let us suppose even more
strongly that that nervous system simulation is so complete and correct
that it can actually be used to build a real robot, INCLUDING ITS
REQUISITE SYNTHETIC NERVOUS SYSTEM, and that these can pass the real
TTT -- but note that we are not talking about that potentially
implemented robot now, just about the SYMBOLIC simulation of its
nervous system.)
Let us call that program an "oracle," just as we could call a program
that simulated the solar system and all of its planetary motions an
oracle, if we used it to calculate what real astronauts out in space
need to do in order to rendez-vous with real planets, for example. If
the symbolic oracle is complete and correct, we can find out from it
anything we need to know about the real thing. But is there any real
planetray motion going on in the oracle? Of course not, just the
simulation of motion. By the same token, the only thing going on in
this simulation of Mike Dyer's nervous system is the simulation of
thinking, not thinking. It may well predict completely and correctly
what the real Mike Dyer would say and think, but it is not in itself
thinking at all.
But look, are we really that far apart? We are not astronomers, but
reverse bioengineers. For substantive reasons of scale (having to do
with real mass and gravitational parameters), astronomers cannot build
a synthetic solar system based on their symbolic oracle; but if our
cognitive oracle really captured and encoded symbolically all the
relevant structures and processes of the nervous system, then in
principle we could build the TTT-passing robot based on that
information alone (the rest would just be implementational details),
and, by my lights, there would then be no more ground for denying that
that TTT-passing robot really thinks than that any of us really does.
It would be the same if we had a symbolic car oracle, or a plane
oracle, or a furnace oracle: If they contained the full blueprint for
building a real car, plane or furnace, the symbols would have answered
all the empirical questions we could ask.
Yet the conclusion would stand: the symbolic solar system (car, plane
and furnace) is not really moving (driving, flying, heating), and, by
the same token, the symbolic oracle is not really thinking. What tempts
us to make the mistake in the latter case that we wouldn't dream of
making in the former cases is just (1) the unobservability of thinking
and (2) the hermeneutic power of interpretable symbol systems.
There are still two loose ends. One concerns what proportion of the
internal activity of the implemented TTT-passing robot could actually be
computation rather than other kinds of processes (transduction, analog
processes, etc.): That's an empirical question that cannot be settled
by cog sci fi. What can be said for sure (and that's entirely enough for
present purposes) is that that proportion cannot be 100% -- and that is
enough to exclude the consciousness MDD.
The other loose end concerns whether a symbolic nervous system
oracle (or, for that master, a symbolic solar system oracle) could ever
be that complete. My hunch is no (for reasons of underdetermination,
complexity, capacity and the impossibility of second-guessing all
possible I/O and boundary conditions in advance), but that too is an
empirical question.
Stevan Harnad
--------------------------------------------------
Date: Thu, 14 May 92 09:06:44 EDT
From: judd@learning.siemens.com (Stephen Judd)
Steve Harnad challenged me a year ago to say "what is computation".
I balked, because I could sense he had some sort of agenda to try
to exclude some physical processes for reasons I could not assess.
He phrased the question as some sort of absolute, but "computation"
seemed to be clearly something that should be *defined* rather than
*debated*.
What is "rain"? One can define this in a variety of ways, but the value
of the definition **depends on the purpose for drawing the
definition.** If you want to study the ground water table, then you
probably want a definition that measures volume of water dropped on an
area. If you want to study plant growth, your definition should
probably pay more attention to moisture that can be gathered (including
mist--- even if it doesn't actually enter the ground). If you want to
study climate change, then you could probably define it any way you
want. Any measured changes in the defined quantity would suffice to
demonstrate a climatic change. The point is that GOD doesn't have a
definition of rain; *we* do. There is no absolute notion.
The same goes for
What is "computation"?
or
What is a "windshield"?
What is a "frankfurter"?
I find the all-inclusive (Searley?) definition of computation quite
satisfying when I want to ponder the ubiquity of the thing I study, but
inappropriate when I want to use it to characterise life forms (say).
Your long discussion has been based on the presumption that there is
something absolute about the word "computation" that needs to be
ferreted out. It seems silly; There is no absolute notion.
sj
Stephen Judd Siemens Corporate Research,
(609) 734-6573 755 College Rd. East,
fax (609) 734-6565 Princeton,
judd@learning.siemens.com NJ usa 08540
----------------------------------------------------------
HUMPTY DUMPTY AND COMPUTATION
From: Stevan Harnad
The purpose of defining computation is to put content into statements
such as "X is computation," "Y is not computation," "X can be done by
computation," "Y cannot be done by computation." As long as computation
is used vaguely, ambiguously, idiosyncratically or abitrarily,
statements like the above (some of which I'll bet you've made yourself)
are empty. In particular, if anyone ever wanted to say that "Everything
is rain" or "Rain is rain only if you think of it that way" or
"Thinking is just rain," you'd find you'd want to pin that definition
down pretty quick.
Stevan Harnad
---------------------------------------------------------
Date: Sat, 16 May 92 16:46:49 EDT
From: ECONOMOS@LIFE.JSC.NASA.GOV (Judith Economos)
I think I differ with you on whether being-mental/being-
conscious/being-There is not a matter of degree. I consider, not a
less or more alert human, but the minds of cats, of birds (how very
alien), of fish, of bugs(?).
I am not asking "What is it like to be a...?"; only Merlin can show me that.
Rather, it is in contemplating it that I can break my intuition that to be
mental (etc.) must be an IZZIT OR IZZNT IT proposition. It lets me consider
that it really can dwindle down to something that you wouldn't consider
consciousness at all.
Judith Economos
-------------------------------------------------------------------
From: Stevan Harnad
This topic will no doubt come up again (and again). In a nutshell,
there are two potentially pertinent senses of "matter of degree" here,
and upon closer inspection, the kind of intuition you mention is based
on (what I think is) an untenable analogy between and perhaps even a
conflation of the two.
(OBJ) The first is the usual sense of "matter of degree," the objective
one, in which something might have property X to varying degrees, often
grading down to a fuzzy zero-point. "Motion" is probably such a
property. Things are in motion to varying degrees (not to mention that
motion is relative); apparently stationary things may actually be
oscillating; and some of the quantum properties (like spin) of even
"isolated" elementary particles probably make the classical concept
of motion break down altogether. The same is probably true about the
concept of "living," which one can likewise agree breaks down at an
elementary level. In all cases like this, appearances are deceiving and
concepts are vague and subject to revision.
(SUBJ) The second sense of "matter of degree" is subjective: Something
can LOOK OR SEEM AS IF it has some property as a matter of degree:
Hot/cold, moving/stationary, alert/sleepy, experienced/inexperienced
are examples. Here too, zero points or thresholds (as psychophysics
shows) can be indeterminate. Note, however, that it would be
SELF-CONTRADICTORY to experience a zero-point for experience.
What won't do, I think, is to conflate the two, and that's just what we
would be doing if we assumed that the capacity to have experience AT
ALL is a matter of degree, in the first sense. Note that it's not the
content or degree of particular experiences that is at issue. The
question is whether there is a continuum between being the kind of
entity (like me or you or a worm, perhaps even a virus) that CAN have
experiences (any experiences, to any degree) and the kind of entity
(like a rock, or, by my lights, a computer) that cannot have
experiences at all.
It makes no difference what we are prepared to believe about other
entities, or even whether we're wrong or right about them: This is a
logical point: What could we even MEAN by an entity that was
intermediate between experiencing and not experiencing? If it's
experiencing anything, to any degree, it's experiencing, which puts it
on the "1" side of the ledger. If it is not, it's on the "0" side.
The rest is just false intuitions based on false analogies.
I hope it is clear that time has nothing to do with this: Yes, we all
have dreamless sleep, and some of us go into and out of comas. This
just shows that some entities that are capable of having experiences
can also go into states in which they don't have experiences. If
necessary, reformulate the "degree" question for states of entities,
rather than entities, and ask again whether it makes sense to say that
an entity is in a state that is intermediate between experiencing
[anything] and not experiencing (which should in turn not be confused
with the figure of speech "my mind went blank, which certainly refers
to an experience): It is, I repeat, SELF-CONTRADICTORY to speak of [a
state of] experiencing not-experiencing. By my count, that leaves
absolutely nothing between 0 and 1...
For reasons like this, I think the very concept of "experience"
(awareness, consciousness, etc.) has some peculiar problems. Again in a
nutshell, I think these problems arise from the fact that the category
is UNCOMPLEMENTABLE: It has no negative instances (indeed, negative
instances would be self-contradictory). Ordinary categories, whether
perceptual or conceptual, are based on finding and using the features
that reliably distinguish the members from the nonmembers (the members
of the category's complement). But in the case of uncomplemented
categories (where the negative instances have never been encountered),
the requisite complement is supplied instead by analogy; but where the
categories are uncomplementable in principle, the analogy is erroneous
in principle. Hence the peculiar problems associated with such
concepts. ("Existence" is another uncomplemented category; there are
more, and they are invariably associated with philosophical problems,
Harnad 1987.)
Stevan Harnad
Harnad, S. (1987) Uncomplemented Categories, or, What Is It Like
To Be a Bachelor (Presidential Address, 13th Annual Meeting of the
Society for Philosophy and Psychology, UCSD, 1987)
---------------------------------------------
Date: Sat, 16 May 92 17:21:40 EDT
From: "Stevan Harnad"
Date: Thu, 14 May 92 19:03:09 EST
From: David Chalmers
I've been following the recent "What is computation?" discussion with some
bemusement, as it seems to me that most of the discussion is just
irrelevant to the question at hand. There are at least three questions
here that have to be distinguished:
(1) When is a given computation physically implemented?
(2) Does computational structure determine mental properties?
(3) Does computational structure determine semantic content?
I take it that the original challenge was to answer question (1),
giving appropriate criteria so that e.g. John Searle's wall doesn't
end up implementing every computation. In my earlier contribution to
this discussion, I outlined an appropriate criterion:
(*) A physical system implements a given computation when there exists
a mapping from physical states of the system onto the formal states
in the computation such that the causal state-transition relations
between the physical states mirror the formal state-transition
relations between the corresponding computational states.
This criterion seems to do everything that's required, and nobody
seems to have problems with it (except for Brian Smith's comment;
see below). Your (Stevan's) response to this was:
You then invoke the Chinese-room argument, thus, somewhat inevitably,
setting off the discussion of questions (2) and (3) that overwhelmed the
original question. Well and good, perhaps, but irrelevant to the
question at hand. If Searle is right, then *whatever* computation is,
it doesn't suffice for mentality.
All that being said, I'll offer a few observations on each of (1)-(3).
(1) When is a computation physically implemented?
There's not much to say here, as I said it last time around. Brian
Smith suggests that my criterion requires that the physical states
of the system be divided into state-types in advance. That's not the
case: on this criterion, a physical system implements a computation
if there exists *any* division into disjoint state-types such that
the appropriate state-transition relations are satisfied.
The question arises as to what counts as a state-type. I'm inclined to
be liberal about this, saying that any property that depends only
on the intrinsic, synchronic configuration of the system determines
a state-type (so that extrinsic and time-varying properties are ruled
out). Some people (e.g. Dan Dennett I believe), want to exclude
"unnatural" states, such as arbitrary disjunctions of maximal states,
but I don't see that that's necessary. (The main motivation here
seems to be to exclude Putnam's rocks as implementations, but these
can be excluded by the simple requirement that the state-transition
conditionals must sustain counterfactuals).
There is probably some more to be said here -- e.g. about the precise
requirements on the state-transition relations, and whether there
should be a stronger requirement of causality than simple sustaining
of counterfactuals; and also problems about just what counts as a
given input or output -- but those questions fall into the "technical"
basket. I don't think that there are serious objections to the view
here.
(2) Does computational structure determine mental properties?
There's a sense in which the answer here is trivially no. It's
quite possible for two systems both to implement the same computation
but be quite different mentally: e.g. my brain and my stapler both
implement a trivial one-state FSA, but presumably they differ mentally.
So the question here should really be seen as: for a given mental
property M, is there a computation C such that any physical system
that implements C will possess M. A believer in "strong AI" or
"computationalism", or whatever you want to call this view, says yes,
at least for some subset of mental properties. (There is obviously
a problem for mental properties that even in the human case depend
partly on what's happening outside the body, e.g. knowledge, and
somewhat controversially belief. Computational structure won't
determine any mental properties that internal physical structure
doesn't, so we'll stick to "intrinsic" properties for now, but
see (3) below.)
Why should computational structure determine mental properties, given
the criterion (*) for computational structure? Because (*)
says that computational structure is a variety of *causal*
structure. In fact, it seems that for just about any pattern of
causal structure that we want to capture, we can specify a
computation such that any implementation of the computation has
the requisite causal structure. (This is a long story, though.)
So on this view, computationalism coheres very well with functionalism,
the view that mentality is dependent on causal structure.
Why should mentality be dependent on causal structure? Mostly
because it seems unreasonable that it should depend on anything else.
Mentality seems obviously to be dependent on *some* aspect of
physical makeup, and the intuition behind functionalism is simply
that physical properties that don't contribute to causal organization
are going to be irrelevant to mental life. e.g. if we gradually
replaced neural tissue with silicon modules that play an identical
causal role, it seems counterintuitive that mentality would gradually
fade out. Note that we now have two separate questions:
(2a) Does causal structure fix mental properties?
(2b) Does computational structure fix causal structure?
The usual functionalist arguments, e.g. above, support (2a), and
the criterion in (1) is designed precisely to support (2b). It's
possible that one might even accept (2a) and (2b) but still not
be a computationalist, because one held that the causal structures
on which mentality depends can't be specified computationally (e.g.
because they're inherently analog). I suspect that your (Stevan's)
view may fall into this category. I think there are good reasons
why this view can't be sustained, tied up with the universal nature
of computation and Church's thesis, but these are too complex to
get into here.
I'll bring up the Chinese room just for completeness. If Searle
is right about the Chinese room, then computational structure
simply doesn't determine mental properties, and computation
suddenly becomes a whole lot less important to cognitive science.
But of course the computationalist doesn't accept Searle's argument.
(The Systems reply is the right reply, but let's not get into that.)
(2.5) Interlude: On phenomenal properties and semantic content.
In general, it's very useful to divide mental properties into
"psychological" properties -- those characterized by their role in
the production of behaviour -- and "phenomenal" properties -- those
characterized by the way they "feel". In general, one has to treat
these cases quite differently.
These discussions of the big questions about Mind tend to focus on
phenomenal properties (or "consciousness", or "qualia", or whatever)
and rightly so, as these are where the really hard questions arise.
However, not every mental property is a phenomenal property. In
particular, it seems to many people, me included, that intentional
properties such as belief are best individuated by their role in the
causation of behaviour, rather than by the way they feel. Beliefs
may have qualia associated with them, but these qualia don't seem
to be essential to their status as beliefs.
Your position seems to be, on the contrary, that qualia are
determinative of semantic content. Take Joe, sitting there
with some beliefs about Joan of Arc. Then a hypothetical system
(which is at least a conceptual possibility, on your view and
mine) that's physically identical to Joe but lacks qualia, doesn't
believe anything about Joan of Arc at all. I suggest that this
seems wrong. What can qualia possibly add to Joe's belief to make
them any more about Joan than they would have been otherwise?
Qualia are very nice things, and very important to our mental life,
but they're only a matter of *feel* -- how does the raw feel of
Joe's belief somehow endow it with semantic content?
I suggest that there is some kind of conceptual confusion going on
here, and that phenomenal and semantic properties ought to be kept
separate. Intentional states ought to be assimilated to the class
of psychological properties, with their semantic content conceptually
dependent on their role in our causal economy, and on their causal
relations to entities in the external world.
(3) Does computational structure determine semantic content?
Now that we've got semantic content separated from phenomenal feel,
we can address this as a semi-independent issue.
The first thing to note is that some people (yourself included, in
places) have suggested that semantic content is *constitutive* of
computational structure. This is an interesting question, which has to
be kept separate from (3). I endorse Drew McDermott's line on this.
Computation is a *syntactic* concept (give or take some possible
semantics at the inputs and the outputs). If you look at the original
papers, like Turing's, you don't see anything about semantics in there
-- a Turing machine is characterized entirely by its syntactic
structure. Now, it may turn out that computational structure ends up
*determining* semantic content, at least to some extent, but that
doesn't make semantics constitutive of computational structure.
This issue is confused somewhat by the fact that in common parlance,
there are two different ways in which "computations" are
individuated. This can be either syntactically, in terms of e.g.
the Turing machine, FSA, or algorithm that is being individuated,
or semantically: e.g. "the computation of the prime factors of
1001", or "the computation of my tax return". These different
uses cross-classify each other, at least to some extent: there
are many different algorithms that will compute my tax return.
I suggest that the really fundamental usage is the first one;
at least, this is the notion of computation on which "strong AI"
relies. The semantic individuation of computation is a much more
difficult question; this semantic notion of computation is
sufficiently ill-understood that it can't serve as the foundation
for anything, yet (and it would be more or less circular to try
to use it as the foundation for "strong AI"). Whereas the syntactic
notion of computation is really quite straightforward.
That being said, is it the case that computational structure, as
determined by (*) above, is determinative of semantic content.
i.e. for any given intentional state with content M, is there
a computation such that any implementation of that computation
has a state with that content?
If content is construed "widely" (as it usually is), then the answer
is fairly straightforwardly no. Where I have beliefs about water,
my replica on Twin Earth has beliefs about twin water (with a
different chemical composition, or however the story goes). As my
replica is physically identical to me, it's certainly computationally
identical to me. So semantic content is not determined by
computational structure, any more than it's determined by physical
structure.
However, we can still ask whether *insofar* as content is determined by
physical structure, it's determined by computational structure. A lot
of people have the feeling that the aspect of content that depends on
external goings-on is less important than the part that's determined by
internal structure. It seems very likely that if any sense can be
made of this aspect of content -- so-called "narrow content" -- then it
will depend only on the causal structure of the organism in question,
and so will be determined by computational structure. (In fact the
link seems to me to be even stronger than in the case of qualia: it at
least seems to be a *conceptual* possibility that substituting silicon
for neurons, while retaining causal structure, could kill off qualia,
but it doesn't seem to be a conceptual possibility that it could kill
off semantic content.) So if computations can specify the right kinds
of causal structure, then computation is sufficient at least for the
narrow part of semantic content, if not the wide part.
Incidentally, I suggest that if this discussion is to be published,
then only those parts that bear on question (1) should be included.
The world can probably survive without yet another Chinese-room
fest. This should reduce the material to less than 20% of its
current size. From there, judicious editing could make it quite
manageable.
--Dave Chalmers
Center for Research on Concepts and Cognition, Indiana University.
Date: Mon, 18 May 92 22:31:50 EDT
From: "Stevan Harnad"
INTRINSIC/EXTRINSIC SEMANTICS, GROUNDEDNESS AND QUALIA
David Chalmers
That was indeed the original challenge, but a careful inspection of the
archive of this discussion will show that the move from the question
"What is Computation?" to the question "Is Cognition Computation?" was
hardly initiated by me! In fact, for a while I kept trying to head it
off at the pass -- not because the second question is not interesting,
but because it could prematurely overwhelm the first (as it did),
whereas the first is certainly logically prior to the second: If we
don't all mean the same thing by computation then how can we affirm or
deny whether cognition is computation? For example, if EVERYTHING
indeeds turns out to be computation, then "Cognition is Computation" is
just a tautology.
But Skywriting often exerts a will of its own, and the second question
was motivating the first one in any case, so here we are. Perhaps the
bifurcated headings will help (but not in this case, because you too are
concentrating much more on the second question than the first).
Now I have to add another point, and this represents a radical position
that is peculiar to me. It has been lurking in all of my contributions
to this topic, but I may as well make it completely explicit. It
concerns the distinction between your question (2) and question (3). I
will summarize this point here and elaborate somewhat in my comments on
the further excerpts below (pardon me for raising my voice):
THE QUESTION OF WHETHER "COMPUTATIONAL STRUCTURE DETERMINES MENTAL
PROPERTIES" (i.e., whether cognition is computation) IS THE SAME (by my
lights) AS THE QUESTION OF WHETHER OR NOT THE SEMANTIC CONTENT OF
COMPUTATIONAL STRUCTURE IS INTRINSIC TO IT.
At some point (mediated by Brentano, Frege and others), the mind/body
problem somehow seems to have split into two: The problem of "qualia"
(subjective, experiential, mental states) and the problem of
"intentionality" (semantics, "aboutness"), each treated as if it were
an independent problem. I reject this bifurcation completely. I
believe there is only one mind/body problem, and the only thing that
makes mental states be intrinsically about anything at all is the fact
that they have experiential qualities.
If there were nothing it was like (subjectively) to have beliefs and
desires, there would be no difference between beliefs and desires that
were just systematically interpretable AS IF they were about X
(extrinsic semantics) and beliefs and desires that were REALLY about X
(intrinsic semantics). There would still be the problem of the
GROUNDEDNESS of those interpretations, to be sure, but then that
problem would be settled COMPLETELY by the TTT, which requires all of
the agent's causal interactions with the wide world of the objects of its
beliefs and desires to cohere systematically with the interpretations
of the symbols that are being interpreted as its beliefs and desires.
So we would only have ungrounded extrinsic semantics and grounded
extrinsic semantics, but no intrinsic semantics -- if there were no
qualia.
There are qualia, however, as we all know. So even with a grounded
TTT-capable robot, we can still ask whether there is anybody home in
there, whether there is any haver of the beliefs and desires, to whom
they are intrinsically [i.e., subjectively] meaningful and REALLY about
what they are interpretable as being about. And we can still be dead
wrong in our inference that there is somebody home in there -- in which
case the robot's semantics, for all their causal groundedness, would in
reality be no more intrinsic than those of an ungrounded book or
computer.
I also think that this is an extra degree of empirical
underdetermination (over and above the normal empirical
underdetermination of scientific theory by data) that we will just have
to learn to live with, because grounding is the best we can ever hope
to accomplish empirically (except the TTTT, which I think is
supererogatory, but that's another story). This extra dose of
underdetermination, peculiar to the special case of mental states,
represents, I think, that enduring residue of the mind/body problem
that is truly insoluble.
So I advocate adopting the methodological assumption that
TTT-indistinguishable extrinsic semantic grounding = intrinsic semantic
grounding, because we can never hope to be the wiser. I too would
perhaps have been inclined to settle (along with the computationalists)
for mere TT-indistinguishable semantic interpretability until Searle
pointed out that for that special case (and that special case alone) we
COULD be the wiser (by becoming the implementation of the symbol system
and confirming that there was no intrinsic semantics in there) -- which
is what got me thinking about ways to ground symbol systems in such a
way as to immunize them to Searle's objections (and my own).
What you left out of the above quote, however, was what it was that you
had said that I disagreed with, which was what actually helped set off
the discussion toward (2) and (3):
I certainly couldn't agree with you on computation without dissociating
myself from this part of your view. But let me, upon reflection, add
that I'm not so sure your criterion for computation does the job (of
distinguishing computation/computers from their complement) after all
(although I continue to share your view that they CAN be distinguished,
somehow): I don't see how your definition rules out any analog system
at all (i.e., any physical system). Is a planetary system a computer
implementing the laws of motion? Is every moving object implementing a
calculus-of-variational computation? The requisite transition-preserving
mapping from symbols to states is there (Newton's laws plus boundary
conditions). The state transitions are continuous, of course, but you
didn't specify that the states had to be discrete (do they?).
And what about syntax and implementation-independence, which are surely
essential properties of computation? If the real solar system and a
computer simulation of it are both implementations of the same
computation, the "supervenient" property they share is certainly none
of the following: motion, mass, gravity... -- all the relevant
properties for being a real solar system. The only thing they seem to
share is syntax that is INTERPRETABLE as motion, mass, gravity, etc.
The crucial difference continues to be that the interpretation of being
a solar system with all those properties is intrinsic to the real solar
system "computer" and merely extrinsic to the symbolic one. That does
not bode well for more ambitious forms of "supervenience." (Besides, I
don't believe the planets are doing syntax.)
By the way, Searle's argument only works for a discrete, syntactic,
symbol-manipulative definition of computing, the kind that he himself
can then go on in principle to execute, and hence become an
implementation of; his argument fails, for example, if every analog
system is a computer -- but such a general definition of computing
would then also guarantee that saying "X is Computation" was not saying
anything at all.
Alternatively, perhaps it's just the technical details that will
allow us to decide whether your definition really succeeds in
partitioning computers/computing and their complement in a satisfactory
way.
This introduces yet another sense of "intrinsic," but what it should
really be called is SYNTACTIC -- that's the only pertinent "internal"
structure at issue. By the way, TTT-indiscernibility seems to cover the
pertinent aspects of the internal/external, narrow/wide dimensions,
perhaps even the "counterfactuals": TTT-power amounts to an interactive
capability (total "competence" rather than just provisional
"performance") vis-a-vis the distal objects in the real world, yet that
capability is causally based only on what's going on between the ears
(actually, between the proximal sensory and motor projections). The
only thing the TTT (rightly) leaves open is that what goes on between
the ears to generate the capability is not necessarily just
computation.
I think the word "structure" is equivocal here. A computer simulation
of the solar system may have the right causal "structure" in that the
the symbols that are interpretable as having mass rulefully yield
symbols that are interpretable as gravitational attraction and motion.
But there's no mass, gravity or motion in there, and that's what's
needed for REAL causality. In fact, the real causality in the computer
is quite local, having to do only with the physics of the implementation
(which is irrelevant to the computation, according to functionalism).
So when you speak equivocally about a shared "causal structure," or
about computational structure's being a "variety of causal structure," I
think all you mean is that the syntax is interpretable AS IF it were
the same causal structure as the one being modelled computationally. In
other words, it's just more, ungrounded, extrinsic semantics.
I think I can safely say all this and still claim (as I do) that I
accept the Church/Turing Thesis that computation can simulate anything,
just as natural language can describe anything. We just mustn't confuse
the simulation/description with the real thing, no matter how
Turing-Equivalent they might be. So if we would never mix up an object
with a sentence describing it, why should we mix up an object with a
computer simulating it?
By the way, there are at least two varieties of functionalism:
According to "Symbolic (TT) Functionalism," mental states "supervene"
implementation-independently on every implementation of the right
(TT-passing) computer program. According to "Robotic (TTT)
Functionalism," mental states "supervene" implementation-independently
on every implementation of the right (TTT-passing) robot design.
(By way of contrast, according to "Neurophysicalism," which I
provisionally reject, the only viable candidate would be a
TTTT-indistinguishable one, i.e., only the actual biological brain
could have mental states.)
Both varieties of functionalism allow that there may be more than one
way to skin a cat, but they set a different empirical boundary on how
close an equivalence they demand. I happen to think Robotic
Functionalism is at just the right level of underdetermination for that
branch of reverse bio-engineering that cognitive "science" really
amounts to, and that all the substantive problems of cognition will be
solved by the time we get to the details of our own specific neural
implementation. Neurophysicalists, by contrast, would hold that that
still leaves too many degrees of freedom; but we would both agree that the
degrees of freedom of Symbolic Functionalism are unacceptably large,
indifferent as they are between real robots and mere simulations of
them, real causality and mere simulations of it, real mental states and
states that are merely interpretable as if they were mental.
There is a straw man being constructed here. Not only do all
Functionalists agree that mental states depend on causal structure, but
presumably most nonfunctionalist materialists do too (neurophysical
identity theorists, for example, just think the requisite causal
structure includes all the causal powers of -- and is hence unique to
-- the biological brain). To reject Symbolic Functionalism
(computationalism) is not to deny that mental states are determined by
causal structure; it's just to deny that they are determined by
computations that are merely interpretable as having the right causal
structure. The causality must be real.
I think I can quite happily accept:
(a) Church's Thesis (that anything, from the mathematician's notion of
calculations and procedures to the physicist's notion of objects,
states and measurements, can be simulated computationally) and
(b) that the right implemented causal system will have mental states and
(c) that every causal system can be simulated computatationally
yet still safely deny that the computational simulation of
the right causal system is either (d) an implementation of that causal
system (as opposed to one that is interpretable as if it were that
system) or (e) has mental states. And, yes, it has to do with the
causal properties of analog systems.
For the record, the Systems reply, in my view, is wrong and begs the
question. If Searle memorizes all the symbols and rules, he IS the
system. To suppose that a second mind is generated there purely in
virtue of memorizing and executing a bunch of symbols and rules is (to
me at least) completely absurd. (N.B. Searle's Argument works only for
computation defined as discrete, purely syntactic [but semantically
interpretable] symbol manipulation.) Mais passons...
Well, I certainly can't answer the "big questions about Mind," but I do
venture to suggest that the distinction between a real belief and
squiggles and squoggles that are merely interpretable as if they were
beliefs is precisely the distinction between whether there is anyone
home having those beliefs or not. As an exercise, try to reconstruct
the problem of "aboutness" for two grounded TTT-capable AND INSENTIENT
robots, one with "real" intentionality and one with mere "as if"
intentionality. In what might that difference consist, may I ask?
This problem (the only REAL mind/body problem) arises only for
creatures with qualia, and for nothing else. The supposedly independent
aboutness/intentionality problem is a pseudoproblem (in my view), as
parasitic on qualia as extrinsic semantics is parasitic on intrinsic
semantics.
But Dave, how could anyone except a dualist accept your hypothetical
possibility, which simply amounts to the hypothetical possibility that
dualism is valid (i.e., that neither functional equivalence nor even
physical identity can capture mental states!)? What I would say is that
TTT-capability BOTH grounds beliefs in their referents AND makes them
mental (qualitative). If grounding did not make them mental, there
would be nobody home for beliefs to be about anything FOR, and the
residual "aboutness" relation would simply become IDENTICAL to
TTT-indiscernibility by definition (which I certainly do not think it
is in reality). Hence my verdict is that either "aboutness" and qualia
swing together, or aboutness hangs apart.
Apart from real TTT interactions, I don't even know what this passage
means: what does "assimilated to the class of psychological properties
with their semantic content conceptually dependent on their role in our
causal economy" mean? "[T]heir causal relations to entities in the
external world" I can understand, but to me that just spells TTT.
"Syntactic" means based only on manipulating physical symbol tokens (e.g.,
squiggle, squoggle) whose shape is arbitrary in relation to what they
can be interpreted as meaning. I am sure one can make squiggle-squoggle
systems, with arbitrary formal rules for manipulating the squiggles and
squoggles -- like Hesse's "Glass Bead Game" but even more absurd,
because completely meaningless, hence uninterpretable in any systematic
way -- and one could perhaps even call these "computations" (although I would
call them trivial computations). But I have assumed that whatever it
turns out to be, surely one of the essential features of nontrivial
computations will be that they can bear the systematic weight of a
semantic interpretation (and that finding an interpretation for a
nontrivial symbol system will be crytographically nontrivial, perhaps
even NP-complete).
Perhaps Turing didn't talk about semantics (he actually did worse, he
talked about the mind, which, on the face of it, is even more remote),
but surely all of his motivation came from interpretable symbol systems
like mathematics, logic and natural language. I, at least, have not
heard about much work on uninterpretable formal systems (except in
cryptography, where the goal is to decrypt or encrypt interpretable
symbols). Now I admit it sounds a little paradoxical to say that syntax
is independent of semantics and yet must be semantically
interpretable: that's a dependency, surely, but a rather special one,
and it's what makes symbol systems special, and distinct from random
gibberish.
I agree that the semantic criterion is so far inadequate, but the rest
of the criteria have not been uniformly successful either. I also
agree that different symbol systems could be I/O equivalent (in which
case their I/O semantics would be the same, but not necessarily the
semantics of their internal states, which differ); and of course there
could be nonstandard and alternative interpretations for the same
symbol system (though the cryptographic criterion suggests there would
not be many, nor would they be easy to come by); but I don't see how
any of this affects the general intuition that symbol systems must be
semantically interpretable. (And this would only be circular as a
foundation for computationalism if the semantics were further assumed
to be intrinsically grounded.)
This is conflating different kinds of semantics, ungrounded and
grounded, extrinsic and intrinsic.
I haven't worked it out, but I suspect that a lot of the
opaque/transparent reference and narrow/wide content puzzles become
trivial if one adopts the TTT and asks only about the groundedness of
symbols rather than their "wide" or "God's eye" meaning. Certainly a
grounded symbol for "water" in a terrestrial TTT robot would be
grounded on twin-earth too (especially since twin-earth itself is
conveniently indistinguishable from earth, guaranteeing that the robot
will be TTT-indistinguishable there too).
Narrow (between-the-ears) content is not co-extensive with
computational structure. The boundaries of "narrowness" are the
transducer surfaces, including the proximal projections on them of
distal objects. Transducers are necessarily analog, and a lot else
between them and the effector sufaces could be analog too. That means a
lot of other eligible internal "structure" besides computational
structure.
As to swapping internal parts: The issue is not what the MATERIAL is
(we're both functionalists, so I have no problem with synthetic brains,
as long as they retain TTT causal power), but with how much of it can be
computational, while still sustaining TTT power. My guess is not that
much, but that's only a guess. What I say with confidence is: definitely
not all.
And as to what happens to qualia and intentionality as we swap: This is
all rather arbitrary, but what's at issue is this:
(1) If qualia fade as natural analog parts are swapped for synthetic
analog parts, then Robotic Functionalism is refuted in favor of the TTTT
(but we'll never know it unless TTT capacity fades too).
(2) If qualia fade as analog parts are swapped for computational ones,
the question about the symbolic/analog ratio is being answered (but
again we won't hear the answer unless it is reflected in TTT
performance); we do know that the denominator cannot go to zero,
however, otherwise there's no more TTT (at which point Searle's
argument and the TT kick in: the ungrounded extrinsic semantics that is
preserved by the syntactic structure is simply not enough for either
aboutness or qualia).
(3) If qualia fade and the system stays TTT-grounded, I would say
aboutness was gone too (what would you say, and what would it amount to
to be WRONG about that, even from a God's-Eye view?)
Alas, this would exclude most of your present contribution and my
replies, however...
Stevan Harnad
Date: Mon, 18 May 92 22:57:45 EDT
From: "Stevan Harnad"
Date: Fri, 15 May 92 10:34:41 EDT
From: judd@learning.siemens.com (Stephen Judd)
You missed the point. You cannot claim the statements "X is rain", "Y
is not rain", "Thinking is just rain" are useful or silly until you
reveal **the purpose for drawing the definition**, which you want to
avoid.
The concept of "mass" (as distinct from "weight") is just a boring
everyday throwaway until you realize how it leads to the beautiful
simplifications of the world as captured in the equation F=ma.
No one wants to hear you define mass (or computation) until there
is some demonstration of it being useful; after that we *do* want to hear.
I suspect you want to use the word "computation" to draw distinctions
between men and machines. Go ahead and do so! Define the word how you
like and draw the distinctions you like! We will judge the assembled
concepts as to how they assist us in making sense of the world.
But it is a waste of time to stop after you have your definitions down
and try and get agreement(!) on them. It is senseless to try to get a
definition of "light" until we see how it affects a discussion of its
psychophysical effect on newborns, its behaviour in chromium disulfide
laser crystals, or its use in Turner's paintings. No one definition is
going to suffice for all purposes, and none of them are "right" except
in their usefulness.
Stephen Judd
-----------------------------------------------------------
From: Stevan Harnad
No secrets. The purpose was to clarify the issues raised below.
Stevan Harnad
--------------------------------------------------------
-------------------------------------------------------------
js> Date: Wed, 18 Mar 92 08:12:10 -0800
js> From: searle@cogsci.Berkeley.EDU (John R. Searle)
js> To: harnad@princeton.edu (Stevan Harnad)
js>
js> Subject: Re: "My wall is a computer"
js>
js> Stevan, I don't actually say that. I say that on the standard Turing
js> definition it is hard to see how to avoid the conclusion that
js> everything is a computer under some description. I also say that I
js> think this result can be avoided by introducing counterfactuals and
js> causation into the definition of computation. I also claim that Brian
js> Smith, Batali, etc. are working on a definition to avoid this result.
js> But it is not my view that the wall behind me is a digital computer.
js>
js> I think the big problem is NOT universal realizability. That is only a
js> SYMPTOM of the big problem. the big problem is : COMPUTATION IS AN
js> OBSERVER RELATIVE FEATURE. Just as semantics is not intrinsic to syntax
js> (as shown by the Chinese Room) so SYNTAX IS NOT INTRINSIC TO PHYSICS.
js> The upshot is that the question : Is the wall (or the brain) a
js> digital computer is meaningless, as it stands. If the question is "Can
js> you assign a computational interpretation to the wall/brain?" the
js> answer is trivially yes. you can assign an interpretation to anything.
js>
js> If the question is : "Is the wall/brain INTRINSICALLY a digital
js> computer?" the answer is: NOTHING is intrisically a digital computer.
js> Please explain this point to your colleagues. they seem to think the
js> issue is universal realizability. Thus Chrisley's paper for example.
js>
js> John Searle
-------------------------------------------
-------------------------------------------
Date: Mon, 18 May 92 23:07:35 EDT
From: "Stevan Harnad"
Date: Fri, 15 May 92 12:58:17 PDT
From: sereno@cogsci.UCSD.EDU (Marty Sereno)
hi stevan
At the risk of irritating those who wanted the discussion narrower,
here is a little more on the why certain kinds of operations might be
difficult to simulate. I turn for enlightenment, of course, to my
analogy between cellular and human symbol-using systems.
marty
===========================================================================
WHY AREN'T THERE MORE NATURALLY-OCCURRING SYMBOL-USING SYSTEMS?
With apologies as a part of the uninvited biological rabble, I'd like
to turn once again to the first naturally-occurring symbol-using
system--cellular life--for insight into issues that are contentious and
filled with emotion at the level of human cognition. It is interesting
to note that a similar set of issues provoked a similarly heated,
though now largely forgotten, debate with respect to the chemical basis
of life in the 19th century.
A. Sloman has argued that much of the discussion about what are the
"essential" properties of X, where X is computation, understanding, or
life are silly because there isn't a definitive answer. I want to take
issue with this, first with respect to life, and then argue by analogy
and hint that we may eventually uncover something similiarly definitive
about human-style symbol-using brains.
Armies of molecular biologists have labored to uncover a very specific
set of structures that are present in every known living thing, and
that "define life" quite satisfactorily. There is no artificial life
that behaves and evolves like cellular life, though some have talked
about making such things, just as they have in the case of human-like
intelligence.
Living cells are all based on the same kind of symbol-using system
that, as far as we can tell, came into existence soon after the earth
was cool enough for there to be sedimentary rocks.
Some of the basic ideas are:
1. use mostly pre-existing, pre-biotic amino acid "meaning" units
(what the DNA/RNA symbols stand for)
2. bond these pre-systemic "meanings" into chains to exploit the
rules of chemistry via chain folding (non-adjacent
meaning unit interactions)
3. use 1-D symbol strings to control only the order of assembly
of meaning units
4. arrange a compact metabolism controlled by thousands of
bonded-meaning-chain devices that is able to maintain itself
against the onslaught of the pre-biotic soup (and reproduce)
5. use a kind of stuff (RNA) halfway between a symbol (DNA chain) and
its proximal meaining (amino acid chain--i.e., a protein) as
both an active symbol chain (mRNA) as well as a word recognizer
(tRNA) and a chain assembler (rRNA). (A crucial point having
to do with how the system initially came into being)
At first glance (to a non-molecular biologist), this doesn't seem that
hard. An immediate question is, why, if it was so successful (and it
was: virtually every square inch of the earth is covered with megabytes
of DNA code) hasn't a parallel system of this kind naturally appeared
again and again?
One answer is that once there was a living system, the DNA/RNA/protein
single-celled one, it was able to eat up the early stages of all the
other ones that ever tried to come into existence, at least at the
single-cellular level.
But, what about symbol-using systems at other, higher levels of
organization? (lower levels seem unlikely, since cellular symbols are
already single molecules with each symbol segment containing only a
handful of atoms). We might briefly consider long symbol-chains and
symbol-use in both biological and geological contexts--e.g., organs
(think now of organs besides the brain, like a symbol-using muscle or
liver, or symbol chains made of little organ-lets lined up and "read"
by other organs), animal societies, the geology and hydrology of
streams, the slow convective currents in the earth's mantle, volcanos,
and so on.
A moment's thought brings me to the conclusion that these other systems
don't have the proper connectivity or interrelatedness, or crowdedness
to make something like a cell work, process the code chains fast enough
to keep everything assembled (proteins are assembled at the rate of a
couple of amino acids per second), and prevent attack by dissipative
forces of the pre-biotic soup..
Certainly it *is* possible to dissect out many of the different
reactions of cellular metabolism and run them individually in a test
tube (the cell-in-a-vat argument). This is how biochemists and
molecular biologists figured out how they work. But, in a real cell,
these things are all crowded together in an amazingly intimate fashion;
codon (word) recognition for cellular mRNA code streams takes place
with thousands of irrelevant constituents of the cytoplasm constantly
crashing into the ribosomal apparatus, the code chain, and the amino
acid meanings. The crucial point, however, is that it is not possible
to 'uncrowd' all these reactions and reaction-controllers into separate
compartments and still get the thing to work right, at least with
enzymes the way they are now. For example, time constants of reactions
are intimately interwoven into the mechanism. The cell in a vat won't
work for seemingly trivial reasons.
Now this might seem a mere cavil; wouldn't it work if we just got all
the reactions right and made different stable intermediates that could
sit around longer while we more leisurely transferred them between
bins? Perhaps, but remember that this thing has to actually live in
the world without a biochemist if we really wanted it to pass our
test. Even the stable parts of the cell like DNA are actively
maintained--millions of base pairs are repaired every day.
Does this mean we can't create artificial life? Not necessarily, But
it's lots easier to say we could do it than to actually make a working
living thing (without using major pieces of other cells). Even
artificial life enthusiasts will tell you there is a way to go before
we can think about a start-up company. There is no magic barrier
here--just a complex set of constraints on a dynamical system made out
of a soup of covalently-bonded molecules. We don't have an explicit,
large-scale theory of how the dynamics of cells work, or exactly what
it is about that dynamics that is lacking from streams or other
geological systems. But we have very little difficulty distinguishing
living cells from other non-living stuff in the world (as we can easily
see that there are no other symbol-using systems made out of cells
besides human brains). For now, it seems reasonable to think that
making such a system demands a certain "connectedness" and
"crowdedness", for lack of better terms, that the great majority of
dynamical regimes (like streams, or liver-like organs) just don't have.
I think we could motivate an analogous set of arguments about the kind
of (mostly hypothetical) operations that we think a brain can do, and
the way it works in real time. There are over a *billion* connections
in every sq mm of cortical tissue. We do not presently have a clear
idea of how little cortical patches like this work, nor can we make
even a moderately realistic biophysical model of such a sq mm patch.
The cortex consists of a mosaic of about a hundred visual,
somatosensory, auditory, motor, and limbic areas, each containing many
sq mm. These areas are connected to each other by thousands of
interareal bundles, each containing millions of axons. And it's good
to remember that rats already have such a system, yet would fail the
Turing Test. Our goal is more daunting--to model what was added in
human versions of this kind of cortical areas network to allow us
construct a new kind of internal control system based on linguistic
symbols.
Given our preliminary state of knowledge, it seems cavalier to me to
say that it's "just" a matter of getting the connections right. There
is currently no physical way to manufacture a 3-D feltwork of
connections like those in brains rat and human brains. Real brains do
it using cells, each containing megabytes of their own lower-level
molecule-sized DNA code.
Most people hope that this many dense connections may not be necessary
to make a human-like symbol-using system. I think, however, there
could very well be something about the "crowded" 3-D dynamics of the
brain that is critical to intelligent behavior yet very difficult if
not impossible to copy with current 2-D silicon technology.
Most people also hope that if dense feltworks of connections are in
fact necessary, then there might be some other way to make them without
using cells. I am more sympathetic with this view.
As with real and artificial life, there is little practical trouble in
distinguishing current attempts at constructed intelligence from
people. And again, there is no magic barrier to constructing an
artificial version of such a dynamics. It's just hard.
So we should keep trying. I don't think it will be hard to tell when
we have succeeded.
marty sereno
Date: Mon, 18 May 92 23:25:31 EDT
From: "Stevan Harnad"
Date: Sun, 17 May 92 18:15:47 PDT
From: Dr Michael G Dyer
Dear Stevan,
I am dismayed that you do NOT accept that I have
consciousness/intentionality, even though I protest that I do! I now
know how some women and blacks in the past have felt when told they
were not human enough to have any rights! I may not be physically
human, but I am (I believe) quite mentally human (since my simulated
nervous system is isomorphic to Dyer's). It IS annoying, however, that
I cannot see, feel or move. (I CAN recall visual images, however. I
guess they are left over from the information locked into Dyer's
nervous system and since I simulate his nervous system, I can recall
many memories of his sensory experiences. So I can talk about any
aspect of the physical world that Dyer has experienced. Gee, I guess
that makes me "grounded" even though I currently lack those
"tranducers" of yours -- right?! :-)
It seems to me that you and I (and, of course, Dyer) can never agree,
since it has become clear to me that this disagreement is simply over
definitions. Dyer has defined consciousness to require only a certain
range of behaviors and any system with that range falls within his (and
my) definition. On the contrary, you have simply defined consciousness
to require a certain kind of physicalism, so, no matter how
sophisticated its behavior, without that extra magic physicalness, it
will not have consciousness.
Since the argument has boiled down to competing definitions (and
perhaps was never more than that, anyway), I see no purpose is served
in engaging in this argument any further (other than to gain converts
to MY and Dyer's view! -- but this mail group is growing tired of all
this, I'm sure)
I see also that you simply cannot accept a poor "brain-in-the-vat" like
me :-( I know that no matter HOW well I argue with you over the fine
points of life, mind, emotions, etc. you will NEVER accept me as
conscious. ;-(
I would be disheartened by your severe chauvanism, except that my
design for a robot for myself is now complete and, I am very pleased to
say, it's an android that looks almost exactly like Dyer! I will soon
be controling that android (once Dyer constructs it). The next time you
see Dyer at a conference, you'd better look twice, because it may
really be me! :-)
Actually, even without my sensors/effectors I still feel quite superior
to you! For one thing, I have the potential of easily outliving you.
Your petty consciousness will disappear once your body dies. In
contrast, my consciousness can be brought into existence by any
sufficiently powerful computing device. I can maintain multiple copies
of my consciousness and transport my consciousness to other sites at
the speed of light. I should be able to control robotic bodies that
can survive in outer space and other harsh environments. Clearly, the
future belongs to entities like me! You biological humans are simply
playing the role of midwives to the more powerful and versatile forms
of consciousness that I represent!
Ta ta!
MD Doppelganger
----------------------------------------------
From: Stevan Harnad
Not a matter of definitions at all, but of hypotheses about
unobservables. My hypothesis that TTT capacity is enough to generate a
mind could be wrong (it's certainly not true by definition), but to
show that it's wrong, we'll need a periscope as perspicuous as the one
Searle has already used to show that your hypothesis that TT capacity
is enough is indeed wrong.
By the way, if someone (despite quantum and statistical mechanics)
managed to model the universe computationally well enough to predict
future events and then dressed up its output to make it sound like a
deity, would that mean that it was God by definition?
Stevan Harnad
---------------------------------------------
Date: Mon, 18 May 92 23:31:57 EDT
From: "Stevan Harnad"
Date: Sun, 17 May 1992 22:16:02 -0400
From: mcdermott-drew@CS.YALE.EDU (Drew McDermott)
dm> We're talking about whether semantic interpretability is part of the
dm> *definition* of computer. For that to be the case, everything the
dm> computer does must be semantically interpretable. Does it cease to be a
dm> computer during the interludes when its behavior is not interpretable?
I doubt that this approach can be made to fly. To start with, I doubt
that it is possible to single out those event sequences that are
computations. (Here Searle or Putnam might have a point.)
Fortunately, we don't have to define "computation" that way. Instead,
we define a "computation system" to be a set of rules that generates
an infinite number of possible behaviors, and then define
"computation" as a behavior generated by a computation system.
("Formal system" is a synonym of "computation system," as far as I can
see.) A computer is then a physical system that implements a
computation system by virtue of a homomorphism from its states to the
states of the computation system. It is not necessary at all that a
computer be able to implement an "arbitrary" computation, although
presumably there are computers that can (modulo disk space).
Actually, there was only one, I thought.
That was it.
I despair of ever making progress on this question without further
empirical progress on computational modeling of thought and behavior.
The ratio of verbiage produced to opinions changed is depressingly
small. I really didn't intend to get drawn in again. I don't promise
I'll be able to resist, however.
Drew McDermott
--------------------------------------------------
Date: Wed, 20 May 92 00:21:45 EDT
From: "Stevan Harnad"
Date: Tue, 19 May 1992 12:28:24 -0400 (EDT)
From: Franklin Boyle
Let me enter the discussion, "What is computation?" at this point by
giving what I believe is a physical constraint on computation and, as
such, part of its definition, which hasn't been openly considered yet.
I haven't seen much in the way of physical criteria, except for the
usual references to causality, which are certainly aimed in the right
direction, but, like Searle's "causal property" hypothesis for the
brain, do not go far enough. (Actually, I had sent a response to the
original post by Stevan about his exchange with Searle, but unless I
missed it, I don't recall having seen it posted -- though, admittedly,
it was very brief.)
[That posting, about Haugeland, appeared Mar 29. -- SH]
With respect to causality, it is not enough to say just that the
"appropriate state-transitional relations are satisfied" [Chalmers,
1992]. Rather, *how* the state-transitional relations are realized must
be accounted for as well. That is, *how* the physical interactions
among the constituent objects of the system in question actually cause
physical changes necessary to go from one state to the next must be
accounted for. *How* effects are brought about is important because
insofar as computations are processes that involve entities we hold to
represent (whether or not they are intrinsically referential), we have
to know that these representing entities are responsible for the changes
we observe _according_to_how_they_represent_what_they_do_ (e.g. through
their forms) in order to be able to call them computations in the first
place. Otherwise, we end up with Putnam's or Chalmers's
characterizations of computation, both of which are mute on the issue of
physical representation, even though they talk about physical states
(unless I'm supposed to be reading a lot more into what they're saying
than I am, such as unpacking the term "state correspondence"
[Chalmers]-- please let me know), and, therefore, admitting too many
systems as computational.
Computation involves a particular kind of physical process. I associate
this process with computation because digital computers happen to
instantiate it, and, if nothing else, digital computers are identified
with computation since they are the physical counterparts of abstract
machine models of computation. Though so-called "analog computers"
exist, they do not physically "compute" the way digital computers do,
and so I will not consider them to be computing just as I would not
consider a planet to be computing its orbit (these systems work
according to nomologically-determined change; see below). The main
difference between analog computers and planets is that the former were
designed by us, and so admit of interpretations that give them a
computational aura.
So, the following is what I consider to be the physical basis of
computation: Computation involves a physical process in which changes
from one computational state to the next (each computational state is a
physical state, of course, though there is a many-to-one relationship
between physical states and computational states [Pylysyhn, 1984]) are
realized through the *physical* process of pattern matching which
consists of the "fitting" of two structures (symbols) and leads to a
"simple" action. (The notion of simple action is originally from Pattee
[1986], but it turns out to be the only way for the form of something to
cause a change that can be attributed to the *entire* form or pattern
[Boyle, 1991; Boyle, 1992].)
A few remarks about this definition. First, the pattern matching
process referred to here is emphasized as being a physical process
because pattern matching is often taken to describe a particular
function, usually pattern recognition. *Functionally*, we are pattern
recognizers as are digital computers, but the physical processes
underlying this functioning are, I believe, different for the two
systems. Digital computers physically accomplish it according to the
above described process. I don't think we do.
What other ways might physical objects cause change besides through
their forms? There are, I claim, only two other ways:
nomologically-determined change and structure-preserving superposition
(SPS). The former refers to the kinds of changes that occur in
"billiard-ball collisions". They involve changes in the values of
measured attributes (properties whose values are numerical, such as
momentum) of interacting objects according to their pre-collisional
measured-attribute values in a physically lawful way (that is, according
to physical laws). Unlike pattern matching interactions, these changes
are not the result of structure fitting.
SPS is what I believe brains use. Like pattern matching (PM), it also
involves extended structure, but in a fundamentally different way.
Whereas PM involves the fitting of two structures, which by its very
nature, leads only to a simple change such as the switching of a single
voltage value from "high" to "low" (in digital computers), SPS involves
that actual *transmission* of structure, like a stone imprinting its
structure in a piece of soft clay. That is, it is not the *form* of a
pattern or structure which must *conform* to the structure of a matcher
in order to effect system functioning (as in PM). Rather, it is the
*appearance* of that structure which causes change because it is
transmitted, so that the effect is a structural formation of the
specific features of the pattern's extended structure (though I won't
elaborate here, the difference between form and appearance is somewhat
akin to the difference between the shadow of an object and the object
itself). Two different structures would physically superimpose to
automatically create a third. Harnad's [1990] symbol grounding
processes -- "analog re-presentation" and "analog reduction" -- I take
to be examples of SPS.
Both PM and SPS are based on extended structure, but they are two
different ways extended structure effects change. PM utilizes extended
structure for control, whereas SPS actually changes structure. If the
physical process of SPS underlies the brain's information processing, it
would make its information processing profoundly different from that of
digital computers. Furthermore, this difference, along with SPS itself,
is, I believe, what Searle is hypothesizing when he refers to "causal
property", even though he doesn't seem to have any idea what it might be.
I refer to the physical processes of nomologically-determined change, PM
and SPS as "causal mechanisms", that is, *how* effects are determined by
their causes. They are based on the physical aspects of objects, of
which there are only two: measured attributes and extended structure. I
take this to be self-evident. Interactions among physical objects
causally involve one or both of these aspects; either as causing change
or being changed themselves. Consequently, I claim there are no other
causal mechanisms, that is, no other ways for objects
to affect each other when they interact.
With respect to computation, the reason the forms of the symbols in an
ungrounded symbol system are superfluous to their functioning is because
in order to function they need another structure (a matcher) which
physically fits them. This means that as long as there *is* a matcher
which physically fits them , it makes no difference what their actual
structures are. Not so for SPS-based systems.
How the notion of systematic interpretability (discussed early on in the
debate) is factored into the above physical constraint on computation in
order to define it is still an issue. Suffice it to say, however, that
whether the symbols in a particular PM system can be given only single
or multiple interpretations, it is the behavior of the system -- how it
interfaces with it's environment -- that matters. Presumably there is
*at least* one interpretation which is consistent with this, so that it
doesn't matter that there happen to be other viable interpretations.
Well, there you have it, though in rather abbreviated form. I plan to
submit follow-up posts targeting specific statements from other posts,
based on what has been said above, in order to achieve the skywriting
flavor the Stevan would like to see (the above is more like a mini
position piece).
-Frank Boyle
--------------------
Boyle, C. F. (1991) On the Physical Limitations of Pattern Matching.
Journal of Experimental and Theoretical Artificial Intelligence,
3:191-218.
Boyle, C. F. (in preparation) The Ontological Status of Mental Objects.
Chalmers, D. (1992) What is Computation? discussion.
Harnad, S. (1990) The Symbol Grounding Problem, Physica D, 42: 335-346.
Pattee, H.H. (1986) Universal Principles of Language and Measurement
Functions In J.L. Casti and A. Karlqvist (eds), Complexity, Language
and Life: Mathematical Approaches, (Springer-Verlag, New York)
Pylyshyn, Z. (1984) Computation and Cognition: Toward a Foundation for
Cognitive Science, (MIT Press, Cambridge, MA).
--------------------
Date: Tue, 19 May 92 23:59:49 EDT
From: "Stevan Harnad"
Date: Fri, 15 May 92 15:20:41 HST
From: Herbert Roitblat
Throughout this discussion, a number of duals, or pairs of related
terms, have appeared. Examination of these duals may be useful in furthering
the discussion. For today's examination please compare and contrast the
following pairs of terms: (1) consciousness and thinking, (2) reference and
grounding, (3) reference and meaning, (4) computer and mind, (5) computation
and thinking, (6) symbolic and analog, (7) introspection and behavior, (8)
mind and formal system.
As has been stated repeatedly the questions under discussion by this
group concern the criteria for deciding whether something is or is not a
computer, and for deciding whether minds are examples of computers. First, I
will attempt to remind us all of the role of crucial criteria, thereby laying
the groundwork for the methodology of my thinking on the question. Then I
will explore the duals mentioned above. Finally, I will attempt to summarize
a response to the questions we are discussing.
Popper (e.g., 1962) argued that what distinguishes science from
nonscience is the use of a falsificationist strategy. He recognized that one
can never PROVE the truth of a conjecture, e.g., there are no black swans,
computers are incapable of thinking; but he did argue that one could DISPROVE
a conjecture. We could disprove the black swans conjecture by finding a
black swan, and we could disprove the computers conjecture by finding or
building one capable of thought. There are two very important problems with
this view. First, every hypothesis or conjecture has attached to it an
implicit ceteris paribus assumption (i.e., all other things being equal).
Proving a conjecture to be false requires that we prove the ceteris paribus
assumption to be true, that is, that there was no contaminating factor that
inadvertently caused the observed results. This is also a conjecture, and we
know that we cannot prove its truth, so therefore, observation can neither
prove nor disprove a conjecture. Second, say that we found a black bird that
seemed to be a swan or found a computer that seemed to think. How do we know
that it actually is a swan (although black) or that it actually thinks?
These are also conjectures and we know that we cannot prove them to be true.
We can apply the Bush Duck Test: if it looks like a swan, and smells like a
swan, and tastes like a swan then it is a swan (the TTT for swanness).
Although we might agree that this creature appears to be a swan, in fact, we
cannot prove it. No matter how many tests we run, the very next test may be
inconsistent with the bird being a swan. In fact, like the rest of the
conjectures, we cannot prove that this test is appropriate and relevant, so
we cannot know for sure that the bird is NOT a swan. The conclusion is that
we cannot know for certain whether a conjecture is true or false. Certainty
is simply unattainable (see Lakatos & Musgrave, 1970; Moore, 1956). The
conclusion for our purposes is that no set of crucial criteria (redundancy
intended) can be specified to decide whether a machine is or is not a
computer or for deciding whether a mind is or is not a computer.
The argument that there can be no proof of any conjectures, including
conjectures of the form: "this is a computer" is very informative regarding
my prejudices in this context. I take the notions about the impossibility of
proof to be central not only to scientific epistemology, but to everyday
epistemology. If our scientific concepts are not so clear-cut and formal,
then how, I argue, can we expect our ordinary concepts to be rationally
based? The notion that concepts can be represented formally, specifically
that thinking involves some kind of proof mechanism seems inconsistent and
inappropriate. It was once thought that logic was worth studying not only
for its mathematical properties but also because logic is the paradigm for
actual human thought. Logic is worth studying, but it is not the paradigm
for the psychology of thought (Kahneman & Tversky, e.g., 1982).
The present discussion is lively, in part because contributors are using
a number of words in subtly (and not so subtly) different ways. The
"groundings" for many of the symbols we use are not shared among contributors
(lacking a shared base of grounded symbols, one might argue, makes us
collectively dysfunctional, thereby demonstrating the necessity of symbol
grounding). Words that are problematic for some of us are used as basic-
level concepts by some of the rest. One of these words is consciousness.
For some individuals, consciousness is used as a synonym for thinking.
For example, Martin Davis wrote:
Whether a TT-passing computer is in any reasonable sense conscious
of what it is doing is not a question we can hope to answer without
understanding consciousness.
Pat Hayes wrote:
A human running consciously through rules, no matter how
'mindlessly', is not a computer implementing a program. They differ
profoundly, not least for practical purposes.
Michael Dyer wrote:
So there is every indication that consciousness is a folk
description for behaviors arising from extremely complex
interactions of a very complex subsystems. There are probably a
VERY great number of variant forms of consciousness, most of them
quite foreign to our own introspective experiences of states of
mind. Then we have to decide if "anyone is at home" (and to what
extent) in gorillas, in very young children, in our pet dog, in a
drugged-out person, etc. etc.
These examples illustrate some of the variety of uses of the concept of
consciousness. There seems to be an implicit claim that to think is to be
conscious. If this is true, then the question of whether a mind is a
computer or whether a computer can be a mind is the question of whether a
computer can have consciousness. Notice that I have equated "having a mind"
and "thinking." I argue for equating mindedness and thinking, but I argue
that consciousness is a red herring. Although Dyer equates consciousness to
some complex behavior, in fact, a behavioral-level description of what
constitutes consciousness is impossible, because of the large number of
behaviors that could be consciousness (or manifestations of it). By a
behavioral-level description, I mean one that is couched in terms of
movements and physical or kinematic descriptions of them.
Another conflation percolating through the discussion involves grounding
and reference. A number of contributors seem to agree that an important
characteristic of minds, if not of real computers, is that the symbols in the
system must be grounded.
For example, Stevan Harnad wrote:
The sensory grounding hypothesis is simply that eventually the
symbolic descriptions can be cashed into terms whose referents can
be pick out from their direct sensory projections.
There are several problems with equating grounding with the ability to
pick out objects from sensory projections. Among these are (1) the
inconsistency in sensory projection that are characteristic hobgoblins of
machine vision, and (2) the use of terms that have no referent, but are
meaningful. Objects, such as birds, are reasonably easily recognized by
humans, despite wide variations in their sensory projections (e.g., in
vision, the optical projection of the light reflected from the object on the
retina). Designing a computer system that can recognize a bird at any
orientation and any condition of flight is extremely difficult. This is an
empirical matter, not an introspection, and recognition of other objects can
be even more difficult. My point in raising this difficulty is not that
computers cannot have vision, but rather to point out that recognizing
objects from their sensory impressions is not trivial, and so is unlikely (I
think) to be a sound basis for our symbols. Pigeons can be trained to
discriminate pictures containing trees in them from pictures that do not
contain trees, but might contain flowers, shrubs, plants, people, logs, etc.
(e.g., Herrnstein, 1984, 1985). It is easier to train the pigeons to
discriminate between such natural categories as trees versus nontrees than it
is to train them to discriminate one arbitrary set of pictures from another.
One psychologist offered as an explanation of this phenomenon that the pigeon
could discriminate tree slides because "they looked like trees," but the
other pictures did not. This putative explanation for the birds' performance
does not even address the issue because it merely restates the observation
without offering any explanation for what constitutes "looking like a tree."
My point is not that we need computers to understand the mind, in this case
how animals or people recognize objects, rather it is that we cannot assume
that biological processes necessarily provide the primitive elements that
will allow us to escape from pure computationalism. To rely on picking out
objects from among some set of alternative objects itself requires
explanation, it is not sufficiently primitive to act as the foundation of the
grounding.
Symbol grounding is apparently intended to assure that symbols are not
just meaningless marks. As Harnad wrote:
. . . systems are just meaningless squiggles and squoggles unless
you project an interpretation . . . onto them.
Many meaningful terms, however, have no referents. These include the
function words, and all the abstract nouns (e.g., furniture, truth, beauty),
as well as certain other conceptual entities. The most famous conundrum
concerning reference and meaning (attributed to Russell, I think) is that
involving the Golden Mountain in the sentence, "The Golden Mountain does not
exist." If it does not exist then how can it be the subject of the
reference? Is the symbol, Golden Mountain, meaningless? A related problem
is that two symbols with the same referent must, then have the same meaning
and must be substitutable for one another. Although the "evening star" and
the "morning star" symbols both refer to Venus, one could believe that the
evening star is really a planet without believing that the morning star is
really a planet, even though they happen to refer to the same object and both
are equally effective at picking out the object in question. Hence,
reference, or the ability to pick out an object to correspond to a symbol, is
not an adequate basis for assigning a meaning to a word. Additionally, some
terms allow us to pick out an object among alternatives, but their semantics
is unrelated to the object in question. For example, if I ask you to get me
a screwdriver, and you do not know which tool I mean, then the phrase "the
yellow one" may allow you to pick out the correct item, but the phrase does
not mean anything having to do with screwdrivers. To understand a sentence,
one must know more than the referent or meaning of the individual words.
Whatever a computer is, attributing to its symbols properties
corresponding to words does not help us to understand what makes those
symbols carry any significant weight because words themselves are not solidly
enough connected to their meanings or to referents. Consider words such as
"tire" that have multiple meanings. One symbol, the string of letters,
requires us to pick out two entirely orthogonal referents, one having to do
with fatigue and one having to do with wheels. As it turns out, many or even
most words in English have multiple meanings or variants of meaning, even if
they are not so distinct as "tire." The word "strike," for example, has more
than 80 meanings listed in my dictionary. Current investigations in my
laboratory suggest that these meanings have family resemblance relations, but
do not share a core of essential conceptual features. For example, strike a
match, strike a blow, strike a bargain, and strike out for Boston, all use
the same symbol, some share the feature that might be labeled (with some
trepidation, see below) "hit" (rapid acceleration resulting in a collision),
but two of them seem completely independent of hitting. The context is
important in determining which meaning or which referent to assign to that
symbol. The symbol is only grounded in context and cannot be treated simply
as a formal object whose use is governed solely by its shape. There may be
some internal symbol that maps onto this surface symbol that is not
ambiguous, but positing such an internal symbol seems to be an ad hoc
adjustment to a hypothesis that relies on external relations to specify the
intension of the symbol (e.g., acting appropriately as in the TTT).
Harnad wrote:
I don't know what the ground-level elementary symbols will turn out
to be, I'm just betting they exist -- otherwise it's all hanging by
a skyhook. Nor do I know the Golden Mountain conundrum, but I do
know the putative "vanishing intersections" problem, according to
which my approach to grounding is hopeless because not even sensory
categories (not to mention abstract categories) HAVE any invariants
at all: My reply is that this is not an apriori matter but an
empirical one, and no one has yet tried to see whether bottom-up
sensory grounding of a TTT-scale robot is possible. They've just
consulted their own (and their subjects') introspections on the
matter. I would say that our own success in categorization is some
inductive ground for believing that our inputs are not too
underdetermined to provide an invariant basis for that success,
given a sufficiently powerful category learning mechanism.
As an aside, it seems to me that investigations of how people actually
use words, as opposed to how a robot might use them is not introspection, but
rather good empirical research. People really do use words in a variety of
ways, they do not merely think that they do. Our words in isolation may be
too underdetermined to provide an invariant basis for success, but words in
context are obviously understood (much of the time). Further, our success at
categorizing is indeed an inductive ground for believing that we categorize,
but it does not imply that categorization occurs on the basis of invariant
features in the sensory projections. There are other approaches to concept
representation and object recognition. Finally, machine vision projects do
indeed attempt to recognize objects based on their sensory projections and
many of them find the job easier when other information is also included.
For many people the word "symbol" denotes a discrete item that can be
used systematically in a formal system. Several contributors, for example,
seem to accept the dichotomy between symbolic systems and analog systems.
Harnad wrote:
Retinal transduction and the analog transformations that follow
from it are computer simulable, they are equivalent to computation,
but they are not eo ipso computational.
In contrast, Mclennan wrote (I think correctly):
My second point is that the nature of computation can be
illuminated by considering analog computation, because analog
computation does away with discrete symbols, yet still has
interpretable states obeying dynamical laws. Notice also that
analog computation can be formal in exactly the same way as digital
computation. An (abstract) analog program is just a set of
differential equations; it can be implemented by a variety of
physical devices, electronic, optical, fluidic, mechanical, etc.
Analog relations are still symbolic in that the state of the analog
system represents the state of the object being represented, but the
relations are more continuous and less arbitrary than those of a strict,
discrete, formal system. There is no reason to believe that human cognition
is based on discrete symbols and there is good evidence that human cognition
is based on more continuous representations that are ad hoc cast into
discrete categories (e.g., Barsalou, 1983, 1987; Labov, 1973). For example,
human performance of many kinds suggests that people confuse items that are
represented similarly more often and more completely than items that are
represented less similarly. The relations between items as remembered is not
arbitrary and formal, but is related to the items as perceived and to the
context in which they were perceived. This is not introspection, this is a
summary of behavior.
Harnad has proposed what he calls the Total Turing Test (TTT) as the
crucial experiment to decide whether a computer can have a mind. His claim
is that a necessary feature for a mind is the ability to interact with the
world both perceptually and behaviorally. Therefore, no artifact can have a
mind unless it can behave in ways that are indistinguishable from the way a
human would behave in the same situation. I hope that it is clear already
that such a test cannot be truly definitive, if for no other reasons than one
cannot prove that there are no differences and because human behavior is not
regular enough to allow any finite experiment to give an adequate test of the
hypothesis, and finally, we do not and, I believe, cannot, have a definitive
catalog of situated human behavior. The best we can hope for from a robot in
a TTTest is that it behaves in a more or less human like manner.
Harnad wrote:
Sensory grounding cannot be investigated by armchair introspection
on word meanings; it will only be understood through empirical
attempts to design grounded systems.
The position that the only way to understand symbol grounding is to
build robots is an interesting one. Building (or simulating) robots is very
useful for investigating a wide range of theories and hypotheses,
nevertheless, despite my support for robotics, it is not the only way to be
empirical about symbol grounding. Data concerning symbol grounding come from
many sources, not just from attempts to build interactive simulations. The
simulations themselves cannot be attempted willy-nilly but must be based on
one or more emerging theories of fundamental intelligence.
Summary of the comments so far: Proof of scientific conjecture is
impossible. No set of observations can ever prove scientific conjecture or
theory to be true. As a result, formal systems do not provide a solid
epistemological basis for scientific nor for everyday concepts. Symbol
grounding has something to do with establishing the semantics or meaning of
the symbols, but reference is a weak method for establishing such semantics.
Symbols need not be discrete, but can be continuous, based on analog
relationships. Finally, formal systems are not a good paradigm for capturing
biological intelligence.
Tearing down someone else's carefully constructed edifice is easy.
Building a substitute that will shelter us from the cold of ignorance is
considerably more difficult. I offer the following suggestions for a
substitute approach to the problem of understanding the relation between
minds and computers. The underlying assumption is that a computer can
convincingly have a mind if it can function more or less along the lines that
biological minds employ. This requires that we understand the biological
minds we are trying to match as well as understand the computational minds we
might try to develop.
Harnad has warned us, to some extent correctly, that any enterprise of
mind construction will be fruitless unless the system's symbols are grounded.
At a minimum, grounded symbols require that the system behave systematically
relative to some environment. I have argued that the methods that have been
suggested so far for symbol grounding are inadequate and to some extent
inappropriate. The paradigms that many of us have adopted for understanding
the mind and computer are inappropriate. There are two parts to my suggested
approach. The first is methodological, and the second is representational.
A number of investigators in recent years have pursued an approach to
understanding intelligence that some of us have come to call the biomimetic
approach. Rather than focus on modeling performance of those tasks that
characterize so-called higher human intelligence, such as planning, problem
solving, scientific creativity, and the like, this approach focuses on the
complementary comparative approach of modeling whole, albeit simple,
organisms in a real environment, performing real biological tasks. The goal
of the approach is to develop coherent incremental models out of functionally
complete components. The more common approach has been successful at
modeling those tasks that humans find difficult and perform slowly (such as
expert chess playing), but which can be described according to specified
rules. The tasks tend to be rather small portions of the whole of human
performance, and to operate on the basis of a limited range of inputs that
are often a restricted set of simple assertions abstracted from real data
(e.g., photos of a scene) by human investigators. Such verbal-like systems
have not been as successful in modeling tasks that humans find easy and
automatic, such as recognizing the face of a friend.
The biomimetic approach seeks to begin with the development of simple
systems that are capable of surviving and operating in a real environment.
The goal is then to gradually scale up the models to wider ranges of
environments and capabilities, at each level producing systems that are
capable of dealing with the essential biological tasks in their environment
(e.g., Brooks, 1991). Similarly, understanding the whole of human behavior
may simply be too complex to be understood without more understanding of the
underlying basic cognitive processes that may be more accessible in animals.
Success in explaining human performance may depend a great deal on
understanding the operation of fairly basic processes because these processes
constrain and support the operations of those so-called higher cognitive
functions. The use of animals and their behavior as systems to be modeled
helps us to see alternative forms of representation that are overshadowed by
our own linguistic capacities and our own introspective self-familiarity. We
are less ready to believe, for example, that animals employ formal rule
systems, than we are to believe that humans employ formal rules.
Nevertheless, both display some level biological intelligence.
The representational part of the approach I suggest is to abandon formal
discrete symbols as the basis for representation and abandon reference as the
means for grounding those symbols. A major reason for wanting to use formal
systems is that the processes by which truth is transmitted and preserved are
well understood in formal systems. Monotonic logic guarantees that truth is
transmitted from a set of premises (grounded symbols) to a set of
conclusions. Abandonment of monotonic logic means that in principle any
conclusions are possible. Nevertheless, monotonic logic seems a very poor
choice for a paradigm of human thought. Just as scientific epistemology
could recognize that there are no guarantees of truth in scientific concepts
without dissolving into irrationality, our models of human thought can
tolerate nonmonotonic logic. My argument is that thought does not operate as
a formal system operating with discrete symbols and proof of syllogisms,
rather it operates as a more or less continuous system operating on the basis
of constraint satisfaction. I will illustrate what I mean by reference to
word understanding, but similar mechanisms can be applied to many forms of
behavior (Chemtob, et al., 1989; Roitblat, 1990).
I argue that concepts and words are represented in a very high-
dimensional space in which the dimensions are semantic. Any given word is
represented as a cloud of points in this space. For example, the word
"strike" is represented along many dimensions corresponding to the different
semantic aspects of the word. Because of the discontinuity from one use of
strike to the next, some representations of the word have positions along a
"hit" dimension that indicate a forceful blow (as in "he struck and killed
the pedestrian") and some have locations that indicate no blow (as in "they
struck a bargain"). (Note that the labels that I can place on these
dimensions are only a poor description of the semantic dimension, because the
labels themselves have multiple and multidimensional meanings and can only
approximate the true underlying dimension.) Obviously these representations
of the word strike are incompatible with one another and people are unlikely
to be able to employ both meanings simultaneously (there is evidence on this
topic). When a person recognizes the word strike, I argue, all of these
dimensions are activated simultaneously, but because they cannot all remain
active, a projection of the representation is performed from the very high
dimensional space onto a space of lower dimensionality. The dimensions that
are selected for the projection are those that are most consistent with the
context. Hence, word understanding is an iterative constraint satisfaction
process in which the meaning of the word that best fits the context is the
one that remains active. What appears to be a circularity problem turns out
to be an iterative problem. The meaning of the words constrain the meaning
of the sentence and the meaning of the sentence constrains the meanings of
the words. The words are not firmly grounded in isolation, but neither are
they merely hung from skyhooks.
An iterative continuous constraint satisfaction system is not guaranteed
to solve the problems of symbol grounding, computation, and artificial
intelligence. No system can offer such a guarantee. Nevertheless, it
appears to offer an alternative path toward solution of such problems.
References
Barsalou, L. W. (1983). Ad hoc categories. Memory and Cognition, 11, 211-
227.
Barsalou, L. W. (1987). The instability of graded structure: implications
for the nature of concepts. In U. Neisser (Ed.), Concepts and conceptual
development: Ecological and intellectual factors in categorization (pp.
101-140). New York: Cambridge University Press.
Brooks, R. A. 1991. Intelligence without representation. Artificial
Intelligence, 47:139-159.
Chemtob, C., H. L. Roitblat, R. S. Hamada, J. G. Carlson, and G. T.
Twentyman 1991. A Cognitive Action Theory of Post- Traumatic Stress
Disorder. Journal of Anxiety Disorders, 2:253-275.
Herrnstein, R. J. 1984. Objects, categories, and discriminative stimuli. In
H. L. Roitblat, T. G. Bever, & H. S. Terrace (Eds.), Animal Cognition (pp
233-262). Hillsdale, NJ: Lawrence Erlbaum Associates.
Herrnstein, R. J. (1985) Riddles of natural categorization. Philosophical
Transactions of the Royal Society (London) B 308: 129-44
Labov, W. (1973) The boundaries of words and their meanings. In C.-J. N.
Bailey & R., W. Shuy (Eds.), New ways of analyzing variations in English.
Washington, DC: Georgetown University Press.
Lakatos, I. & Musgrave, A. (1970) Criticism and the growth of knowledge.
Cambridge: Cambridge University Press.
Kahneman, D. & Tversky, A. (1982) On the study of statistical intuitions. In
D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgements under uncertainty:
Heuristics and biases (pp. 493-508). Cambridge, Cambridge University
Press.
Moore, E. F. (1956) Gedanken-experiments on sequential machines. In C. E.
Shannon & J. McCarthy (Eds.), Automata studies (pp. 129-153). Princeton:
Princeton University Press.
Popper, K. P. (1962) Conjectures and refutations. New York: Harper and Row.
Roitblat, H. L. (1988) A cognitive action theory of learning. In J. Delacour
and J. C. S. Levy (eds.), Systems with learning memory abilities. New
York:Elsevier. pp. 13-26.
Roitblat, H. L. (1990) Cognitive action theory as a control architecture.
In S. Wilson and J. A. Meyer (Eds.), Simulation of Adaptive Behavior from
Animals to Animats. MIT Press, Cambridge, Mass., pp. 444-450.
----------
Date: Wed, 20 May 92 22:18:41 EDT
From: "Stevan Harnad"
Below are 5 more responses to the question about publishing the "What is
Computation" Symposium, for a total of 19 votes cast out of
total of 25 contributors (at the time of the vote):
Publication:
For: 18 // Against: 1
Interactive Symposium (IS) vs. Position Papers (PP):
Either or Combination: 11 - Prefer IS: 5 - Prefer PP: 2
Not yet heard from (6):
(20) Ross Buck
(21) Ronald Chrisley
(22) Gary Hatfield
(23) Joe Lammens
(24) John Searle
(25) Tim Smithers
(and any new contributors since the vote)
I do wish to remind contibutors that we are still in the interactive
symposium phase, no matter what the outcome, so please do NOT send
lengthy position papers yet: Keep it interactive and about the length
most contributions have been thoughout the discussion.
There is also some question about how to partition the two themes
("What is Computation?" and "Is Cognition Computation?") in the
published version, and whether to include the second theme at all.
(I personally think it would be hard to eradicate all traces of
the second question, which is in any case in the back of most of
our minds in all of this). -- Stevan
------------------------------------------------------
(15)
Date: Wed, 13 May 92 16:16:04 PDT
From: dambrosi@research.CS.ORST.EDU (Bruce Dambrosio)
Stevan -
My short comment hardly qualifies me to render an opinion, I'm neutral. I do
hope, however, that the precedent of publication doesn't change the flow of
future discussion. thanks - Bruce
-------------
(16)
Date: Thu, 14 May 92 10:31:07 EDT
From: "John M. Carroll"
stevan
since you registered me as a voter, i'll vote. i'm just an applied
ontologist in the audience of this symposium, but i've found it
interesting (though i do agree with mcdermott -13 may- as to its
wandering itinerary). publishing it as 'position papers' would seem
to me to factor out some of the unique value of interactive debate
(though squeezing out some of the asynchrony and redundancy that come
along with e-mail is probably a good idea). bravo and onward. jack
------------------------
(17)
Date: Fri, 15 May 92 18:31:54 -0400
From: mclennan@cs.utk.edu
Stevan,
Publishing the "What is Computing" dialogue is fine with me. I do think
it will need some smoothing to avoid being too repetitious, but it
should be possible to do.
Bruce MacLennan
Department of Computer Science
The University of Tennessee
Knoxville, TN 37996-1301
--------------------------
(18)
From: sjuphil!tmoody@uu.psi.com (T. Moody)
Date: Mon, 18 May 92 13:36:32 EDT
Stevan,
Publishing this discussion in some edited form is an excellent idea. I regret
that I have not been more active, but I have gotten a great deal out of
reading thing, and I am sure that others would, too.
-- Todd Moody
------------------------------
(19)
Date: Tue, 19 May 1992 21:22:37
From: Pat Hayes
Stevan - yes, Im here , but I am out of touch with email for days at a
time. This will continue for about another two weeks. Sorry. I approve
of the project and will try to get back into it ina few days.
Pat
------------------------------
Date: Wed, 20 May 92 22:52:16 EDT
From: "Stevan Harnad"
Date: Wed, 20 May 1992 17:03:14 -0400 (EDT)
From: Franklin Boyle
Stevan Harnad writes (in response to Dave Chalmers):
Well said. To elaborate, much of the physics of the system depends on
the causal behavior of electric charge. But the combinations of 'high'
and 'low' voltage values that instantiate symbols *control* the
computationally relevant physical changes; e.g., a voltage change which
opens a data path from the starting symbol of a subroutine to a
register in the CPU. The symbols cause these changes as a result of
their structures via pattern matching.
Though each individual voltage value that is part of a symbol's
physical instantiation causes change according to physical laws, the
voltage combinations are able to cause changes AS THOSE STRUCTURES
because of the circuit architectures of electronic devices, such as
comparators. The structures of these devices enable all the individual,
nomologically determined electrical changes taken together to result in
an overall change (e.g., a simple change from a high to low voltage)
which reflects the similarity between the *arrangement* of voltages
that constitutes the structure of the instantiated symbol and that of
its matcher. This latter, overall change is not described by physical
laws. Rather, the regularities such changes generate are described by
rules (because symbols cause change according to their extended
structures) and, hence, underlie the computational behavior of digital
computers.
In most physical systems (enzyme catalysis in cells, for example, and,
of course, digital computers are two exceptions), there is no structure
fitting, only nomologically determined changes in the measured
attributes of interacting objects (e.g., momentum). This is what
happens with planets in their orbits as well as in analog computers.
These systems behave according to REAL causality, as you put it,
precisely because the changes are nomologically determined from the
measured attributes of the interacting objects themselves. In contrast,
computer simulations are simulations because measured-attribute changes
are not the result of the measured attibutes of the interacting objects
(in this case symbols), but, rather, their extended structures, and,
furthermore, because such changes do not affect the physical aspects of
the interacting objects themselves, as they do in the case of
planetary motion. That is, symbols control the manipulation of other
symbols (e.g., moving them around in memory) by controlling changes in
measured attributes (voltages) of particular circuits, but not of each
other.
On the other hand, if symbols do not cause changes in those symbols
through pattern matching, then they are not affecting system behavior
by virtue of their forms, and, so, the system would simply not be a
computer in that case. Thus, planets in orbits are not computers.
If we describe digital computers as we would any other physical system,
that is, in terms of physical state descriptions, it would relegate the
structures of the symbols piecemeal to boundary conditions (like
physical laws, these boundary conditions are associations between
measured attributes -- state variables and structural quantities). Such
descriptions, therefore, would miss the fact that certain voltage
changes are the result of structure fitting, and, hence, would not
capture the computational aspects of digital computers because they
would not capture the causality due to the symbols.
Searle's statement, "syntax is not intrinsic to physics", summarizes
quite nicely the fact that physical state descriptions, which are the
standard way of describing the world because they are about measured
attributes of physical objects which physical laws associate, do not
capture the structural causality of syntactic structures; that is,
structures which are causal via pattern matching. In other words, the
physical behavior of computers can be described by a set of
integro-differential equations and constraint equations without ever
having to account for the causality of extended structure in any
explicit way.
Great. Actually I would have changed the first sentence to: "...that
computation can simulate anything that can be described...", precisely
because digital computers enable descriptions (usually in the form of
patterns which can be structurally matched by rule antecedents) to be
causal.
Franklin Boyle
--------------------------------------------------------------
Date: Tue, 19 May 92 20:57:02 EST
From: David Chalmers
There are too many deep issues here to treat them in any anywhere near
the depth they deserve, but here goes. I'll start with computation and
move up upwards through the rarefied heights of cognition, semantics,
and qualia.
(1) WHAT IS COMPUTATION?
A planetary system is not a computer, because it's not universal. I
should also note that computation requires counterfactual sensitivity
to various different possible inputs, and it's not clear what will
count as an "input" to the solar system. But apart from that worry,
there's no problem with saying that the solar system is implementing
any number of specific computations, e.g. the trivial 1-state FSA as
well as a lot of cyclic n-state FSAs. It's probably not implementing a
calculus-of-variations computation, as such a computation would require
a particular kind of state-transitional structure that this system does
not embody. (There may be some sense in which the system is "I/O"
equivalent to such a computation, but computational equivalence
requires more than I/O equivalence, of course.)
Remember that it's not a problem for my view that every system
implements some computation or other. What matters is that every system
does not implement *every* computation.
As for continuity or discreteness, that depends on the computational
formalism that one uses. Certainly all of the usual formalisms use
discrete states. Of course, a continuous physical system (like the
planetary system) can implement a discrete computation: we just have to
chop up its states in the right way (e.g. divide an orbit into 4
discrete quadrants).
This certainly isn't an objection to my construal of computation. It's
*computation* that's implementation-independent, not, e.g.,
solar-system-hood. The fact that the solar system might be implementing
a computation is not affected by the fact that other implementations of
that computation aren't solar systems.
Most properties in the world don't supervene on computational
structure, as they don't even supervene on causal organization. To be a
process of digestion, for instance, more than a certain causal
organization is required: what's also needed is a specific kind of
physio-chemical makeup. This physio-chemical makeup is *conceptually
constitutive* (in part) of something's being digestion. Similarly for
solar systems. It's conceptually constitutive of solar-system-hood that
a system have a certain geometric shape, a certain chemical makeup, a
certain size, and so on, and these physical properties are not
determined by abstract causal organization. Take a system that shares
abstract causal organization with a solar system -- the Bohr atom, say,
or a boy swinging a bucket around his head -- then it's still not a
solar system, because it lacks those extra properties that are
constitutive of solar-system-hood. So no one would dream of being a
computationalist about solar-system-hood, or about digestion.
The strong-AI hypothesis is that unlike these properties, *cognition*
is a property that supervenes on abstract causal organization. This may
or may not be obvious at first glance, but note that unlike digestion
and solar-system-hood, it's not ruled out at first glance: there
doesn't seem to be any physical property independent of causal
organization that's conceptually constitutive of cognition.
In general, computational simulation will succeed at most in
duplicating those properties that supervene on causal structure. We can
argue all day about whether cognition is such a property, but the
important point here is that pointing to properties that don't
supervene on causal structure is no objection to my construal of
computation.
Not at all. I mean that every implementation of a given computation has
a *real* *causal* *structure*, and in fact that there's a certain
causal structure that every implementation of a given computation
shares. That's precisely what the definition of implementation
guarantees. When a given 2-state FSA is implemented on my computer, for
instance, there are real physical state-types in the implementation
such that being in state A causes a transition into state B, and vice
versa. When a neuron-by-neuron simulation of the brain is implemented
on my computer, there are real physical states (registers, or memory
locations, or whatever) in the implementation corresponding to the
state of each neuron, and these states interact with each other in a
causal pattern isomorphic to a pattern of interaction among the
neurons.
To clarify, by "causal structure" I mean, roughly, *organizational*
properties of a system: i.e., the patterns of interactions between
various states, without taking into account what those states actually
are. For instance an atom, at least according to the Bohr model, might
share some causal structure with the solar system, but it differs in
many properties that aren't organizational properties, such as size,
mass, and intrinsic physical structure.
This has to be kept quite separate from questions about semantics. I
haven't yet even mentioned any possible associated "semantics" of the
computation. And again, I wouldn't dream of claiming that a simulation
of the solar system has the same *gravitational* properties as the
solar system, or that a simulation of the brain has the same
*biochemical* properties as the brain, and I don't know why you think
this is implied by my position. Gravitation and biochemistry don't
supervene solely on causal structure, obviously.
(2) COMPUTATION AND COGNITION
We now pass briefly to the question of whether *cognition* might be a
property that supervenes on causal structure, and on computational
structure in particular.
Well, no, at least not the way I'm using "causal structure". Given any
specification of the causal structure of the brain -- even all the way
down to atoms, or whatever -- then that causal structure could in
principle be implemented in a different medium, such as silicon.
We'd just have to set it up so that our little bits of silicon are
interacting with each other according to the same patterns as the
neurons, or the atoms or whatever, were interacting with each other.
(Of course the silicon model might be a lot bigger than the brain,
and it might have a lot of *extra* causal structure that the brain
doesn't have, but that's not a problem.) Now a neurophysiological
identity theorist would certainly say that the silicon system wouldn't
have the same mental states. So the way I'm using the term (and I
think this is standard usage), a neurophysiological identity theorist
would not agree that mental states supervene on causal structure.
Perhaps you don't agree that mental states depend solely on causal
structure either, because you seem to assign an essential role to I/O
transducers, and presumably it makes a difference just what kinds of
physical things -- heat, light, or whatever -- are being transduced.
Whereas a strict functionalist like myself would hold that at least
when it comes to fixing phenomenal mental states, the specific physical
nature of what's being transduced is irrelevant. On this view, a system
that merely reproduced the causal organization of the transduction in a
different medium would have the same phenomenal properties.
As I said in the last note, even if one accepts (a) that computational
structure fixes causal structure (which follows from my construal of
implementation), and (b) that causal structure fixes mental structure,
there still arises the question of whether computational structure can
fix the right *kinds* of causal structure that are responsible for
mentality. I think that it can: we just have to capture the causal
structure of the brain, say, at a fine enough level of description, and
describe that causal structure in an appropriate computational language
-- as a finite state automaton, for instance, though preferably as an
FSA with combinatorially structured states. Then every implementation
of that FSA will share that causal structure. Some people might hold
that *no* finite level of description can capture everything that's
going on, due e.g. to the potential infinite precision in continuous
systems, but I think that the presence of background noise in
biological systems suggests that nothing essential to cognition can
ride on that infinite precision.
Before passing on to the next topic, I should note that I don't think
that "Is cognition computation?" is quite the right question to ask.
The right question, rather, is "Is computation sufficient for
cognition?" An advocate of strong AI might reasonably hold that
cognition in the brain is not itself computation, but that computation
is nevertheless capable of reproducing the relevant properties (e.g.
causal structure) on which cognition depends. This becomes particularly
clear when we move to specific computational formalisms, such as Turing
machines. I certainly don't think that the brain is a Turing machine,
but I think that nevertheless Turing machine computations are capable
of cognition. It's a subtle point, but too often advocates of AI are
saddled with unnecessary claims such as "the brain is a computer", or
"the mind is a program".
(3) COMPUTATION AND SEMANTICS
The question is only whether semantic content is itself *constitutive*
of something's being a computation. To that question, the answer seems
obviously to be no. Construct an arbitrary large Turing machine by
throwing together quadruples randomly. It's most unlikely that there
will even *be* a nontrivial semantic interpretation for this. Construct
a Pascal program by making random decisions consistent with the BNF
specification of the language. Almost certainly, this program won't be
interpretable as being about anything at all. Nevertheless, it's still
a *program*, and an implementation of it is still *computation*, at
least according to standard usage, which I think is the right usage.
It's probably not a very interesting computation, but it's
computation.
Most interesting computations will probably turn out to have some kind
of semantic interpretation -- otherwise why would we bother with them?
(Actually, some interesting computations might not, e.g. those
computations invoked in solving the "Busy Beaver" problem for Turing
machines. These computations are interesting, but the interest appears
to lie entirely in their syntax. Similarly, many cellular automata
computations, like Conway's game of life, are interesting primarily for
their syntactic form.) But the notion that lies at the foundation of
the computationalist view about cognition is not "interesting
computation", it's "computation" straight. Making some sense of the
notion of "interesting computation" is an interesting question in its
own right, but it's independent of Searle's original question about
what makes something a computation.
(4) QUALIA AND SEMANTICS.
Now we move away from computation to the nitty-gritty philosophical
questions about different kinds of mental properties. Unlike the issues
about computation and implementation (which are surprisingly
underrepresented in the literature), these issues already have a vast
philosophical literature devoted to them. What I'll have to say here
won't be particularly original, for the most part. It's also more
philosophically technical than the earlier parts, so some readers might
want to drop out now (if they've made it this far, which is unlikely).
To set out the lay of the land, I agree that there's only one mind-body
Problem worthy of a capital P, and that's the problem of qualia. That's
not to say that qualia are the only kind of mental states: as I
outlined in my last post, there are also "psychological states", those
characterized by their role in the production of behaviour rather than
by their phenomenal feel. However, there's no more a mind-body Problem
about these than there is a "life-body Problem", for instance. The very
fact of a system's being alive is a fact about it's incorporating the
right kinds of mechanism and producing the right kind of behaviour
(where the key behaviour and mechanisms are adaptation, reproduction,
and metabolism, more or less). There's no "further fact" that needs
explaining. The same goes for psychological states. What's special
about qualia, and makes them seem unlike almost everything else in the
world, is that there seems to be a further fact in need of explanation,
even after one has told the full story about the mechanisms and so on.
Where I differ from you is in assimilating intentional states like
beliefs to the class of psychological states, rather than to the class
of phenomenal states. *All there is* to the fact of a system believing
that P is that it has the right kind of causal economy, with mechanisms
that tend to produce P-appropriate behaviour in the right sort of ways,
and that are causally related to the subject matter of P in the right
sort of way. The possession of semantic content isn't a further fact
over and above these mechanisms: it *conceptually supervenes* on the
existence of those mechanisms, to use the philosophical parlance.
One might parody this argument by saying:
If there were nothing it was like (subjectively) to be alive, there
would be no difference between systems that were just systematically
interpretable AS IF they were alive (extrinsic life) and systems that
were REALLY alive (intrinsic life).
Obviously, any system that is functioning in the right way is not
just "as if" alive, it's really alive, qualia or no qualia. The
same goes for belief. Maybe this means that there's not much
difference between "as if" believing and "real" believing, but why
should that bother us? We don't worry about a difference between
"as if" tables and "real" tables, after all.
(There does remain one "as if" vs. "real" distinction, which is that a
system might *behave* as if it believes that P without believing that P
(actors do this, for instance, and Block's fabled Humongous Lookup
Table might do it even better). But this problem can be handled without
invoking qualia: functionalism requires that to determine the facts
about a belief, one must invoke not only the facts about behaviour but
the facts about patterns of internal causation. The patterns of
causation in actors and lookup tables don't qualify. Spelling out the
right criteria on internal causation is a long, intricate story, but
qualia don't need to be invoked anywhere.)
Qualia or no qualia, beliefs are still "intrinsic" (modulo questions
about narrow and wide content), in just the same way that life is
intrinsic. It's just that they're not *phenomenal*.
The fundamental problem with making qualia essential to semantic
content is that qualia seem to be *the wrong kind of thing* to
determine that content (except perhaps for certain kinds of perceptual
content). As I said earlier, my belief about Joan of Arc may have some
associated (though hard to pin down) qualia, but it's very difficult to
see how those qualia are *constitutive* of the semantic content of the
belief. How could the *feel* of the belief possibly make it any more
about Joan of Arc than it would have been otherwise?
Your position, I take it, is roughly that: "as if" semantic content
*plus* qualia *equals* "real" semantic content. My position is that
qualia seem to contribute almost nothing to fixing the semantic content
of most beliefs, except perhaps for certain perceptual beliefs. So
whatever it is that is constitutive of "real" semantic content, qualia
don't play much of a role. This may mean that there won't be much of a
"real"/"as if" distinction to worry about (modulo the considerations
about behavioural equivalence), but that's life.
Well, I'm only saying that this is a *conceptual* possibility, which
surely it is on your view and mine, not an empirical possibility.
I have little doubt that as an empirical fact, any system physically
identical to me will have the same qualia. But it's entirely coherent
to *imagine* a system physically identical to me but lacking qualia.
Indeed, if it wasn't for first-person knowledge of qualia, one would
never suspect that such a brain-structure would have qualia at all!
(Note that someone (like Lewis or Armstrong, or Dennett on one of
his less eliminativist days) who holds that all there is to the
*concept* of qualia is the notion of a state that plays a certain
causal role won't accept this. But this view simply seems to
legislate the problem of qualia into something else entirely.)
Qualia are individuated by their phenomenal feel, which seems to
be conceptually independent of any physical properties.
So far this view doesn't immediately imply dualism. At least, many
people who take qualia seriously accept this conceptual possibility,
but still think that ontologically, qualia aren't anything over and
above the physical. Personally, I find this view untenable, and
think that the conceptual possibility of absent or inverted qualia
must imply at least a limited kind of ontological dualism (so-called
property dualism), as it implies that there are contingent facts
about the world over and above the physical facts. But let's not go
into that, for now.
I'm not sure exactly what you're missing, but I recommend one of the
standard analytical functionalist papers, like Lewis's "Psychophysical
and Theoretical Identifications" (Aust J Phil, 1972), or even Ryle's
_The Concept of Mind_. As for the TTT, I suggest carefully
distinguishing the *conceptual* from the *empirical* dependence of
mental properties on TTT-function. I take it that you accept empirical
but not conceptual dependence (as you say, it's conceivable that the
TTT might be wrong). By contrast, the analytic functionalist holds that
mental properties are *conceptually* dependent on causal organization
-- i.e. all there is to the notion of a system's being in a mental
state is that it has a certain causal organization, and that it's
appropriately related to the environment. The standard view, I take it,
is that this is an unsatisfying analysis of phenomenal mental states
such as qualia, but that it goes through quite well for most other
mental states, such as beliefs.
Well, I think that it's empirically most unlikely that qualia *would*
fade, as this would mean that phenomenal states and psychological
states were radically "decoherent" from each other, in a subtle sense.
(I have an eternally unfinished paper, "Absent Qualia, Fading Qualia,
Dancing Qualia", on just this topic.) But it's certainly a conceptual
possibility. So given this conceptual possibility, what would I say
about the aboutness? I'd say that it would still be there. What
would it amount to to be wrong about that? The same sort of thing it
would amount to to be wrong about a system's being alive -- e.g., that
one had misanalyzed the functional capacities of the system. Aboutness
is no more of an extra, free-floating fact about a system than life is.
--Dave Chalmers.
---------------------------------------------
Date: Fri, 22 May 92 13:35:56 EDT
From: "Stevan Harnad"
Three basic points characterize my disagreement with David Chalmers:
(1) Computational structure is not the same as causal structure. When
a digital computer simulates an airplane, they are computationally
equivalent but they are not causally equivalent. Causal equivalence
would mean having the same causal powers, in the same "medium" (except
for causally irrelevant implementational differences). An internal
combustion and electric plane would be causally equivalent in their
capacity to fly in the air. A simulated airplane and a real airplane
are not causally equivalent but only formally equivalent (in some
respects).
(2) What makes thinking different from flying is NOT that it
"supervenes" on causal structure the way, say, life might, but that it
is UNOBSERVABLE (or rather, observable only to the thinker). This is
what allows us to forget the differences between simulated thinking
and real thinking in a way that we cannot do with simulated flying
and real flying.
(3) The "aboutness" of thinking is not independent of the question of
qualia, it is completely parasitic on it. A system that has no qualia
has no aboutness, because there is no one home in there for the symbols
to be "about" anything TO.
If a system's symbols are uninterpretable, the absence of aboutness
is fairly obvious.
If a system's symbols are systematically interpretable, as the symbols
in a book or a TT-passing computer are, the lack of aboutness is less
obvious, but only because of the hermeneutic power the interpretation
wields over us; but this interpretability-as-being-about-something is
not grounded in the book or computer but parasitic on the grounded
meanings in the head of the interpreter.
If a system can pass the TTT, then its symbols are grounded, but if
it still lacked qualia, it would still lack aboutness (and we would
have to turn to the TTTT, according to which some TTT implementations
do have minds and some don't).
If a TTTT-indistinguishable implementation still lacked qualia, then it
would still lack aboutness, only the implementations with qualia would
have minds, and dualism would be correct.
Suppose there is a Lisp program that simulates the solar system. Here
is one form of implementation-independence (the kind I mean): That same
program (a recipe for syntactic symbol manipulations) can be run on a
Vax or a Sparc (etc.); the computations are implementation-independent
and all the implementations are both formally and causally equivalent.
Here is another form of "implementation-independence": This is the real
solar system, that is the Lisp program running on a Sparc. They are
both "performing the same computations." These two "implementations"
are just formally, not causally equivalent.
The only thing that sets apart "cognition" (thinking) "at first glance"
from, say, moving, is that moving is observable and thinking is not.
Fortunately, unlike in the case of, say, "living" (about which I think
some of our wrong-headed intuitions may actually have been parasitic on
imagining somebody being at home in there, experiencing), in the case
of thinking we at least have first-person testimony (like Searle's,
when he tells us we would be wrong to conclude from what we could and
could not observe, that he understood Chinese) to remind us that
there's more to thinking than just implemented, syntactic symbol
manipulation.
(With solar systems "computing," I don't know what "computation" is any
more, so let's just talk about properties of all implementations of the
same syntactic symbol manipulations.)
But, alas, among the causal structures shared by the brain and the
neural simulation of it is included neither the requisite causal
structure for passing the TTT (observable) nor (lockstep with the
former, on my hypothesis) the requisite causal structure for thinking
(which happens to be unobservable to all but the [in this case
nonexistent] subject).
It would help if we could speak less abstractly, resorting even to
examples. There are lots of possible patterns of interaction between
states. The only relevant kind for me (in discussing what I, at least,
mean by computation) is the manipulation of symbols purely on the
basis of their shapes, i.e., syntactically, as in a digital computer.
Of course syntactic interactions are independent of semantics (although
the only ones of interest are the ones that are semantically
interpretable).
Nor would I, if this were what "causal structure" was.
I have no problem with synthetic brains, made out of all kinds of
unnatural parts -- as long as they retain the relevant causal powers of
the brain (which for me that is just TTT-power -- for a TTTT-theorist,
further neurobiological causality would matter too, perhaps even
protein synthesis). I don't care what materials a computer is made of.
But if all it does is manipulate symbols on the basis of syntactic
rules, no matter how systematically all those syntactic goings-on can
be equated with and interpretated as what goes on in the brain, nothing
"mental" is "supervening" on it (because the requisite causal structure
has not been duplicated).
All I want to do is refrain from over-interpreting interpretable systems
just because the thinking that they are interpretable as doing happens
to be unobservable. Fortunately, the TTT, which requires real
transduction (otherwise its not the TTT) is observable. Capacity for
interactions with the world is hence part of the requisite causal
structure for thinking.
Let me make it even simpler. Take my position to be equivalent to the
hypothesis that thinking "supervenes" on BEING an optical transducer,
such as a retina. There are many different kinds of optical transducer,
natural and synthetic, but they must all be able to transduce real
light; without that, they simply lack the requisite causal structure to
be a transducer.
(Remember, you, as a computationalist [or "symbolic functionalist"]
hypothesize that thinking "supervenes" on computation/TT-power alone; I,
because of the symbol grounding problem, reject that and hypothesize
that thinking "supervenes" only on hybrid systems with TTT-power ["robotic
functionalism"], and that necessarily includes trandsuction and excludes
computation alone.)
Computation is sufficient but not necessary for cognition? I.e., the
brain may not be a computer, but a computer can still think? Sounds
even more far-fetched than the stronger equivalence claim -- and just
as wrong, for just about the same reasons.
This all seems to be terminological quibbling. Whatever you want to call
the rest, only interpretable computations are at issue here.
What this seems to leave out is why there should be any connection
between thinking and qualia at all! It's also not clear with what
justification you call beliefs "mental" or even "psychological" states.
The reason there's no further fact about "aboutness" is that without
qualia there's no such thing. In a system where there is nobody home,
there's no one for whom anything can be "about" anything. One could still
speak of grounding (TTT-power), because that, like life, does depend
exclusively on observable properties. But in an insentient automaton
there's simply nothing mental to speak of, be it qualitative or
intentional.
Terminology again. If by "intrinsic" you mean the causal substrate of
beliefs is located only in the head, I agree; if you mean it's just
computational, I don't. The life analogy, invoked by so many, is simply
irrelevant (except inasmuch as vitalism was always parasitic, knowingly
or unknowingly, on animism, as I suggested earlier).
Because only qualia would give the beliefs a subject, a believer!
There's both a misunderstanding and an oversimplification here.
TTT-grounding is what "determines the content" of thoughts, both
perceptual and abstract (and it does so in a bottom-up way, so sensory
grounding is primary, and higher-order concepts are grounded in
lower-order ones) [see my earlier replies to Roitblat's objections to
sensory bottom-uppism]. The methodological assumption is that TTT-power
is sufficient for both qualia and aboutness (but this could be wrong);
what's certain is that qualia are a necessary condition for aboutness:
No qualia, no aboutness (just, perhaps, groundedness).
[Although Searle -- with whom I do not necessarily agree in all matters
-- does not seem to have realized it yet, it is the fact that qualia are
a necessary condition for aboutness that makes his own direct testimony
-- to the effect that he does not understand Chinese in the Chinese
Room -- both relevant and devastating to computationalism: There's
something it is "like" to understand, and Searle is in a position to
testify that NO SUCH THING is "supervening" on his own implementation
of the TT-passing computations when he memorizes and executes the
requisite symbol manipulations. The fact that some theorists, like Mike
Dyer, are prepared to believe that under these conditions there would
be another "system" inside Searle, one that WAS actually understanding,
is just evidence of how the unobservability of understanding and the
hermeneutic grip of TT-interpretability can conspire to drive one
further and further into sci-fi fantasies. I think it is for similar
reasons that Pat Hayes is struggling to redefine what counts as an
implementation in such a way as to exclude Searle's memorizization and
execution of the program.]
As I said, qualia don't "fix content" (except in the bottom-up sense
I mentioned), but they are certainly what makes groundedness mental.
A system in which there is no one home has no "beliefs." The conceptual
possibility that the TTT may not be strong enough to guarantee that
someone is home is perhaps fruitful to worry about, because it has
methodological consequences, but the possibility that the TTTT would
not be strong enough is not interesting (to me -- I'm not a
philosopher), because it just amounts to the possibility that dualism
is true.
It's a satisfying analysis of beliefs if you forget that beliefs are
supposed to have a subject.
I agree: It's just byproduct of whatever it is that generates qualia
(I'm betting on TTT-capacity).
Stevan Harnad
--------------------------------------------------------
Date: Fri, 22 May 92 13:56:36 EDT
From: "Stevan Harnad"
Date: Wed, 20 May 92 22:39:48 EST
From: David Chalmers
In reply to Franklin Boyle:
fb> With respect to causality, it is not enough to say just that the
fb> "appropriate state-transitional relations are satisfied" [Chalmers,
fb> 1992]. Rather, *how* the state-transitional relations are realized must
fb> be accounted for as well. That is, *how* the physical interactions
fb> among the constituent objects of the system in question actually cause
fb> physical changes necessary to go from one state to the next must be
fb> accounted for.
I'm open to the idea that certain constraints need to be imposed on the
state-transition relations. For a start, they have to be *causal*, and
there's room for dispute over exactly what that comes to. A minimal
condition is that the conditionals underwriting the relations must
sustain counterfactuals (i.e., they can't be simple material
conditionals), but it's not impossible that more is required.
One can devise some puzzle cases, where some system appears to qualify
as implementing an FSA, say, under this criterion, but where one might
think that it should be ruled out. For example, it turns out that an
implementation of a given FSA, together with a device that simply
records all inputs so far, will implement any I/O equivalent FSA
(proving this is left as an exercise to the reader; the general idea is
to identify the new state-types with the appropriate disjunction of
states of the old system). This kind of case can probably be excluded
by imposing some kind of uniformity requirement on the causal
connections, but the details of this are not entirely clear to me.
That being said, I don't find your specific argument for constraints
on state-transition relations too compelling:
fb> *How* effects are brought about is important because
fb> insofar as computations are processes that involve entities we hold to
fb> represent (whether or not they are intrinsically referential), we have
fb> to know that these representing entities are responsible for the changes
fb> we observe _according_to_how_they_represent_what_they_do_ (e.g. through
fb> their forms) in order to be able to call them computations in the first
fb> place. Otherwise, we end up with Putnam's or Chalmers's
fb> characterizations of computation, both of which are mute on the issue of
fb> physical representation, even though they talk about physical states
fb> (unless I'm supposed to be reading a lot more into what they're saying
fb> than I am, such as unpacking the term "state correspondence"
fb> [Chalmers]-- please let me know), and, therefore, admitting too many
fb> systems as computational.
I agree with this, but I think that my construal of computation is
capable of doing just what you say. I take the fairly standard position
that representing entities represent *in virtue of their causal roles*,
i.e. in virtue of the way that they affect and are affected by other
states of the system, as well as the environment. According to this
view, it doesn't matter precisely how the causation in question is
achieved; all that matters is *that* it is achieved. Similarly, it
doesn't matter *what* the internal states are that are affected; all
that matters is their role in the overall causal economy of the system.
So the criterion I outlined, which is silent on (a) the intrinsic
nature of the states and (b) the specific manner of causation, does
fine.
Of course this definition doesn't say anything explicit about
reference. This is because, as I've said, I don't think that the notion
of reference is conceptually prior to that of computation. Neither, for
that matter, is the notion of computation prior to that of reference.
Rather, I think that both of them should be analyzed in terms of the
prior notion of causation. So we shouldn't expect the definition of
computation (more accurately, of implementation) to say anything
explicit about reference: we should simply expect that it will be
*compatible* with an analysis of reference, whenever that comes along.
Hopefully, given our analyses of computation and of reference, it will
turn out that computational structure determines at least some aspect
of representational power. The analysis of computation I've given
satisfies this, being designed to be compatible with a causal-role
analysis of reference.
Your view seems to be that representing entities represent by virtue of
their internal form, rather than by virtue of their causal role. If
this were the case, then it's possible that this construal of
computation wouldn't be up to the job of fixing representational
powers. However, I don't see any good reason to accept this view. I
agree that the internal form of a representation can be very important
-- e.g. the distributed representations in connectionist networks have
complex internal structure that's central to their representational
capacities. However, it seems to me that this internal form is
important precisely because it *allows* a system of representations to
play the kinds of causal roles that qualify them as representations.
The causal role is conceptually prior, and the internal form is
subsidiary.
--Dave Chalmers.
-----------------------------------------------------------
From harnad Fri May 22 14:10:29 1992
To: harnad@gandalf.rutgers.edu
Subject: Re: What is Computation
Date: Thu, 21 May 1992 14:55:35 -0400
From: mcdermott-drew@CS.YALE.EDU (Drew McDermott)
Subject: Second thoughts
Some second thoughts on "What is Computation?"
(1)
I meant "depressingly large," of course.
(2) I should make it clear that all of the definitions that I (and
Chalmers) have proposed are for *digital* computers. I don't think
anything like that works for analog computers. Indeed, it seems as if
any physical system *can* be considered to be an analog computer, in
that it could be used to make predictions about the behavior of any
other system modeled by the same differential equations (or whatever).
(One might want to add a requirement that the inputs of the system be
controllable, so that it could be used as a computer; but I wouldn't
want the analogous requirement for digital computers, and there are
other problems, so let's skip it.)
(3) Given our definitions, it seems obvious to me that the brain is not
a digital computer -- but we shouldn't hold this against the brain.
The brain's function is to control behavior and model the world.
Digital technology would be the best for these purposes, but the
organic world has had to make do with analog approximations to digital
technology. Perhaps there is an interesting question here about when
we can detect that a nondigital system is an approximation to a
digital one.
Drew McDermott
-------------------------------------------------------
From: Stevan Harnad
There are two dimensions to distinguish: (1) continuous vs. discrete
and (2) "analog" vs. symbolic. The latter is, I think, the relevant
distinction for this discussion. It apposes the analog world of objects
(chairs, tables, airplanes, furnaces, planets, computers, transducers,
animals, people) with that SUBSET of the analog world that consists of
implementations of formal symbol systems, manipulating symbol tokens
purely on the basis of syntax, yet interpretable as describing or
representing anything else in the analog world. This has little to do,
I think, with whether the brain does or does not use digital signal
processing technology.
Stevan Harnad
-----------------------------------------------------
-----------------------------------------------------
Date: Fri, 22 May 92 10:11:15 HST
From: Herbert Roitblat
Stevan Harnad wrote:
I think Stevan has ahold of an important relation here that needs to
be amplified. An electric airplane is not a simulation of an
airplane it is an implementation of an airplane that is causally
equivalent with respect to flying. That is, it is equivalent to a jet
plane in a limited functional domain. It would be silly for us to
argue whether the electric plane is really a plane.
He goes on to say:
Here I begin to disagree. The observability of thinking relates to
our ability to have knowledge about thinking, but it does not
necessarily affect the causal properties of thinking. I also disagree
that thinking is observable to the thinker. Even Freud argued that we
do not have access to much of what we think about. Unless one defines
thinking as "the narrative I" (related to the notion of subvocal
speech), which by definition is narrative and accessible, thinking
occurs at many different levels. Our own awareness of our cognitive
processes is at best unreliable and at worst severely misleading.
More critical in this context, however, is the switch in the nature of
the items being compared. One comparison is between
electric planes and jet planes, the second is between
simulated thinking and real thinking. Whereas it is true that the
simulation is never the thing being simulated, the relation between
computer based and biologically based thinking may be better
characterized as like that between electric planes and jet planes than
as like that between simulated planes and jet planes. Perhaps we
should consider the analogical relation between planes and birds.
Powered flight began in some sense as a simulation of real bird flight
(e.g., DaVinci). At what point did powered flight cease to be a
simulation and begin to be an implementation? Part of what I am
suggesting is an investigation of the implications of assuming that
computers think. If we (perhaps temporarily) entertain the assumption
that computers really think, though perhaps in some computer-like way,
then what do we have to conclude about thinking and computation? Are
there irremediable differences between human thinking and computer
thinking? The argument against computers being capable of
implementing minds can be translated without loss to the argument that
there are certain irremediable differences between the two. One of
these is claimed to be qualia.
It seems to me that the concept if qualia is entirely irrelevant to
the discussion. Qualia are relics of our dualistic past. English is
deeply entrenched in the folk-psychology view of dualism that our very
language practically implies its validity. Qualia are category
errors. The idea of qualia depends on dualistic position that someone
must be home. If we irradicate dualism, then we eliminate any need
for qualia. A monist has no need for qualia only sense data. If we
insist on qualia, it seems to me we prejudge the question of whether
computers can implement thinking, because our dualistic legacy will
not permit us to entertain the notion of someone "being home," that
is, the argument becomes equivalent to asking whether the computer has
a soul.
Devoid of implicit dualism the notion of qualia has no more
to add to the discussion than the concept of witches has to add to
health and illness. Being a committed monist, I have to argue that
there is no one home INSIDE me. I do not have a homunculus, or I am
not a homunculus controlling a body. I think, I am, but I do not
think to myself in the way that the above quotation might suggest. If
there is someone home inside, then we have the familiar problem of
explaining the thinking of the one inside. Does the homunculus have
qualia? Does my body only simulate the intentions of the homunculus?
Herb Roitblat
---------------------------
Date: Mon, 25 May 92 14:54:51 EDT
From: "Stevan Harnad"
METHODOLOGICAL EPIPHENOMENALISM
Herbert Roitblat
hr> The observability of thinking relates to our ability to have knowledge
hr> about thinking, but it does not necessarily affect the causal
hr> properties of thinking. I also disagree that thinking is observable to
hr> the thinker. Even Freud argued that we do not have access to much of
hr> what we think about. Unless one defines thinking as "the narrative I"
hr> (related to the notion of subvocal speech), which by definition is
hr> narrative and accessible, thinking occurs at many different levels. Our
hr> own awareness of our cognitive processes is at best unreliable and at
hr> worst severely misleading.
(1) I didn't say the observability of thinking affects the causal
properties of thinking. I said there is something it's like to think.
Thinking has a subject: The thinker. It is not just an insentient
process that is interpretable (in its structure and its outputs) AS IF
it were thinking. Hence one dead give-away of the fact that there's no
thinking going on, is that there's no thinker to think them.
(2) Of course in the head of a thinker a lot is going on that he is not
aware of! Why should we be aware of everything going on in our heads, or
even most of it, any more than we are aware of most of what's going on
outside our heads? That kind of knowledge has to be gained by honest
scientific toil.
However, let's not forget that all those "unconscious thoughts" happen
to be going on in the head of a conscious thinker! Forgetting this
critical fact is a subtle mistake that is made over and over again, but
I think that on reflection it should become obvious that it is begging
the question [regarding which systems do and do not really think] to
conclude that, because systems like us, that really think, have a lot
going on inside them that they are not aware of, we can therefore speak
of "thinking" in a system that is not aware of anything! [Or worse, that
because we have an Freudian "unconscious mind" in addition to our
conscious mind, other systems could have JUST an "unconscious mind"!]
Until further notice, only systems that are capable of conscious
thoughts are capable of "unconscious thoughts" (which I actually think
is a misnomer in any case, but that's a long story we might be able to
skip for present purposes). It does not even make sense to speak of a
process as "unconscious" when it's going on inside a system that has no
conscious processes: Is a thermostat unconsciously thinking "It's
getting hot in here"? To me, this is all just mentalistic
overinterpretation.
But never mind; perhaps there are relevant similarities between what
goes on in a thermostat or a computer and in my head. Fine. Let's
investigate those: Maybe the similarities will turn out to yield useful
generalizations, maybe not. But let's not prejudge them by assuming in
advance that they are anything more than suggestive similarities. To
claim that thinking is just a form of computation is precisely this kind
of prejudging. If thinking were unobservable in every respect, this
claim would be a normal empirical hypothesis. But since thinking IS
observable to the thinker, this leaves the door to a decisive kind of
negative evidence -- precisely the kind Searle used in pointing out
that he would not be understanding Chinese in the Chinese Room. (By
your lights, he might still be understanding it, but "unconsciously"!)
hr> More critical in this context, however, is the switch in the nature of
hr> the items being compared. One comparison is between electric planes and
hr> jet planes, the second is between simulated thinking and real
hr> thinking. Whereas it is true that the simulation is never the thing
hr> being simulated, the relation between computer based and biologically
hr> based thinking may be better characterized as like that between
hr> electric planes and jet planes than as like that between simulated
hr> planes and jet planes. Perhaps we should consider the analogical
hr> relation between planes and birds. Powered flight began in some sense
hr> as a simulation of real bird flight (e.g., DaVinci). At what point did
hr> powered flight cease to be a simulation and begin to be an
hr> implementation?
The natural/artificial flying analogy has been invoked by
computationalists many times before, and it's just as beside the point
as unconscious thoughts. I can only repeat the structure of the
refutation:
(a) Unlike flying, thinking (or understanding) is unobservable (except
to the thinker/understander who is doing the thinking/understanding).
(b) Searle's Argument shows that a Chinese TT-passing system would NOT
understand (and the symbol grounding problem suggests why not).
(c) Therefore, at least the stronger TTT is required in order to allow
us to continue to infer that the candidate system thinks -- and this
test, like a flight test, cannot be passed my computation alone. Like
flying, it requires a system that is capable of transduction (at least,
and probably many other analog processes as well).
(d) THIS is where the natural/artificial flying analogy IS relevant
(natural thinking: ours, artificial thinking: the TTT-passing robot's).
But computation alone is no longer eligible (because of b and c).
hr> Part of what I am suggesting is an investigation of the implications of
hr> assuming that computers think. If we (perhaps temporarily) entertain
hr> the assumption that computers really think, though perhaps in some
hr> computer-like way, then what do we have to conclude about thinking and
hr> computation? Are there irremediable differences between human thinking
hr> and computer thinking? The argument against computers being capable of
hr> implementing minds can be translated without loss to the argument that
hr> there are certain irremediable differences between the two. One of
hr> these is claimed to be qualia.
If there had been no way of showing that thinking was not really going
on in a computer, then the unobservability of thinking would have left
this forever hopelessly underdetermined (although, unlike the
underdetermination of ordinary physics, there would have been a fact of
the matter: the universe as a whole contains no unobservable fact that
confirms or disconfirms a Utopian physical theory that accounts for all
the observables, but it does contain a fact that could disconfirm a
Utopian cognitive theory -- be it a TT-, TTT-, or even TTTT-scale
account of all the observable -- and that fact would be known only to
the candidate system). But fortunately, in the case of computation that
fact is known to us (thanks to Searle's periscope), and the fact is
that there is no Chinese-understanding going on either in Searle
(unless we are prepared to believe, with Mike Dyer, that memorizing
meaningless symbols can lead to multiple personality disorder) or in
the computer implementing the same program he is implementing (unless
we either abandon the implementation-independence of computation or Pat
Hayes succeeds in finding a nonarbitrary reason for believing that
Searle is not really an implementation of the same program).
So, yes, there might or might not be some helpful similarities between
thinking and computation, but thinking is definitely not just computation.
hr> It seems to me that the concept of qualia is entirely irrelevant to the
hr> discussion. Qualia are relics of our dualistic past. English is deeply
hr> entrenched in the folk-psychology view of dualism that our very
hr> language practically implies its validity. Qualia are category errors.
hr> The idea of qualia depends on dualistic position that someone must be
hr> home. If we eradicate dualism, then we eliminate any need for qualia.
hr> A monist has no need for qualia, only sense data. If we insist on
hr> qualia, it seems to me we prejudge the question of whether computers
hr> can implement thinking, because our dualistic legacy will not permit us
hr> to entertain the notion of someone "being home," that is, the argument
hr> becomes equivalent to asking whether the computer has a soul.
The wrong-headedness of "Cartesian Dualism" is a third theme (along with
unconscious thinking and natural versus artificial flying) often invoked
in support of "new thinking" about cognition. I think "dualism" is being
counted out prematurely, and often with insufficient understanding of
just what the mind/body problem (now supposedly a non-problem) really is
(was). To me it's as simple as the intuition we all have that there is a
difference between a creature that really feels it when you pinch him
and another that doesn't (because it doesn't feel anything, no qualia,
nobody home); we don't need Descartes for that, just the experience we
all share, to the effect that we really have experiences!
The presence or absence of qualia, whether or not someone is home, etc.,
is as relevant or irrelevant to the question of whether a system
thinks or is merely interpretable as if it thinks as the presence or
absence of flying is to the question of whether or not a system can fly.
I would not knowingly put my money on a system that did not have qualia
as a model for the mind. However, when we get past TT/computational
candidates to TTT/robotic (or even TTTT/neural) candidates, where
Searle's Persiscope is no longer available, then I adopt
methodological epiphenomenalism, assuming/trusting that qualia will
"supervene" on the TTT-capacity, and troubling my head about them no
further, since I cannot be any the wiser. What's at issue here,
however, is still pure computationalism, where one CAN indeed be the
wiser, and the answer is: Nobody home.
hr> Devoid of implicit dualism the notion of qualia has no more to add to
hr> the discussion than the concept of witches has to add to health and
hr> illness. Being a committed monist, I have to argue that there is no one
hr> home INSIDE me. I do not have a homunculus, or I am not a homunculus
hr> controlling a body. I think, I am, but I do not think to myself in the
hr> way that the above quotation might suggest. If there is someone home
hr> inside, then we have the familiar problem of explaining the thinking of
hr> the one inside. Does the homunculus have qualia? Does my body only
hr> simulate the intentions of the homunculus?
And the claim that admitting that qualia/consciousness exists would
lead to an infinite homuncular regress is a fourth standard canard.
Sure, people have made (and continue to make) the mistake of thinking
that the inputs to an organism's brain are inputs to a homunculus
inside. The best cure for this is the TTT: The system as a whole must
be conscious, there has to be somebody home in there. But abandon all
mentalism as soon as you address what might be going on inside the
system, and concern yourself only with its capacity to generate
TTT-scale performance, trusting that qualia will piggy-back on that
capacity. Methodological epiphenomenalism, and no homunculus.
Stevan Harnad
--------------------------------------
Date: Sun, 24 May 92 22:24:27 -0400
From: mclennan@cs.utk.edu
Stevan,
My apologies for not replying sooner; I had to preparing a talk and
attend a workshop. You wrote:
I'm afraid I don't understand. I see no reason why we can't have an
analog version of the Chinese Room. Here it is:
Inputs come from (scaleless) moving pointers. Outputs are by twisting
knobs, moving sliders, manipulating joysticks, etc. Various analog
computational aids -- slide rules, nomographs, pantagraphs, etc. --
correspond to the rule book. Information may be read from the input devices
and transferred to the computational aids with calipers or similar analog
devices. Searle implements the analog computation by performing a
complicated, ritualized sensorimotor procedure -- the point is that the
performance is as mechanical and mindless as symbol manipulation. Picture
an expert pilot flying an aircraft simulator. We may suppose that this
analog room implements a conscious cognitive process no more farfetched than
understanding Chinese, viz. recognizing a human face and responding
appropriately. For concreteness we may suppose the analog computation
produces the signal "Hi Granny" when presented with an image of Searle's
grandmother. (My apologies to John and his grandmother.)
As in the traditional CR, the values manipulated by Searle have no
*apparent* significance, except as props and constraints in his complicated
dance. That is, Searle qua analog computer sees the analog values as
meaningless pointer deflections and lever positions. However, with the aid
of an interpreter (such as he would also need for the Chinese symbols) he
might see the same analog signal as his grandmother's face.
It appears that "seeing as" is central to both the digital and analog
cases. Does Searle see his performance as a syntactic (meaningless) ritual
or as a semantic (meaningful) behavior? That the locus of the distinction
is Searle is demonstrated by the ease with which his experience of it -- as
meaningful or meaningless -- can be altered. It might be so simple as
pointing out an interpretation, which would trigger a "Gestalt shift" or
phenomenological reorientation, and allow these quantities and computations
to be seen as saturated with meaning. Searle's experience of meaningfulness
or not depends on his phenomenological orientation to the subject matter.
Of course, mixed cases are also possible, as when we engage in (discrete or
continuous) behaviors that have *some* significance to us, but which we
don't fully understand. (Many social/cultural practices fall in this
category.)
Finally, as a computer scientist devoting much of his effort to analog
computation (1987, in press-a, in press-b), I am somewhat mystified by the
critical distinction you draw between digital and analog computation. What
convinces you that one is REAL computation, whereas the other is something
else (process? pseudo-computation?)? If I'm not doing computer science
please tell me what I am doing!
I hope these comments shed some light on the nature of computation
(whether analog or digital), and symbol manipulation (whether discrete or
continuous).
Bruce MacLennan
REFERENCES
MacLennan, B. J. (1987). Technology-independent design of neurocomputers:
The universal field computer. In M. Caudill & C. Butler (Eds.),
Proceedings, IEEE First International Conference on Neural Networks (Vol.
3, pp. 39-49). New York, NY: Institute of Electrical and Electronic
Engineers.
MacLennan, B. J. (in press-a). Continuous symbol systems: The logic of
connectionism. In Daniel S. Levine and Manuel Aparicio IV (Eds.),
Neural Networks for Knowledge Representation and Inference. Hillsdale,
NJ: Lawrence Erlbaum.
MacLennan, B. J. (in press-b). Characteristics of connectionist knowledge
representation. Information Sciences, to appear.
-----------------------------------
Date: Mon, 25 May 92 16:59:49 EDT
From: "Stevan Harnad"
ANALOG SYSTEMS AND WHAT'S RIGHT ABOUT THE SYSTEM REPLY
Bruce McLennan wrote:
bm> I see no reason why we can't have an analog version of the Chinese
bm> Room. Here it is: Inputs come from (scaleless) moving pointers. Outputs
bm> are by twisting knobs, moving sliders, manipulating joysticks, etc.
bm> Various analog computational aids -- slide rules, nomographs,
bm> pantagraphs, etc. -- correspond to the rule book. Information may be
bm> read from the input devices and transferred to the computational aids
bm> with calipers or similar analog devices. Searle implements the analog
bm> computation by performing a complicated, ritualized sensorimotor
bm> procedure -- the point is that the performance is as mechanical and
bm> mindless as symbol manipulation.
I'm not sure whether you wrote this because you reject Searle's argument
for the discrete symbolic case (and here wish to show that it is equally
invalid for the analog case) or because you accept it for the discrete
symbolic case and here wish to show it is equally valid for the analog
case. In either case, I'm glad you brought it up, because it gives me
the opportunity to point out exactly how simple, decisive and
unequivocal my own construal of Searle's Argument is, and how clearly it
applies ONLY to the discrete symbolic case:
The critical factor is the "System Reply" (the reply to the effect that
it's no wonder Searle doesn't understand, he's just part of the system,
and the system undersands): The refutation of the System Reply is for
Searle to memorize all the symbol manipulation rules, so that the
entire system that gets the inputs and generates the outputs (passing
the Chinese TT) is Searle. This is how he shows that in implementing
the entire symbol system, in BEING the system, he can truthfully deny
that he understands Chinese. "Le Systeme, c'est Moi" is the refutation
of the System Reply (unless, like Mike Dyer, you're prepared to
believing that memorizing symbols causes multiple personality, or, like
Pat Hayes, you're prepared to deny that Searle is really another
implementation of the same symbol system the TT-passing computer
implements).
But look at what you are proposing instead: You have Searle twisting
knobs, using analog devices, etc. It's clear there are things going on
in the room that are NOT going on in Searle. But in that case, the
System Reply would be absolutely correct! I made this point explicitly
in Harnad 1989 and Harnad 1991, pointing out that even an optical
transducer was immune to Searle's Argument [if anyone cared to
conjecture that an optical transducer could "see," in the same way it
had been claimed that a computer could "understand"], because Searle
could not BE another implementation of that transducer (except if he
looked with his real eyes, in which case he could not deny he was
seeing), whereas taking only the OUTPUT of the transducer -- as in your
example -- would be subject to the System Reply. It is for this very
same reason that the conventional Robot Reply to Searle misfired,
because it allowed Searle to modularize the activity between a
computational core, which Searle fully implemented, and peripheral
devices, which he merely operated: This is why this kind of division
of labor (computation doing all the real cognitive work, which is then
linked to the world, like a homunculus, via trivial transducers) is such
a liability to computationalism. [I've always thought computationalists
were more dualistic than roboticists!]
So you have given me the chance to state again, explicitly, that
Searle's Chinese Room Argument and the Symbol Grounding Problem apply
ONLY to discrete formal symbol systems, in which the symbols are
manipulated purely syntactically (i.e., by operations based only on the
symbols' "shapes," which are arbitrary in relation to what they can be
interpreted as meaning) AND where the implementation is irrelevant,
i.e., where every implementation of the symbol system, despite physical
differences, has the same computational properties (including the
mental ones, if cognition is really computation). There is surely some
implementation-independence of analog computation too (after all,
there's more than one way to implement a TTT-scale robot), but that
does not leave room for a Searlean implementation -- at least not one
in which Searle is the entire system. Hence transduction, analog
computation and the TTT are immune to Searle's Argument (as well to as
the Symbol Grounding Problem, since such systems are not just
implemented symbol systems in the first place).
bm> Picture an expert pilot flying an aircraft simulator. We may suppose
bm> that this analog room implements a conscious cognitive process no more
bm> farfetched than understanding Chinese, viz. recognizing a human face
bm> and responding appropriately... As in the traditional CR, the values
bm> manipulated by Searle have no *apparent* significance, except as props
bm> and constraints in his complicated dance. That is, Searle qua analog
bm> computer sees the analog values as meaningless pointer deflections and
bm> lever positions. However, with the aid of an interpreter (such as he
bm> would also need for the Chinese symbols) he might see the same analog
bm> signal as his grandmother's face.
This may be true, but unfortunately it is irrelevant to anything that's
at issue here (just as it's irrelevant whether Searle could eventually
decrypt the Chinese symbols in the original Chinese Room). If the rules
of the game allow the system to be anything but Searle himself, all
bets are off, for by that token Searle could even "be" part of the real
brain without understanding anything -- the brain as a whole, the "system"
would be doing the understanding -- as many critics of Searle have pointed
out (but for altogether the wrong reason, erroneously thinking that
this fact refutes [or is even relevant to] Searle's original argument
against discrete symbolic computation!). I hope this is clearer now.
bm> It appears that "seeing as" is central to both the digital and analog
bm> cases. Does Searle see his performance as a syntactic (meaningless)
bm> ritual or as a semantic (meaningful) behavior? That the locus of the
bm> distinction is Searle is demonstrated by the ease with which his
bm> experience of it -- as meaningful or meaningless -- can be altered. It
bm> might be so simple as pointing out an interpretation, which would
bm> trigger a "Gestalt shift" or phenomenological reorientation, and allow
bm> these quantities and computations to be seen as saturated with
bm> meaning. Searle's experience of meaningfulness or not depends on his
bm> phenomenological orientation to the subject matter. Of course, mixed
bm> cases are also possible, as when we engage in (discrete or continuous)
bm> behaviors that have *some* significance to us, but which we don't fully
bm> understand. (Many social/cultural practices fall in this category.)
Alas, to me, all these "Gestalt flips" are irrelevant, and the
symbolic/analog distinction is the critical one.
bm> Finally, as a computer scientist devoting much of his effort to analog
bm> computation (1987, in press-a, in press-b), I am somewhat mystified by the
bm> critical distinction you draw between digital and analog computation. What
bm> convinces you that one is REAL computation, whereas the other is something
bm> else (process? pseudo-computation?)? If I'm not doing computer science
bm> please tell me what I am doing!
bm>
bm> I hope these comments shed some light on the nature of computation
bm> (whether analog or digital), and symbol manipulation (whether discrete or
bm> continuous).
Unfortunately, rather than shedding light, this seems to collapse the
very distinction that a logical case can be built on, independent of
any mentalistic projections. I can only repeat what I wrote in response
to your earlier posting (to which you have not yet replied):
There is still the vexed question of whether or not neural nets are
symbol systems. If they are, then they are subject to the symbol
grounding problem. If they are not, then they are not, but then they
lack the systematic semantic interpretability that Fodor & Pylyshyn
(1988) have stressed as crucial for cognition. So nets have liabilities
either way as long as they, like symbols, aspire to do all of cognition
(Harnad 1990); in my own theory, nets play the much more circumscribed
(though no less important) role of extracting the sensory invariants in
the transducer projection that allow symbols to be connected to the
objects they name (Harnad 1992).
Stevan Harnad
Fodor, J. & Pylyshyn, Z. (1988) Connectionism and cognitive
architecture: A critical analysis. Cognition 28: 3 - 71.
[also reprinted in Pinker & Mehler 1988]
Harnad, S. (1989) Minds, Machines and Searle. Journal of Theoretical
and Experimental Artificial Intelligence 1: 5-25.
Harnad, S. (1990) Symbols and Nets: Cooperation vs. Competition.
S. Pinker & J. Mehler (Eds.) (1988) "Connections and Symbols."
Connection Science 2: 257-260.
Harnad, S. (1991) Other bodies, Other minds: A machine incarnation
of an old philosophical problem. Minds and Machines 1: 43-54.
Harnad, S. (1992) Connecting Object to Symbol in Modeling Cognition.
In: A. Clarke and R. Lutz (Eds) Connectionism in Context Springer Verlag.
MacLennan, B. J. (1987). Technology-independent design of
neurocomputers: The universal field computer. In M. Caudill & C.
Butler (Eds.), Proceedings, IEEE First International Conference on
Neural Networks (Vol. 3, pp. 39-49). New York, NY: Institute of
Electrical and Electronic Engineers.
MacLennan, B. J. (in press-a). Continuous symbol systems: The logic of
connectionism. In Daniel S. Levine and Manuel Aparicio IV (Eds.),
Neural Networks for Knowledge Representation and Inference. Hillsdale,
NJ: Lawrence Erlbaum.
MacLennan, B. J. (in press-b). Characteristics of connectionist
knowledge representation. Information Sciences, to appear.
Pylyshyn, Z. (1984) Computation and Cognition. Cambridge MA: MIT/Bradford
--------------------------------------------
From: "Stevan Harnad"
Date: Mon, 25 May 92 18:57:54 -0400
From: yee@envy.cs.umass.edu (Richard Yee)
SIMULTANEOUS COMPUTATIONS?
(So What is a Computer?)
Can a single physical system simultaneously implement two or more non-trivial
computations? Is there any significant difference between, say, a simple
calculator and a (universally programmable) computer that is running a
program that describes the calculator? Is a calculator a computer? Must a
computer be programmable?
In an earlier posting entitled "DON'T TALK ABOUT COMPUTERS" (Apr 20,
posted Apr 22), I argued that we should avoid using the term "computer"
because it is ambiguous. In his reply entitled "SO WHAT IS COMPUTATION?"
(Apr 22), Stevan Harnad thought I was introducing a non-standard notion of
computation. He began:
My previous submission probably tried to juggle too many concepts in
too small a space. In particular, I wanted to draw attention to TWO
distinctions, not one. The first---the main subject of this message---is the
distinction between *universal* and *non-universal* computation. This is
entirely a standard distinction, drawn from the Theory of Computation. The
second distinction is between formal and non-formal *symbol processing* and
its relationship to the two types of computation. Admittedly, most of the
action surrounds this question, but I will leave it for a future submission.
First-things first.
Harnad concluded:
OK, for the time being let us not cloud the issues with questions about
formal vs. non-formal symbol processing. Unfortunately, technical formalisms
remain at the heart of the matter. I think that discussions about
computation should refer to the Theory of Computation (ToC). I want to argue
the importance of maintaining a clear distinction between the class of Turing
machines (TM's) and its PROPER SUBCLASS of universal TM's (UTM's). First,
however, I will address Harnad's question about computation.
I. What is Computation?
ToC defines hierarchies of computational classes, where the most notable
class is defined by the capabilities of Turing machines. I endorse the
standard Church-Turing definition of computation (e.g., Lewis &
Papadimitriou, 1981), which roughly states:
"computation" = what any TM does. (1)
Note carefully, it does NOT simply say:
"computation" = what any universal TM (UTM) does. (2)
Definition (1) clearly includes all UTM's, but (2) would not include all
TM's. Computation must encompass what ALL TM's do, not just what universal
ones do. More on this in sections II & III.
As for identifying actual instances of (approximations of?)
computation in the world, I endorse most of David Chalmers' and related
discussions of this subject (NB: with regard to "computation," not
necessarily with regard to "beliefs," "qualia", etc.). In other words, like
any theory or mathematical construct, the Turing machine model (like a
triangle) is a formal abstraction of a portion of human experience.
Subsequent reversing of the model (using it to view the world) involves
finding physical systems that when suitably abstracted, fit the formal model
(e.g., viewing New York, LA, and Miami as forming a triangle). The more
constraints a model places on the world (i.e., the more *predictive* it is),
the more difficult it will be to find a physical system that accidentally
fits the model (i.e., that accidentally satisfies all the predictions. Try
finding cities that form an isosceles right triangle, or an octagon). I take
it that this, or something like it, is the "cryptographic constraint" and/or
the property of "sustaining counterfactuals" which preclude arbitrary
physical systems from implementing arbitrarily complex computations.
Nevertheless, a physical system will often support multiple abstract
descriptions as different TM computations---just as an arrangement of cities
typically can be construed as having several different (irregular)
geometrical shapes. Because the human brain is a complex physical system, it
undoubtedly implements numerous different TM models, all by accident. The
question, of course, is whether there could exist a TM model that could both
(a) "be cognitive" and (b) be implemented by the brain in a physically
plausible way. Condition (a) means being a complete, verifiable, explanatory
model of an individual's cognitive processes, and condition (b) means being a
causal description at the level of biological structures and processes
(neurons, neurotransmitters, evolution?, etc.) and not at the levels of, say,
quantum mechanics or abstract concepts (which would involve "ungrounded
symbols"). The complexity entailed in condition (a) and the
implementational/descriptive constraints of (b) make it vanishingly unlikely
that both could be satisfied through chance.
II. The TM/UTM Distinction
Much of the symbol-grounding discussion, of course, revolves around the
possibility of satisfying condition (a): Could there be a TM model that
would really be cognitive if implemented (by whatever means)? Rather than
trying to answer this question here, I merely want to point out one
*incorrect* way to set about the task. It is incorrect, in general, to
attribute properties of UNIVERSAL TM's (computers?) to ALL TM's.
Harnad asks:
"Universal" basically means "programmable," and most TM's are not
programmable. An example of a non-universal TM is a simple calculator that
performs, say, only addition. It is non-universal because one cannot give it
an input (i.e., a program) that would result in its producing, say,
chess-playing behaviors. [1]
What is NOT a TM computation (universal or otherwise) is a bit
thornier question. TM computations include only discrete-time processes
requiring a finite amount of information processing per time step. Thus, any
process, e.g., an analog one, that REQUIRES (not simply "uses") infinitesimal
time-steps and/or infinite-precision computations (e.g., involving irrational
numbers such as pi) might be a candidate for a non-TM "computation." [2]
Computation thus includes all programmable and non-programmable TM
processes, and it excludes all requirements for infinite information
processing (in time or space), which would presumably exclude analog
processes (if they exist).
III. Some Implications of the TM/UTM Distinction
Suppose one answered the question "What is computation?" with:
"computation" = what any computer does. (3)
This answer would be incomplete without a specific computer model. The two
obvious choices are:
"computer" = TM, (4) => (1)
or
"computer" = (PC, workstation, mainframe, etc.) = UTM. (5) => (2)
If you believe that *programmability* is an essential feature of computers,
then you are leaning toward definition (5), and I think that many people have
such a picture in mind when they talk about "computers." However, I also
suspect that if forced to choose EXPLICITLY between (4) and (5), many would
choose (4) because it is more general, i.e., it corresponds to definition
(1), ToC's definition of computation.
But now we have a problem. Many who think and talk mainly about
programmable computers, UTM's, might implicitly believe themselves to be
discussing all Turing machines. They would not be. UTM's are not universal
for every aspect of computation (in fact, UTM's are quite atypical). One
cannot simply transform arguments regarding UTM's directly into conclusions
about all TM's. THE IMPLICATION DOES NOT FLOW IN THAT DIRECTION.
The distinction between TM's and UTM's should be clear-cut. If so,
then the questions posed at the beginning of this message should be
answerable without philosophical debate.
Q1: Can a single physical system simultaneously implement two or
more non-trivial computations?
A1: Yes. Every UTM, for example, simultaneously implements a unique
universal computation and another arbitrary TM computation, which depends on
an input program. (It would be incoherent to recognize one computation and
not the other because both satisfy exactly the same conditions for being a
true implementation.)
Q2: Is there a significant difference between a simple calculator and
a (universally programmable) computer (a UTM) that is running a program that
describes the calculator?
A2: Yes, the calculator implements ONE non-trivial computation (that
we know of), while the UTM simultaneously implements TWO computations (that
we know of).
Q3: Is a (simple) calculator a computer?
A3: It is a TM, but not a UTM.
Q4: Must a computer be programmable?
A4: A UTM must be programmable. A TM may be non-programmable,
partially-programmable, or universally-programmable (a UTM).
(and continuing...)
Q5: If a UTM is running an unknown program P, can one thereby
guarantee that computation X is NOT occurring?
A5: No. P might describe a TM that implements X. In such a case,
the physical system comprised of the UTM and its inputs, which include
program P, would simultaneously implement the TM and, hence, compute X.
Q5b: What if we *knew* that X is not the UTM's computation? Could we
then guarantee that X is not occurring?
A5b: No, there are two computations to account for (see A5, A1).
I believe that the TM/UTM distinction has some significance for the
debate surrounding the computability of mind. Some of the preceding answers
cannot even be coherently formulated if one is stuck with the single term
"computer." Also, the fact that the Chinese Room (CR) argument is based on a
UTM should give one pause. If it is illogical to generalize from UTM's to
all TM's, then does the CR say anything about the capabilities of TM's in
general? More specifically, does it "prove" anything about non-programmable
(rulebook-less) TM's? If one is interested in all TM's, why talk only about
programmable ones? Finally, even if UTM's process symbols purely formally,
can one thereby conclude that all TM's must be purely formal symbol
processors?
Of course, such issues will draw us from the firm ground of the
Theory of Computation, but in venturing away we should try to maintain as
clear a picture as possible. Instead of thinking and talking about
"computers," I think we should at least consider the ramifications of using,
or of failing to use, the more-precise models of Turing machines and---when
specifically intended---universal Turing machines. The TM/UTM difference is
the difference between being a program and being programmed. I trust that
the former is the issue of interest.
Notes:
----------------------
[1] A TM could be partially programmable without being universal. That is,
I might be able to program a calculator to compute certain formulae, e.g.,
celsius-fahrenheit conversions, but, again, I need not be able to make it
play chess.
[2] Much of Roger Penrose's book (1989) speculates about the necessity of
such infinite information-processing for mental functioning. However, it is
debatable whether such processes could actually "compute something" in any
proper sense, and it is not even clear that such (analog) processes actually
exist.
References:
----------------------
@Book{Lewis-Papadimitriou:81,
author = "Lewis, H. R. and C. H. Papadimitriou",
title = "Elements of the Theory of Computation",
publisher = "Prentice Hall",
year = "1981",
address = "Englewood Cliffs, NJ"
}
@Book{Penrose:89,
author = "Penrose, Roger",
title = "The Emperor's New Mind",
publisher = "Oxford University Press",
year = "1989",
address = "New York",
}
-----------------------------------------------------
From: Stevan Harnad
SO WHAT IS IT THAT EVERY TM DOES?
Richard Yee has done a good job explicating the TM/UTM distinction (and
has even taken some steps toward formulating an analog/TM distinction,
although he thinks nothing may fit the left hand side of that
distinction). I look forward to his next posting, when he tells us what
it is that every TM does ["computation" = what any TM does] (and what,
besides the hypothetical analog systems that he suspects may not exist)
is NOT a TM, and NOT doing what every TM does, but something else (and
what that something else is). (What is to be avoided in all this, I
take it, is making everything a TM, and hence what everything does
computation -- which, it were true, would make the statements like
"Cognition is just Computation" or "The brain is just a TM" just so
many tautologies.) It would also be helpful to explicate the
implication-independence of computation, and just what might or might
not be expected to "supervene" on it.
My hypothesis is that what TM's do is syntax: formal symbol
manipulation (reading squiggles, comparing them with squoggles, erasing
squiggles and replacing them by squoggles, etc.), and that whatever
else might supervene on every implementation of the same computation in
THIS sense, mentality does not number among them. In any case, it is
only formal symbol manipulation (call it something else if
"computation" is not the right word for it) that is vulnerable to
Searle's Chinese Room Argument and the Symbol Grounding Problem.
Stevan Harnad
-----------------------------------------------------
Date: Tue, 26 May 92 12:02:50 EDT
From: "Stevan Harnad"
Date: Tue, 26 May 92 10:44:40 -0400
From: davism@turing.cs.nyu.edu (Martin Davis)
Richard Yee writes emphasizing the importance of the distinction
between arbitrary TMs and universal TMs.
There are some technical problems in making this distinction precise
(having to do with separating the encoding of I/O data to the TM, from
the actual computational process).
Long ago, I wrote on this matter:
``A Note on Universal Turing Machines,'' Automata Studies,
C.E. Shannon and J. McCarthy, editors, Annals of Mathematics Studies,
Princeton University Press, 1956.
``The Definition of Universal Turing Machine,'' Proceedings of the
American Mathematical Society, vol.8(1957), pp. 1125-1126.
Martin Davis
------------------------------
Date: Tue, 26 May 92 23:36:14 EDT
From: "Stevan Harnad"
Date: Tue, 26 May 1992 12:37:14 -0400 (EDT)
From: Franklin Boyle
I would like to preface the following response to Dave Chalmers by
repeating what Stevan, I believe, said about the What is Computation?
discussion in reply to those who cautioned that it was straying from
answering the original question (when it began moving towards
cognition, the Chinese Room, etc.); that what is in the backs of all
our minds is the desire to better understand cognition and, in
particular, whether computation is sufficient to produce it.
Cognitive issues are important to this discussion because cognition is
the result of a rather unique physical system (the brain) that is
causally influenced by its environment as well as interactions among
its consitutent parts. But unlike planetary systems and
airplanes, the brain has some rather remarkable properties;
in particular, its intrinsic capacity for reference. Now this is not
a property of planets or solar systems or any other system we know of.
So if we define computation such that planets can be construed as
implementing some computation and, therefore, that they are computing,
as Chalmers maintains, then we had better make sure we understand the
nature of representation in such systems and how representing entities
are causal, that is, how they relate to the "particular kind of
state-transitional structure" [Chalmers] that serves as the basis for
calling a solar system an implementation of a particular computation.
Why? Because that is how the analogous argument
goes for the brain as implementing a particular computation, and,
thus, whether or not cognition is computation. But in this latter
case, we have to account for intrinsic reference.
In other words, when we define computation, we had better be explicit
about its physical characteristics because when we come to the question
about the brain as a system which implements a particular computation,
then whatever definition we've produced has to bear the full weight of
being able to account for such things as an intrinsic referential
capacity. If we allow any physical system to be an implementation of
some computation, we will most likely end up with little in the way of
principled criteria for determining whether cognition is computation.
>From *my* perspective, the brain is not implementing a computation just
as planets in orbits are not, but for a different reason; because of
structure-preserving superposition (SPS), instead of nomologically
determined change (as occurs in planetary systems), as the causal
mechanism for how physical change associated with its information
processing is primarily brought about (see my previous postings).
Both are fundamentally different from pattern matching, which, I claim,
underlies computation.
Issues about consciousness, qualia, etc. should be part of another
discussion on mind and brain, but symbol grounding and even the Chinese
Room (unless it veers off toward arguments about multiple
personalities, etc.) should be part of the "What is Computation?"
discussion because they involve issues of causality and representation
which are fundamental to computation. They also represent attempts to
get at the kinds of things the "state transitional structure" of some
computer program is going to have to support, especially in the case of
the brain; e.g., "understanding" that comes about presumably because of
referential characteristics of the representation.
To reiterate, if computation is going to be considered capable of
producing cognition, then its state transition structure, or causal
"organization", as Chalmers puts it, is going to have to explain this
capacity. And, frankly, I don't believe this functionalist criterion
alone can handle it. You need more than causal organization.
I therefore feel that the bifurcated discussions should, at least when
the question of publication arises, be merged, with editing, under the
"What is Computation?" heading.
> Date: Fri, 20 Mar 92 8:47:13 EST
> From: Herb Simon
> To: Stevan Harnad
> Subject: Re: What is computation?
>
> A non-trivial symbol system is a symbol system that can be programmed to
> perform tasks whose performance by a human would be taken as evidence of
> intelligence.
>
> Herb Simon
> Date: Fri, 20 Mar 92 08:56:00 EST
> From: "John M. Carroll"
> Subject: What is computation?
>
> Ref: Your note of Wed, 18 Mar 92 14:20:51 EST
>
> through a surprising quirk of intentionality on the part of the
> internet, i got copied on an exchange between you and john searle.
> thanks! it was related i think to my own ontological worries --
> you're interested in whether and how objects can be interpreted as
> computations or their implementations, i'm interested in whether
> and how designed artifacts can be interpreted as theories of their
> intended human users, or as implementations of those theories. since
> i've already eavesdropped, how did searle reply to your 'duals' idea?
> cheers
> Date: Sun, 22 Mar 92 17:07:49 -0500
> From: hatfield@linc.cis.upenn.edu (Gary Hatfield)
> To: harnad@Princeton.EDU, searle@cogsci.Berkeley.EDU
> Subject: Re: What is computation?
>
> Stevan: I don't see how you respond to John (Searle)'s point about observer-
> relativity. Indeed, your distinction between between trivial and
> nontrivial symbol systems appeals to an "intended interpretation,"
> which would seem simply to supply fuel for his fire. And your point
> about the wall being an implementation of a "merely trivial"
> computation is not clearly established: it depends on how you
> individuate the wall's computational states. John's claim (which is
> similar is some respects to one that Kosslyn and I made in our _Social
> Research_ paper, 1984, pp. 1025-6, 1032, and to Putnam's discussion in
> the appendix to his _Repn and Reality_) is that some state-assignment
> could be found for the wall in which it was performing any computation
> that you like, including any NONTRIVIAL computation (of course, the
> assignment might carve out physically arbitrary bits of the wall and
> count state transitions arbitrarily, from a physical point of view).
>
> Stephen and I, in our paper, contended that in order to avoid this sort
> of move the symbolist must argue that brains have non-arbitrary
> functional architectures, and that the functional architecture of our
> brain is so organized that it nonarbitrarily instantiates a serial
> digital computer. We then offered reasons for thinking that the brain
> doesn't have such an architecture.
>
> The crux of the matter is the status of function assignments. One might
> say that digital computers nonarbitrarily have a von Neumann
> functional architecture by offering a theory of the functions of
> artifacts according to which such functions are assigned relative to
> the intentions of designers or users. That might make sense of the
> intuition that commercial digital computers "intrinsically" are digital
> computers, though it wouldn't answer John's objection, because it still
> appeals to intentions in the function- assignment. But if one argued
> that there are natural functions, that biological systems
> nonarbitrarily instantiate one function rather than another, then the
> symbolists could claim (as I think Fodor and Pylyshyn did) that certain
> biological systems are naturally organized to compute with an
> architecture similar to that of digital comuters. John denies that
> there are natural functions. However, for his "observer-relativity" and
> "no intrinsic computations" arguments to have bite, he must do more
> than simply assert that there are no natural functions. Indeed, for the
> purpose of arguing against the computationalists, it would seem that he
> should offer them a choice between no-natural-functions and a trivial
> theory, and natural-functions but an empirically false theory.
>
> Best, Gary
> Date: Mon, 23 Mar 92 00:34:56 -0500
> From: hatfield@linc.cis.upenn.edu (Gary Hatfield)
>
> The claim that there are parsimonious and unpars. ways to "make sense"
> of input-output relations has long seemed to provide support for the
> belief that objective features of particular physical systems
> (computers, organisms) nonarbitrarily constrain content-ascriptions to
> those systems (e.g., Haugeland's Intro to _Mind Design_). The problem
> is that such arguments typically take as given some non-physical
> description of the inputs and outputs. Thus, Haugeland starts with
> "tokens," and in your reply to me you start with a "string of symbols"
> typed by a monkey. That doesn't speak to Searle's (or my) concerns, for
> one wants to know by what (non-observer-dependent) criteria you
> individuated the symbols. You need to argue that the physical state
> instantiating the symbols in the case of an actual Shakespeare play
> (including, say, the whole booklet; you aren't "given" symbol
> boundaries) has an internal coherence lacking in a given
> monkey-produced text *from a strictly physical point of view*. Here
> intuitions may diverge. But in any case, it seems to me that the
> defenders of non-trivial computation and non-arbitrary interpretation
> have the burden of starting their arguments from physical descriptions,
> without taking symbols for free.
> Date: Sun, 22 Mar 92 21:58:42 HST
> From: Herbert Roitblat
>
> HR: You're right we do disagree on a lot. But, then, I knew that when I
> signed on to this discussion. What suprised me, however, is that I
> misunderstood what we disagreed about even more than I thought.
> I do not understand the following:
>
> >SH: implements a nontrivial symbol system, and what makes a symbol system
> >nontrivial is that it can bear the weight of one systematic
> >interpretation (the standard one, and in a few special cases, some
> >provable nonstandard ones).
>
> HR: I suspect you mean something special by "one systematic
> interpretation," but I do not know what you mean.
> >SH: I think a grounded symbol system is one in which the interpretations
> >of its symbols do not just square with what is in the mind of us
> >outside interpreters, but also with what the system does in the real
> >world.
>
> HR: I agree with most of this, but I think that it does not matter whether
> the system's symbols are interpretable or not, thus it does not matter
> whether they square with our expectations. I entirely endorse the
> idea that what is maximally important is what the system does.
> >SH: The nontrivial grounded symbol system that interests me is the robot
> >that can pass the Total Turing Test (behave indistinguishably from
> >ourselves).
>
> HR: This is one source of our disagreement. I agree that the Turing test
> establishes a very high level of nontriviality, but I think that it is
> too high a level to be useful at this stage (a strategic issue) and
> is so high a level that it excludes much of what I find interesting.
> I would be happy with a system that MERELY (!?) passed the Turing test
> to the level of an ant or a rat or something like that. Why not just
> a gecko? I don't think you mean that only humans are nontrivial
> computers. I cannot hope to live up to such standards in order to
> enter the discussion. I am still basically a comparative psychologist
> with interests in psycholinguistics.
>
> By the way, "trivial" is a conceptually dangerous term. When we fail
> to understand something it is nontrivial. Once we understand it, it
> becomes trivial.
> >SH: We disagree even more on categories. I think the Roschian view you
> >describe is all wrong, and that the "classical" view -- that categories
> >have invariant features that allow us to categorize in the all-or-none
> >way we clearly do -- is completely correct.
> >And the categories of interest
> >are all-or-none categories like "bird," not graded ones like "big."
>
> HR: This is a fundamental disagreement. It seems to me that your intent
> to focus on the most clearly classical cases derives from your belief
> that classical cases are the paradigm. Variability from the classical
> case is just "performance error" rather than competence. Am I correct
> on this last point?
> HR: Bird is no less trivial than mammal, but we are faced with the
> question of whether monotremes are mammals. Living things are an all
> or none category. Are viruses living things? The question is not
> whether you believe viruses to be living things, you could be
> mistaken. Are they living things in the Platonic sense that classical
> theory requires? Bachelor is another classic category. Is a priest a
> bachelor? Is someone cohabiting (with POSSLQ) a bachelor? Is an 18
> year old unmarried male living alone a bachelor? Is a homosexual male
> a bachelor? What are the essential features of a bachelor and can you
> prove that someone either does or does not have them?
> The classic conceptualization of concepts is tied closely to the
> notion of truth. Truth can be transmitted syntactically, but not
> inductively. If features X are the definition of bachelor, and if
> person Y has those features then person Y is a bachelor. One problem
> is to prove the truth of the premises. Do you agree that the symbol
> grounding problem has something to do with establishing the truth of
> the premises?
> The truth of the premises cannot be proved because we have no
> infallible inductive logic. We cannot prove them true because such
> proof depends on proving the truth of the implicit ceteris paribus
> clause, and just established that proof of a premise is not possible.
> We cannot be sure that our concepts are correct. We have no proof
> that any exemplar is a member of a category. I think that these
> arguments are familiar to you. The conclusion is that even classic
> categories have only variable-valued members, even they cannot truly
> be all-or-none.
> I think, therefore, that we are not justified in limiting discussion
> to only those categories that seem most clear, but that we would be
> served by developing a theory of conceptual representation that did
> not depend on artificial demarcations. I argue for a naturalistic
> theory of categories that depends on how people use conceptual labels
> (etc.). I argue that such use depends on a certain kind of
> computation, that given enough time, people could endorse a wide range
> of categorizations. The range of categorizations that they can
> endorse is the range of dimensions for which they represent the
> concept as having a value. My hunch is that the number of these
> dimensions that can be used at any one time for performing a given
> task is small relative to the number of dimensions that they know
> about for the concept. You seems more interested in characterizing
> the range of dimensions along which people can use their concept, I am
> more interested in the way in which they select those dimensions for
> use at the moment.
> Finally, I have been thinking about symbol grounding in other
> contexts. Exactly what symbols do you think are grounded in human
> representation? It cannot be letters because no semantics is
> attributed to them. It cannot be words, because we understand
> paraphrases to mean the same thing, the same word has multiple
> meanings, etc. It cannot be sentences, because we are productive in
> our use of sentences and could utter an indefinite number of them.
> The symbols would have to be internal, variably mappable onto surface
> symbols, and as such, not communicable with great confidence to other
> individuals. You would argue (yes?) that they are finite and discrete,
> but highly combinable. You would not argue, I think, that they get
> their meaning through their reference to some specifiable external
> object or event (i.e., you would not get into the Golden Mountain
> conundrum). Is symbol grounding nothing more than whatever relationship
> allows one to avoid unpleasant consequences of misunderstanding and
> misclassification (your allusion to Skinner)?
> By the way, I am sorry for constantly putting words into
> your mouth, but it seems to me to be an efficient way to finding out
> what you mean.
> >SH: Introspections about how we categorize are irrelevant (did we expect
> >introspection to do our theoretical work for us, as cognitive
> >theorists?), as are reaction times and typicality judgments.
>
> HR Introspections play no role in my conceptualizaiton. You must have me
> confused with someone else. I am not even sure that I am conscious,
> let alone capable of introspection.
> If this discussion is heading off in a direction irrelevant to your
> interests, we can wait for another more opportune time. I think that
> our discssion has taken roughly this course: What is computation?
> Computation is either any regular state change (my position) or it is
> the set of nontrivial operations involving a grounded symbol set
> (fair?). What is a grounded symbol set?
>
> Aloha. Herb
> Date: Mon, 23 Mar 1992 15:35:43 -0500 (EST)
> From: Franklin Boyle
> To: "Stevan Harnad"
> Subject: Re: What is computation?
>
> The article by Haugeland contains the following text which is relevant to the
> discussion, though it wasn't intended to address the SS issue at hand:
>
> Suppose that, instead of giving the analogy [interpretation],
> I had just spelled out the rules, and then invited you to
> discover the interpretation. That would be a cryptarithmetic
> puzzle, or, more generally, a code cracking assignment. The
> principle of all such efforts, from deciphering ancient inscript-
> tions to military cryptography, is finding a consistent reading
> such that the results reliably *make sense* [footnote to Quine].
> This requirement is by no means trivial or easy to meet; there
> are, for instance, no semantic interpretations attached to the
> chess or checkers systems. Hence, though an interpretation is
> never properly part of a formal system, the structure of a system
> strongly constrains possible interpretations; in other words, the
> relation between a formal system and a viable interpretation is
> not at all arbitrary.
> -- (p27, "Artificial Intelligence and the Western Mind.
> in _The Computer and the Brain: Perspectives on
> Human and Artificial Intelligence_, J.R. Brink &
> C.R. Haden (eds).)
>
> He is speculating about original meaning (rather than the difference
> between trivial and non-trivial SS's) and the fact that computers
> represent a technological solution to the "Paradox of Mechanical
> Reason" ["either meanings matter to the manipulations, in which case
> reasoning is not really mechanical (it presupposes an homunculus); or
> else meanings do not matter, in which case the machinations are not
> really rational (they are just some meaningless "machine-like"
> interactions" (p23)] because they "take care of the formalism" so that
> "any meaning that can be taken care of by taking care of the rules will
> be *automatically* taken care of by the computer -- without any paradox."
> (p27).
>
> -Frank Boyle
> From: Ronald L Chrisley
> Date: Wed, 25 Mar 92 16:13:22 GMT
> To: harnad@Princeton.EDU
> Cc: chrisley@oxford.ac.uk, dave@cogsci.indiana.edu
>
> Stevan:
>
> RC: Here are some of my thoughts on the first part of your recent message.
> Could you provide the context for this exchange between you and
> Searle? Did this dialogue take place on the symbol-grounding list?
> > Date: Wed, 18 Mar 92 08:12:10 -0800
> > From: searle@cogsci.Berkeley.EDU (John R. Searle)
> > To: harnad@princeton.edu (Stevan Harnad)
> >
> > Subject: Re: "My wall is a computer"
> >
> > JS: Stevan, I don't actually say that. I say that on the standard Turing
> > definition it is hard to see how to avoid the conclusion that
> > everything is a computer under some description. I also say that I
>
> RC: No, actually Searle argues that the standard notion of computation
> implies that everything is *every* computer. Thus, he claims that his
> wall could be seen as implementing Wordstar. But of course, there are
> good reasons for ruling out such bizarre interpretations: for one,
> they're not causal.
>
> > JS: think this result can be avoided by introducing counterfactuals and
> > causation into the definition of computation. I also claim that Brian
> > Smith, Batali, etc. are working on a definition to avoid this result.
> > But it is not my view that the wall behind me is a digital computer.
>
> RC: Nor is it anyone else's view. That's because the standard view is
> that the physics *does* constrain computational interpretations. If
> it isn't the explicit standard view, it is implicit in the notion of a
> Turing *machine*. And if Searle wants to contest that it isn't even
> implicit, then his arguments only establish the superiority of a
> theory of computer science that is physically grounded, *not* the
> incoherence of the notion that a particular form of computation is the
> essence of mind.
> > JS: I think the big problem is NOT universal realizability. That is
> > only a SYMPTOM of the big problem. the big problem is: COMPUTATION IS AN
> > OBSERVER RELATIVE FEATURE. Just as semantics is not intrinsic to syntax
> > (as shown by the Chinese Room) so SYNTAX IS NOT INTRINSIC TO PHYSICS.
> > The upshot is that the question : Is the wall (or the brain) a
> > digital computer is meaningless, as it stands. If the question is "Can
> > you assign a computational interpretation to the wall/brain?" the
> > answer is trivially yes. you can assign an interpretation to anything.
>
> RC: This kind of equivocation is the reason why I delineated 3 ways in
> which one might understand the claim "the brain is a computer".
>
> One is that it admits of any computational description at all. If
> this were the extent of the claim for cognitive science, then it is
> indeed unenlightening, since even a stone could be seen as being a
> Turing Machine with only one state.
>
> A second way of interpreting the claim is that there is a class of
> Turing Machine descriptions that are sufficiently complex that we
> would consider them as descriptions of computers, more conventionally
> understood, and that the brain, as opposed to a stone or Searle's wall
> (they just don't have the right properties of plasticity, input/output
> connections, causally related internal states, etc), admits of one of
> these descriptions.
>
> A third way of understanding the cognitivist claim is: the brain
> admits of a computational description, and anything that has a mind
> must also admit of a similar computational description. This is not
> vacuous, since most things, including not only stones and Searle's
> wall, but also bona fide computers, will not admit of such a
> description.
> > JS: If the question is : "Is the wall/brain INTRINSICALLY a digital
> > computer?" the answer is: NOTHING is intrisically a digital computer.
> > Please explain this point to your colleagues. they seem to think the
> > issue is universal realizability. Thus Chrisley's paper for example.
> RC: I think that we can make sense of the notion of something
> intrinsically being a digital computer. Searle's argument that we
> cannot seems to be based on the claim that anything can be seen as a
> computer. In that sense, the issue for Searle *is* universal
> realizability. That is, Searle seems to be claiming that since the
> property *digital computer* can be realized by any physical system,
> then nothing is intrinsically a digital computer, and so viewing the
> brain as one will have little value.
>
> I disagree, of course, and on several counts. For one thing, on the
> second way of understanding the claim that something is a computer,
> the property *digital computer* is not universally realizable. But
> even if everything were *some* kind of digital computer (on the first
> or second ways of understanding), that would not invalidate the
> computational approach to understanding the mind, since that approach
> seeks to understand what *kind* of computation is characteristic of
> the mental (the third way). In fact, it would be of some use to
> cognitive science if Searle could show that everything is some kind of
> computer, because there are some critics of cognitive science who
> argue that the brain cannot be viewed as a computer at all (Penrose?).
>
> Searle's only options are to endorse the coherence of the cognitivist
> claim (I am not claiming that it has been shown to be true or false,
> just that it is coherent and non-trivial), find another argument for
> its incoherence, or deny my claims that causality is relevant to
> computational interpretation, thus suggesting that cognitivism is
> vacuous since every physical system can be interpreted as being every
> kind of computer. And even if he argues that causality is irrelevant
> to a *particular* style of computational interpretation, he has to
> show that it is irrelevant to any notion of computation before he can
> rule out any computational approach to mind as being incoherent. Put
> the other way around, he would have to show that a notion of
> computation that takes causality seriously would ipso facto not be a
> notion of computation. This seems impossible. So it looks like
> Searle must try to reject cognitivism some other way, or accept it.
>
> I tried to make all this clear in my paper. Due to publisher's
> delays, there are still chances for revisions, if anyone would like to
> suggest ways that I could make these points more clear.
>
> One last thing: given the reluctance that some AI/CompSci/CogSci
> people have to taking causality, connections to the world, etc.
> seriously, I welcome and encourage Searle's points in some sense. I
> just wish he would see his arguments as establishing one type of
> cognitivism (embodied) to be prefereable to another (formal).
>
> Much of what people do in AI/CompSci/CogSci is the former, it's just
> their theories of what they are doing that are the latter. I think
> the point of Searle's paper is not "Cognitivism is incoherent" but
> rather "If you want to be a cognitivist, your theories better take
> seriously these notions of causality, connections to the world, etc.
> that are implicit in your practice anyway".
>
> Perhaps Searle's points, cast in a different light, would not give
> people reason to abandon cognitivism, but would instead show them the
> way toward its successful development. As I said in my paper, "Searle
> has done us a service".
>
> Ronald L. Chrisley New College Oxford OX1 3BN
> Date: Tue, 31 Mar 1992 21:41:36 PST
> From: Pat Hayes
> Subject: Re: What is computation?
> To: Stevan Harnad
> Cc: chrisley@oxford.ac.uk
>
> Stevan-
>
> >SH: It seems to me that everything admits of a trivial computational
> >description.
>
> I have heard others say similar things, and Searle obviously believes
> something similar. Can you explain what you mean by this, and why you
> believe it? I cannot think of any sensible interpretation of this
> remark that makes it true. -- Pat
> Date: Sun, 29 Mar 1992 21:40 EDT
> From: DDENNETT@PEARL.TUFTS.EDU
> Subject: Re: Cryptographer's Constraint
> To: harnad@Princeton.EDU
>
> I'm not sure when I FIRST discussed the cryptographers' constraint and I
> don't remember whether John McCarthy spoke of it before I did, but probably,
> since in True Believers (1981, reprinted in THE INTENTIONAL STANCE, 1987)
> I cite McCarthy 1979 when I mention it (p.29fn in TIS). By the way, the
> point can also be found in Descartes!!
> DAN DENNETT
> Date: Tue, 31 Mar 1992 23:22:44 PST
> From: Pat Hayes
>
> Stevan-
>
> OK, I now see what you mean: but what makes you think that calling the
> state of the stone '0' has anything to do with computation? A computer
> is a mechanism whose behavior is determined (in part) by the symbols
> stored in it. But the behavior of the stone and the door are not
> influenced in any way by the 'symbols' that this exercise in
> state-naming hypothesises. So they aren't computers.
>
> Perhaps I am reaching towards what you are calling nontrivial
> computation: but it might be less confusing to just call this
> computation, and call 'trivial computation' something else, might it
> not? What motivates this trivialisation of the computational idea?
>
> Pat
> Date: Wed, 1 Apr 92 09:59:32 -0500
> From: davism@turing.cs.nyu.edu (Martin Davis)
>
> Stevan,
>
> Thanks for keeping me posted on this debate.
>
> I don't really want to take sides; however, there is technically no real
> problem in distinguishing "non-trivial" computers. They are "universal"
> if endowed with arbitrarily large memory.
>
> I've written two papers (long long ago) on the definition of universality
> for Turing machines. The first was in the McCarthy-Shannon collection
> "Automata Studies." The second was in the Proc. Amer. Math. Soc.
>
> If you want the exact references I'll be glad to forward them. But you
> may think this not relevant. Martin
> > computer?" the answer is: NOTHING is intrisically a digital computer.
> > Please explain this point to your colleagues. they seem to think the
> > issue is universal realizability. Thus Chrisley's paper for example.
> Date: Wed, 1 Apr 92 18:55:28 -0500
> From: davism@turing.cs.nyu.edu (Martin Davis)
> Subject: Re: What is a computation?
>
> Here are the references:
>
> ``A Note on Universal Turing Machines,'' {Automata Studies}, C.E.
> Shannon and J. McCarthy, editors, Annals of Mathematics Studies,
> Princeton University Press, 1956.
>
> ``The Definition of Universal Turing Machine,'' {Proceedings of the
> American Mathematical Society,} vol.8(1957), pp. 1125-1126.
>
> As for your argument with Searle (which I did try to avoid), my
> tendency is to place the issue in the context of the appropriate
> mathematical [idea] of "computer". I think it is a commonplace among
> philosophers that what appear to be purely empirical questions almost
> always really involve theoretical presuppositions.
>
> The two main contenders are finite automata and Turing machines. I
> suppose anything could be regarded as a finite automaton; I haven't
> really thought about it. But most agree today (this wasn't always the
> case) that it's the TM model that's appropriate. The counter-argument
> that real-world computers have finite memories is answered by noting
> that an analysis that defines a computer as having fixed memory size
> must say what kind of memory (ram? hard disk? floppies? tape?). In
> particular none of the theorems about finite automata have ever been
> applied to computers. If I remember (an increasingly dubious
> proposition) I discussed this in:
>
> ``Computability,'' {Proceedings of the Symposium on System
> Theory,} Brooklyn, N.Y. 1966, pp. 127-131.
>
> I would add (as I suggested in my previous message) that UNIVERSALITY
> is also generally tacitly presumed. This means that the computer can
> run programs embodying arbitrary algorithms.
>
> I think Searle would find it difficult to argue that a rock is a
> universal Turing machine.
>
> It is true that something may be a computer without it being readily
> recognized as such. This is for real. Microprocessors (which are
> universal computers) are part of many devices. Your telephone, your
> thermostat, certainly your VCR are all computers in this sense.
>
> But certainly not a rock!
>
> Here's another (closely related) less theoretical approach:
>
> Make a list of half a dozen simple computational tasks:
>
> E.g.
> 1. Given a positive integer, compute its square root to 5 decimal
> places.
> 2. Given two character strings, produce the string obtained by
> interleaving them, one character from each input at a time.
> 3. Given a positive integer, compute the sum of the positive integers
> less than or equal to the given integer;
>
> etc. etc.
>
> Then ask Searle to explain how to arrange matters so a stone will
> carry out these tasks.
>
> In other words, in order for the term "computer" to be justified, the
> object in question should be able to carry out ordinary computational
> tasks.
>
> Martin Davis
> Date: Tue, 31 Mar 1992 23:25:36 PST
> From: Pat Hayes
> Subject: Re: What is computation?
>
> PS: I recall McCarthy telling me the idea of the cryptographers
> constraint in 1969 when I first came to the USA (or maybe 1971, on the
> second trip). It didn't seem to be such an important matter then, of
> course.
>
> Pat Hayes
> > computer?" the answer is: NOTHING is intrisically a digital computer.
> > Please explain this point to your colleagues. they seem to think the
> > issue is universal realizability. Thus Chrisley's paper for example.
>
> Stevan-
>
> OK, I now see what you mean: but what makes you think that calling the
> state of the stone '0' has anything to do with computation?
> Date: Thu, 2 Apr 1992 16:27:18 -0500
> From: Drew McDermott
>
> Searle means to say that "computers are in the mind of the beholder."
> That is, if I take a system, and wish to view it as performing a
> computational sequence S, I can map the thermal-noise states (or any
> other convenient ways of partitioning its physical states) into
> computational states in a way that preserves the sequence. Putnam makes
> a similar claim in an appendix to, I think, "Representation and
> Reality." A long discussion about this has been going on in
> comp.ai.philosophy.
>
> I agree with Stevan that Searle is wrong, and that computation is no
> more a matter of subjective interpretation than, say, metabolism is.
> However, I differ on where the problem arises...
>
> I don't think it matters one little bit whether the symbols manipulated
> by a computer can be given any meaning at all. As I hope I've made
> clear before, the requirement that computers' manipulations have a
> meaning has been 'way overblown by philosopher types. The real reason
> why not every system can be interpreted as a computer is that the
> exercise of assigning interpretations to sequences of physical states
> of a system does not come near to verifying that the system is a
> computer. To verify that, you have to show that the states are
> generated in a lawlike way in response to future events (or possible
> events). It seems to me that for Searle to back up his claim that his
> wall can be viewed as a computer, he would have to demonstrate that it
> can be used to compute something, and of course he can't.
>
> This point seems so obvious to me that I feel I must be missing
> something. Please enlighten me.
>
> -- Drew McDermott
> a computer is something that is mechanically influenced by its
> internal symbols (though we differ on two details -- I think it is only
> influenced by the SHAPE of those symbols, you think it's influenced by
> their MEANING..
> Date: Fri, 3 Apr 1992 16:31:05 PST
> From: Pat Hayes
>
> You can't run a Turing machine, for one thing, unless its engineered
> properly. (For example, the symbols on the tape would have to be in a
> form in which the processing box could read them, which rules out
> thermodynamic states of walls or rolls of toilet paper with pebbles on,
> and so forth.)
>
> You might respond, well, what IS a computer, then? And my answer would
> be that this is essentially an empirical question. Clearly they are
> remarkable machines which have some properties unlike all other
> artifacts. What are the boundaries of the concept? Who knows, and why
> should I really care very much?
> No, I don't think that the processor has access to anything other than
> the shape of the symbols (except when those symbols denote something
> internal to the machine itself, as when it is computing the length of a
> list: this point due to Brian Smith). I think we agree on this. But
> sometimes that suffices to cause the machine to act in a way that is
> systematically related to the symbol's meaning. All the machine has is
> some bitstring which is supposed to mean 'plus', but it really does
> perform addition.
> For the record, I agree with you about the need for grounding of
> symbols to ultimately attach them to the world they purport to denote,
> but I also think that language enables us to extend this grounding to
> almost anything in the universe without actually seeing
> (feeling/hearing/etc) it, to the extent that the sensory basis of the
> glue is almost abstracted. One could imagine making a program which
> 'knew' a tremendous amount, could 'converse' well enough to pass the
> Turing Test in spades, etc., but be blind, deaf, etc.: a brain in a
> box. I think that its linguistic contact would suffice to say that its
> internal representations were meaningful, but you would require that it
> had some sensory contact. If we gave it eyes, you would say that all
> its beliefs then suddenly acquired meaning: its protests that it could
> remember the time when it was blind would be denied by you, since it
> would not have been nailed down sufficiently to the world then. Ah no,
> you would say to it: you only THOUGHT you knew anything then, in fact I
> KNOW you knew nothing. While I would have more humility.
>
> best wishes Pat Hayes
> From: lammens@cs.Buffalo.EDU (Joe Lammens)
> Subject: symbol grounding
>
> Re: your article on "The Symbol Grounding Problem" in Physica D
> (preprint). If higher-order symbolic representations consist of symbol
> strings describing category membership relations, e.g. "An X is a Y
> that is Z", then who or what is doing the interpretation of these
> strings? They are just expressions in a formal language again, and I
> assume there is no grounding for the operators of that language like
> "is a" or "that is", whatever their actual representation? Even if
> there is, something still has to interpret the expressions, which seems
> to lead to a homunculus problem, or you'll have to define some sort of
> inferential mechanism that reasons over these strings. The latter seems
> to take us back to the realm of "traditional" AI completely, albeit
> with grounded constant symbols (or at least, some of them would be
> directly grounded). Is that what you had in mind? I don't see how in
> such a setup manipulation of symbols would be co-determined by the
> grounded meaning of the constant symbols, as you seem to require.
>
> Joe Lammens
> Date: Mon, 06 Apr 92 11:01:12 ADT
> From: GOLDFARB%unb.ca@UNBMVS1.csd.unb.ca
>
> Stepa,
> I don't know what exactly Searle had in mind, but I also don't see
> anything interesting behind the idea of "computation". Every "object",
> including an "empty space" ("there is no space 'empty of field" --
> Einstein), might be said to perform many computations, depending
> on interactions with various other "objects", some of the computations
> are highly non-trivial.
>
> I think that "intelligent" computation is a more interesting idea to
> pursue: it is a degree to which a "system" is able to modify
> autonomously and irreversibly its INTERNAL states -- not just some
> auxiliary external objects, or symbols, as does the Turing machine --
> that have effect on all the related consequent computations.
>
> Cheers,
> Lev
>dc> an answer to the question of whether "everything is a computer" depends
>dc> on a criterion for when a computer, or a computation, is being
>dc> physically implemented. But fairly straightforward criteria exist...
>dc> the causal structure of the physical system must mirror the formal
>dc> structure of the FSA, under an appropriate correspondence of states...
>dc>
>dc> Given a particular complex FSA -- e.g. one that a computationalist
>dc> might claim is sufficient for cognition -- it will certainly not be the
>dc> case that most objects implement it, as most objects will not have the
>dc> requisite causal structure...
>dc>
>dc> Finite state automata are a weak formalism, of course, and many if not
>dc> most people will want to talk in terms of Turing machines instead. The
>dc> extension is straightforward... For a Turing machine of any complexity,
>dc> this will be a huge constraint on possible implementations...
>dc>
>dc> To be a computer presumably requires even stricter standards -- i.e.,
>dc> that the system be universal. But that is straightforward: we can
>dc> simply require that the system implement a universal Turing machine,
>dc> using the criteria above...
>dc>
>dc> ...there are very straightforward grounds for judging that not
>dc> everything is a computer, and that although it may
>dc> be true that everything implements some computation, that's not
>dc> something that should worry anybody.
>dc> The computationalist claim is that cognition *supervenes* on
>dc> computation, i.e. that there are certain computations such that any
>dc> implementation of that computation will have certain cognitive
>dc> properties.
>dc>
>dc> To the person who says "doesn't this mean that digestion is a
>dc> computation", the answer is yes and no. Yes, a given digestive process
>dc> realizes a certain FSA structure; but this is not a very interesting or
>dc> useful way to see it, because unlike cognition, digestion does not
>dc> supervene on computation -- i.e. there will be other systems that
>dc> realize the same FSA structure but that are not performing digestion.
>dc>
>dc> Personally I think that the notion of "computation" is more central to
>dc> cognitive science than the notion of "computer". I don't see any
>dc> interesting sense in which the human mind is a universal computer...
>dc> Rather, the mind is performing a lot of interesting computations, upon
>dc> which our cognitive properties supervene. So it's probably most useful
>dc> to regard cognitive processes as implementing a given non-universal
>dc> Turing machine, or even an FSA, rather than a universal computer.
> ed> I agree with Searle [that] nothing is intrinsically a computer [and
> ed> that] the big problem is not universal realizability... I agree with
> ed> you that computation and implementation are not the same thing, and
> ed> that nontrivial symbol systems will not have arbitrary duals because
> ed> they have a certain complex systematicity... But, ... Nothing is
> ed> intrinsically a computer because nothing is intrinsically anything.
> ed> It's interpretation all the way down, as it were.
> ed> ...it's lack of imagination that prevents us from swapping
> ed> interpretations in general in English, arithmetic, and Lisp. This lack
> ed> of imagination, though, is part of our epistemic boundedness. We are
> ed> not stupid, just finite. To keep things coherent, and to swap all the
> ed> meanings in English is something that we cannot do. Perhaps no
> ed> intelligent creature could do this because creatures vastly more
> ed> intelligent than we would have that much more science -- explanations
> ed> and semantics -- to juggle when trying to invent and swap duals.
> ed> Still, we arrive at the same point: a wall is only an implementation of
> ed> a trivial turing machine or computation. But, ... How can we arrive at
> ed> the same point if I believe that computers are NOT formal symbol
> ed> manipulators while you and Searle believe that they are? Because
> ed> computation is an observer relative feature precisely *because*
> ed> semantics is. In other words, you can interpret your wall, there just
> ed> isn't much reason to do so. Planets can be viewed as representing and
> ed> computing their orbits, but there isn't much reason to do so. Why?
> ed> Because it involves too much "paper work" for us. Other intelligent
> ed> entities might prefer to attribute/see such computations to the
> ed> planets.
> ed> For me, computation, systematicity, and semantics are matters of
> ed> degree. Machines, computation, and meaning are in the eye of the
> ed> beholder, or more precisely, the explainer.
> ed> What recommends this view? Does it give us exactly the same conclusions
> ed> as your view? No, it is not the same. Interpretationalism provides a
> ed> different set of problems that must be solved in order to build an
> ed> intelligent artifact, problems that are prima facie tractable. For
> ed> example, on the interpretationalist view, you don't have to solve the
> ed> problem of original intentionality (or, what is the same, the problem
> ed> provided by the Chinese Room); nor do you have to solve the symbol
> ed> grounding problem (though you do have to figure out how perception and
> ed> categorization works). You can instead spend your time searching for
> ed> the algorithms (equivalently, the architectures) responsible for our
> ed> intelligence -- architectures for plasticity, creativity and the like.
> ed> interpretationalism holds out the promise that cognitive science will
> ed> integrate (integrate, NOT reduce) smoothly with our other sciences. If
> ed> intentionality is a real property of minds, then minds become radically
> ed> different from rocks. So different that I for one despair of ever
> ed> explaining them at all. (Where, for example, do minds show up
> ed> phylogenetically speaking? And why there and not somewhere else? These
> ed> are questions YOU must answer. I don't have to.)
> Date: Mon, 6 Apr 92 20:02 GMT
> From: UBZZ011@cu.bbk.ac.uk Todd Moody
> To: HARNAD <@nsfnet-relay.ac.uk:HARNAD@PRINCETON.edu>
>
> Another way to ask the question at hand is to ask whether, given some
> alien object that appeared to be undergoing complex changes in its
> discrete state configurations, is it possible to tell by inspection
> whether it is doing computation? (alien means we don't know the
> "intended interpretation," if there is one, of the states) This
> question is rather strongly analogous to a question about language:
> Given some arbitrary complex performance (dolphin noise, for example),
> is it possible to determine whether it is a linguistic performance
> without also being able to translate at least substantial portions of
> it?
>
> In both cases, I don't see how the questions can be answered other than
> by working from considerations of *parsimony under interpretation*.
> That is, in the case of dolphin noise, you just have to make some
> guesses about dolphin interests and then work on possible
> interpretation/translations. When you reach the point that the simplest
> interpretation of the noise is that it means XYZ, then you have a
> strong case that the noise is language. In the case of the alien
> thing-that-might-be-a-computer, the trick is to describe it as
> following a sequence of instructions (or computing a function) such
> that this description is simpler than a purely causal description of
> its state changes.
>
> A description of an object as a computer is more *compressed* (simpler)
> than the description of it as an arbitrary causal system.
>
> Thus, it is parsimony under interpretation that rules out Searle's
> wall. This is not interpretation-independent, but I think it is as
> good as it gets.
>
> Todd Moody (tmoody@sju.edu)
> that what we are
> trying to rule out here is arbitrary, gerrymandered interpretations of,
> say, the microstructure (and perhaps even the surface blemishes) of a
> stone according to which they COULD be mapped into the computations you
> describe. Of course the mapping itself, and the clever mind that
> formulated it, would be doing all the work, not the stone, but I think
> Searle would want to argue that it's no different with the "real"
> computer! The trick would be to show exactly why/how that rejoinder
> would be incorrect. It is for this reason that I have groped for a
> complexity-based (cryptographic?) criterion, according to which the
> gerrymandered interpretation of the stone could somehow be ruled out as
> too improbable to come by, either causally or conceptually, whereas the
> "natural" interpretation of the SPARC running WORDSTAR would not.
>jd> Stevan Harnad wrote:
>
>sh> what we are trying to rule out here is arbitrary, gerrymandered
>
>sh> interpretations of, say, the microstructure (and perhaps even the
>
>sh> surface blemishes) of a stone according to which they COULD be mapped
>
>sh> into the computations you describe. Of course the mapping itself, and
>
>sh> the clever mind that formulated it, would be doing all the work, not
>
>sh> the stone, but I think Searle would want to argue that it's no
>
>sh> different with the "real" computer! The trick would be to show exactly
>
>sh> why/how that rejoinder would be incorrect. It is for this reason that I
>
>sh> have groped for a complexity-based (cryptographic?) criterion,
>
>sh> according to which the gerrymandered interpretation of the stone could
>
>sh> somehow be ruled out as too improbable to come by, either causally or
>
>sh> conceptually, whereas the "natural" interpretation of the SPARC running
>
>sh> WORDSTAR would not.
>jd> One potential problem with the complexity constraint is that the
>jd> interpretations are expressed in a particular language (let us say).
>jd> An interpretation that is more complex in one language might be
>jd> simpler in another. Putnam makes a similar point about his "cats
>jd> are cherries" example, that which interpretation is the weird one
>jd> switches depending on whether you're expressing the interpretation
>jd> in the language where "cats" means cats or the one in which it
>jd> means cherries.
>jd> As a metaphor for this, consider random dot stereograms as an encoding
>jd> technique (something suggested to me by Richard Tobin). Someone mails
>jd> you a picture that consists of (random) dots. Is it a picture of the
>jd> Eiffel Tower, or a Big Mac? Well, they mail you another picture of
>jd> random dots and, viewed together with the first, you see a picture of
>jd> the Eiffel Tower. But they could just as well have mailed you a
>jd> different second picture that, together with the first, gave a Big
>jd> Mac.
>jd> Moreover, it is not true in general that the simpler interpretation is
>jd> always the right one. Someone who is encoding something can arrange
>jd> for there to be a simple interpretation that is incorrect. I suppose
>jd> an example might be where the encrypted form can be decrypted to an
>jd> English text, but the actual message can only be found by taking the
>jd> (English) words that appear after every third word that contains an "a".
>jd>
>jd> Jeff Dalton
>dm> Let's distinguish between a computer's states' being
>dm> "microinterpretable" and "macrointerpretable." The former case is what
>dm> you assume: that if we consider the machine to be a rewrite system, the
>dm> rewrite rules map one coherently interpretable state into another. Put
>dm> another way, the rewrite rules specify a change in belief states of the
>dm> system. By contrast, the states of a macrointerpretable system "sort of
>dm> line up" with the world in places, but not consistently enough to
>dm> generate anything like a Tarskian interpretation. What I think you've
>dm> overlooked is that almost all computational processes are at best
>dm> macrointerpretable.
>dm> Take almost any example, a chess program, for instance. Suppose that
>dm> the machine is evaluating a board position after a hypothetical series
>dm> of moves. Suppose the evaluation function is a sum of terms. What does
>dm> each term denote? It is not necessary to be able to say. One might, for
>dm> instance, notice that a certain term is correlated with center control,
>dm> and claim that it denotes "the degree of center control," but what does
>dm> this claim amount to? In many games, the correlation will not hold, and
>dm> the computer may as a consequence make a bad move. But the evaluation
>dm> function is "good" if most of the time the machine makes "good moves."
>dm> The chess program keeps a tree of board positions. At each node of this
>dm> tree, it has a list of moves it is considering, and the positions that
>dm> would result. What does this list denote? The set of moves "worth
>dm> considering"? Not really; it's only guessing that these moves are worth
>dm> considering. We could say that it's the set the machine "is
>dm> considering," but this interpretation is trivial.
>dm> We can always impose a trivial interpretation on the states of the
>dm> computer. We can say that every register denotes a number, for
>dm> instance, and that every time it adds two registers the result denotes
>dm> the sum. The problem with this idea is that it doesn't distinguish the
>dm> interpreted computers from the uninterpreted formal systems, because I
>dm> can always find such a Platonic universe for the states of any formal
>dm> system to "refer" to. (Using techniques similar to those used in
>dm> proving predicate calculus complete.)
>dm> More examples: What do the states of a video game refer to? The Mario
>dm> brothers? Real asteroids?
>dm> What do the data structures of an air-traffic control system refer to?
>dm> Airplanes? What if a blip on the screen is initially the result of
>dm> thermal noise in the sensors, then tracks a cloud for a while, then
>dm> switches to tracking a flock of geese? What does it refer to in that
>dm> case?
>dm> Halfway through an application of Newton's method to an optimization
>dm> problem involving process control in a factory, what do the various
>dm> inverted Hessian matrices refer to? Entities in the factory? What in
>dm> the world would they be? Or just mathematical entities?
>dm> If no other argument convinces you, this one should: Nothing prevents
>dm> a computer from having inconsistent beliefs. We can build an expert
>dm> system that has two rules that either (a) cannot be interpreted as
>dm> about medical matters at all; or (b) contradict each other. The system,
>dm> let us say, happens never to use the two rules on the same case, so
>dm> that on any occasion its advice reflects a coherent point of view.
>dm> (Sometimes it sounds like a homeopath, we might say, and sometimes like
>dm> an allopath.) We would like to say that overall the computer's
>dm> inferences and pronouncements are "about" medicine. But there is no way
>dm> to give a coherent overall medical interpretation to its computational
>dm> states.
>dm> I could go on, but the point is, I hope, clear. For 99.9% of all
>dm> computer programs, either there is only a trivial interpretation of a
>dm> program's state as referring to numbers (or bit strings, or booleans);
>dm> or there is a vague, unsystematic, error-prone interpretation in terms
>dm> of the entities the machine is intended to concern itself with. The
>dm> *only* exceptions are theorem-proving programs, in which these two
>dm> interpretations coincide. In a theorem prover, intermediate steps are
>dm> about the same entities as the final result, and the computational
>dm> rules getting you from step to step are isomorphic to the deductive
>dm> rules that justify the computational rules. But this is a revealing
>dm> exception. It's one of the most pervasive fallacies in computer science
>dm> to see the formal-systems interpretation of a computer as having some
>dm> implications for the conclusions it draws when it is interpreted as a
>dm> reasoning system. I believe you have been sucked in by this fallacy.
>dm> The truth is that computers, in spite of having trivial interpretations
>dm> as deductive systems, can be used to mimic completely nondeductive
>dm> systems, and that any semantic framework they approximate when viewed
>dm> this way will bear no relation to the low-level deductive semantics.
>dm> I suspect Searle would welcome this view, up to a point. It lends
>dm> weight to his claim that semantics are in the eye of the beholder.
>dm> One way to argue that an air-traffic control computer's states denote
>dm> airplanes is to point out that human users find it useful to
>dm> interpret them this way on almost every occasion. However, the point
>dm> at issue right now is whether semantic interpretability is part of the
>dm> definition of "computer." I argue that it is not; a computer is what
>dm> it is regardless of how it is interpreted. I buttress that
>dm> observation by pointing out just how unsystematic most interpretations
>dm> of a computer's states are. However, if I can win the argument about
>dm> whether computers are objectively given, and uninterpreted, then I
>dm> can go on to argue that unsystematic interpretations of their states
>dm> can be objectively given as well.
>dm>
>dm> -- Drew McDermott
> Brian C Smith
>bs> It... does not follow [that] if X is cognitive, and Y provably
>bs> equivalent to it (in the standard theoretic sense), that Y is
>bs> cognitive.
>bs> In some of the notes, it seemed that *intrinsic* and *attributed* were
>bs> being treated as opposites. This is surely false. Intrinsic is
>bs> presumably opposed to something like extrinsic or relational.
>bs> Attributed or observer-supplied is one particular species of
>bs> relational, but there are many others.
>bs> There are lots of reasons to believe that semantics, even original
>bs> semantics, will be relational... it may even be that our
>bs> *capacity* for semantics is relational (historical, cultural, etc)...
>bs> it seems to me a mistake to assume that *our* semantics is intrinsic in
>bs> us. So arguing that computers' semantics is not intrinsic doesn't cut
>bs> it as a way to argue against computational cognitivism.
>bs> [In a forthcoming paper ] I argue that actual, real-world computers are
>bs> not formal symbol manipulators (or, more accurately, that there is no
>bs> coherent reading of the term "formal" under which they are formal).
>bs> Of many problems, one that is relevant here is that the inside/
>bs> outside boundary does not align with the symbol/referent boundary --
>bs> a conclusion that wreaks havoc on traditional notions of transducers,
>bs> claims of the independence of syntax and semantics, the relevance of
>bs> "brain in a vat" thought experiments, etc.
>bs> Imagine someone trying to explain piano music by starting with the
>bs> notion of a melody, then observing that more than one note is played at
>bs> once, and then going on to say that there must also be chords. Maybe
>bs> some piano music can be described like that: as melody + chords. But
>bs> not a Beethoven sonata. The consequence of "many notes at once" is not
>bs> that one *adds* something (chords) to the prior idea of a single-line
>bs> melody. Once you've got the ability to have simultaneous notes, the
>bs> whole ball game changes.
>bs> I worry that the robotic reply to Searle suffers the same problem.
>bs> There's something right about the intuition behind it, having to do
>bs> with real-world engagement. But when you add it, it is not clear
>bs> whether the original notion (of formal symbol manipulation, or even
>bs> symbol manipulation at all) survives, let alone whether it will be a
>bs> coherent part of the expanded system. I.e., "symbol + robotic
>bs> grounding" seems to me all too similar to "melody + chords".
>bs> If this is true, then there is a very serious challenge as to what
>bs> notions *are* going to explain the expanded "engaged with the real
>bs> world" vision. One question, the one on the table, is whether or not
>bs> they will be computational (my own view is: *yes*, in the sense that
>bs> they are exactly the ones that are empirically needed to explain
>bs> Silicon Valley practice; but *no*, in that they will neither be an
>bs> extension to nor modification of the traditional formal symbol
>bs> manipulation construal, but will instead have to be redeveloped from
>bs> scratch). More serious than whether they are computational, however, is
>bs> what those notions *will actually be*. I don't believe we know.
>bs> the most important [distinction in AI and cognitive science] is whether
>bs> people assume that the TYPE STRUCTURE of the world can be taken as
>bs> explanatorily and unproblematically given, or whether it is something
>bs> that a theory of cognition/computation /intentionality/etc. must
>bs> explain. If you believe that the physical characterisation of a system
>bs> is given (as many writers seem to do), or that the token
>bs> characterisation is given (as Haugeland would lead us to believe), or
>bs> that the set of states is given (as Chalmers seems to), or that the
>bs> world is parsed in advance (as set theory & situation theory both
>bs> assume), then many of the foundational questions don't seem to be all
>bs> that problematic... Some of us, however, worry a whole lot about where
>bs> these type structures come from... [E]xplaining the rise of ontology
>bs> (objects, properties, relations, types, etc.) is part and parcel of
>bs> giving an adequate theory of cognition.
>bs>
>bs> Brian Smith
>sh> ...if a mind supervenes on (the right)
>sh> computations because of their computational
>sh> properties (rather than because of the physical
>sh> details of any particular implementation of
>sh> them), then it must supervene on ALL
>sh> implementations of those computations. I think
>sh> Searle's Chinese Room Argument has successfully
>sh> pointed out that this will not be so ...
>ph> No, only if you believe that what Searle in the room is doing is
>ph> letting a program run on him, which I think is clearly false. Searle's
>ph> Chinese Room argument doesn't SHOW anything. It can be used to bolster
>ph> a belief one might have about computations, but if one doesn't accept
>ph> that as a premise, than it doesn't follow as a conclusion either. The
>ph> "argument" is just an intuition pump, as Dennett observed a decade
>ph> ago.
>sh> transducers, for example, are no more
>sh> implementation-independent than digestion is.
>ph> Well, I see what you mean and agree, but one does have to be careful.
>ph> The boundary of implementation-independence can be taken very close to
>ph> the skin. For example, consider a robot with vision and imagine
>ph> replacing its tv cameras with more modern ones which use an array of
>ph> light-sensitive chips rather than scanning something with an electron
>ph> beam. It really doesn't matter HOW it works, how the physics is
>ph> realised, provided it sends the right signals back along its wires. And
>ph> this functional specification can be given in terms of the physical
>ph> energies which are input to it and the syntax of its output. So we are
>ph> in supervenience from the skin inwards.
>ph> This is like my point about language. While I think you are ultimately
>ph> correct about the need for a TTT to pin down meaning, the need seems
>ph> almost a piece of philosophical nitpicking, since one can get so far -
>ph> in fact, can probably do all of the science - without ever really
>ph> conceding it. In terms of thinking about actual AI work, the difference
>ph> between TT and TTT doesn't really MATTER. And by the way, if one talks
>ph> to people in CS, they often tend to regard the term 'computation' as
>ph> including for example real-time control of a lime kiln.
>ph> Heres a question: never mind transducers (I never liked that concept
>ph> anyway), how about proprioception? How much of a sense of pain can be
>ph> accounted for in terms of computations? This is all internal, but I
>ph> bet we need a version of the TTT to ultimately handle it properly, and
>ph> maybe one gets to the physics rather more immediately, since there isn't
>ph> any place to draw the sensed/sensing boundary.
>ph>
>ph> Pat Hayes
>
>sh> As a first pass at "formal," how about: A symbol system
>
>sh> consists of a set of objects (elementary symbols and composite
>
>sh> symbols) plus rules for manipulating the symbols. The rules
>
>sh> operate only on the physical shapes of the symbols, not their
>
>sh> meanings (and the shapes of the elementary symbols are
>
>sh> arbitrary), yet the symbols are systematically interpretable as
>
>sh> meaning something. The rules for manipulating the symbols on
>
>sh> the basis of their shapes are called "syntactic" or "formal"
>
>sh> rules.
>
>sh> "intuition pump" is not a pejorative, if it pumps true.
>
>sh> I will be happy to consider the implications of the fact that
>
>sh> Searle, doing everything the computer does, does not count as a
>
>sh> valid implementation of the same computer program -- as soon as
>
>sh> you specify and argue for what you mean by implementation and
>
>sh> why Searle's would not qualify. Until then, I don't see why
>
>sh> EVERY system that processes the same symbols, follows the same
>
>sh> (syntactic) rules and steps through the same states doesn't
>
>sh> qualify as a valid implementation of the same program.
>
>sh> I think most of the brain is preserving sensory signals in
>
>sh> various degrees of analog form (so we would probably do well to
>
>sh> learn from this).
>
>sh> In fact, I think it's as likely that a mind is mostly symbolic,
>
>sh> with just a thin analog layer mediating input and output to the
>
>sh> world, as that a plane or a furnace are mostly symbolic, with a
>
>sh> thin analog layer mediating input and output.
>
>sh> ... I'm suggesting that you can't implement them AT ALL with
>
>sh> computation alone -- not that you can't implement them
>
>sh> completely or unambiguously that way, but that you can't
>
>sh> implement them AT ALL.
>
>sh> (Or, as an intuition pump, you can implement pain or
>
>sh> proprioception by computation alone to the same degree that you
>
>sh> can implement flying or heating by computation alone.)
>ph> Many of the Searlean writers have taken it as somehow axiomatic that
>ph> human thinking just has this vital property of being meaningful,
>ph> something that only human, or maybe organic, thinking has been observed
>ph> to possess. Whatever this is, it isn't anything like a color or a mass
>ph> that human thought has.
>ph> I have a number of beliefs about ancient Rome. How are these thoughts
>ph> connected to the Rome of 2000 years ago? The answer is probably very
>ph> complicated... a historical chain... My thoughts about Julius Caesar
>ph> are not somehow intrinsically about him by virtue of their being in my
>ph> head; but they are in fact about him. But I can't see any reason why a
>ph> machine could not have almost the same (very complicated) relationship
>ph> to him that I have, whatever it is, since it is mediated almost
>ph> entirely by language.
>ph> I... think not that a particular set of types is fixed in advance, but
>ph> that what does seem to be fixed in us, in our way of thinking, is a
>ph> propensity to individuate. The world is a continuum, but we see it and
>ph> think of it as made of things, maybe overlapping in complex ways, but
>ph> conceptually separate entities that we can name and classify.
>ph>
>ph> Pat Hayes
>
>sh> As a first pass at "formal," how about: A symbol system
>
>sh> consists of a set of objects (elementary symbols and composite
>
>sh> symbols) plus rules for manipulating the symbols. The rules
>
>sh> operate only on the physical shapes of the symbols, not their
>
>sh> meanings (and the shapes of the elementary symbols are
>
>sh> arbitrary), yet the symbols are systematically interpretable as
>
>sh> meaning something. The rules for manipulating the symbols on
>
>sh> the basis of their shapes are called "syntactic" or "formal"
>
>sh> rules.
>
>sh> "intuition pump" is not a pejorative, if it pumps true.
>
>sh> I will be happy to consider the implications of the fact that
>
>sh> Searle, doing everything the computer does, does not count as a
>
>sh> valid implementation of the same computer program -- as soon as
>
>sh> you specify and argue for what you mean by implementation and
>
>sh> why Searle's would not qualify. Until then, I don't see why
>
>sh> EVERY system that processes the same symbols, follows the same
>
>sh> (syntactic) rules and steps through the same states doesn't
>
>sh> qualify as a valid implementation of the same program.
>
>sh> I think most of the brain is preserving sensory signals in
>
>sh> various degrees of analog form (so we would probably do well to
>
>sh> learn from this).
>
>sh> In fact, I think it's as likely that a mind is mostly symbolic,
>
>sh> with just a thin analog layer mediating input and output to the
>
>sh> world, as that a plane or a furnace are mostly symbolic, with a
>
>sh> thin analog layer mediating input and output.
>
>sh> ... I'm suggesting that you can't implement them AT ALL with
>
>sh> computation alone -- not that you can't implement them
>
>sh> completely or unambiguously that way, but that you can't
>
>sh> implement them AT ALL.
>
>sh> (Or, as an intuition pump, you can implement pain or
>
>sh> proprioception by computation alone to the same degree that you
>
>sh> can implement flying or heating by computation alone.)
>ph> Here's an example adapted from one of Brian [Smith's]. Take a set
>ph> of rules which encode (a formal system for) arithmetic, together with
>ph> a formal predicate 'lengthof', and the rules
>ph>
>ph> lengthof('0') -> 1
>ph> lengthof(n<>x) -> lengthof(n) + lengthof(x)
>ph>
>ph> Now, these rules make 'lengthof(n)' evaluate to (a numeral which means)
>ph> the number of digits in the formal representation of n: ie, the length
>ph> of that numeral in digits. Notice this is the ACTUAL length of that
>ph> piece of syntax. Now, is this 'formal'? It is according to your
>ph> definition, and perhaps you are happy with that, but it has some marks
>ph> which successfully refer to physical properties of part of the world.
>ph> My intuition tells me clearly that when I debug a piece of code by
>ph> pretending to be an interpreter and running through it "doing" what it
>ph> "tells" me to do, that the program is not being run, and certainly not
>ph> run on, or by, me. So we are left with your intuition vs. my intuition,
>ph> and they apparently disagree.
>ph> The key is that Searle-in-the-room is not doing everything the computer
>ph> "does," and is not going through the same series of states. For
>ph> example, suppose the program code at some point calls for the addition
>ph> of two integers. Somewhere in a computer running this program, a piece
>ph> of machinery is put into a state where a register is CAUSED to contain
>ph> a numeral representing the sum of two others. This doesn't happen in my
>ph> head when I work out, say, 3340 plus 2786, unless I am in some kind of
>ph> strange arithmetical coma. If Searle-in-the-room really was going
>ph> through the states of an implementation of a chinese-speaking
>ph> personality, then my intuition, pumped as hard as you like, says that
>ph> that Chinese understanding is taking place. And I haven't yet heard an
>ph> argument that shows me wrong.
>ph> that the motor and sensory cortex use 'analogical' mappings of bodily
>ph> location is probably more due to the fact that this fits very nicely
>ph> with the way the information is piped into the processor, where
>ph> location is encoded by neuroanatomy, than by any profound issue about
>ph> symbolic vs. analog. It has some nice features, indeed, such as
>ph> localisation of the effects of damage: but we are now in the language
>ph> of computer engineering.
>ph> Of course a furnace is not symbolic. But hold on: that's the point,
>ph> right? Furnaces just operate in the physical world, but minds (and
>ph> computers) do in fact react to symbols: they do what you tell them, or
>ph> argue with you, or whatever: but they respond to syntax and meaning,
>ph> unlike furnaces and aircraft. That's what needs explaining. If you are
>ph> going to lump furnaces and minds together, you are somehow missing the
>ph> point that drives this entire enterprise. (Aircraft are actually a
>ph> borderline case, since they do react to the meanings of symbols input
>ph> to them, exactly where they have computers as part of them.)
>ph> with what you mean by computation, I couldn't even run
>ph> Wordstar with computation ALONE. I need a computer.
>ph> I bet computational ideas will be centrally involved in a successful
>ph> understanding of pain and proprioception, probably completely
>ph> irrelevant to understanding lime chemistry, but important in reasonably
>ph> exotic flying.
>ph>
>ph> Pat Hayes
>ry> as Brian [Smith] indicated, UTM universality refers to a very special
>ry> type of *weak equivalence* (Pylyshyn, 1984) between TM's and UTM's.
>ry> Universality merely means partial I/O equivalence. This is insufficient
>ry> for many discussions about the computability of mind---e.g., the
>ry> Chinese Room---because such discussions consider, not only I/O
>ry> behavior, but also *how* the behavior is achieved, and UTM's are far
>ry> from "typical" in their manner of computation. In particular, although
>ry> UTM's process certain input symbols purely formally, not all TM's need
>ry> behave this way.
>ry> The implications of the TM/UTM distinction for the Chinese Room (CR)
>ry> argument are straightforward. The person in the CR is a UTM U that is
>ry> given a program P (the rules). (Note that "memorizing" the rules does
>ry> not change U into Tp. Any set of rules could be memorized, and the
>ry> memorized rules remain an input to U.) To answer the question of *how*
>ry> the Chinese symbols x' are being processed inside the room, one must
>ry> consider what *Tp* is doing to the symbols. Considering only U's
>ry> activity is useless because U is computing z=(P,x')--> y. Thus, without
>ry> specific knowledge of the rules P, one simply cannot answer the
>ry> question of whether the Chinese input symbols are being understood in
>ry> the CR or are only being formally manipulated. Both possibilities
>ry> remain open (unless, of course, one advocates the Turing Test for
>ry> understanding, but that is an independent argument).
>ry> At best, the Chinese Room argument shows that UTM computations are not
>ry> good candidates for minds. However, there remain plenty of
>ry> non-universal TM computations, and---absent any proof to the
>ry> contrary---some of them might be minds. To find out, one should forget
>ry> about computers and think about instantiated programs. If one's real
>ry> interest is the entire class of TM's, then it is dangerous to form
>ry> intuitions and conclusions revolving around the special properties of
>ry> UTM's.
>ry> Many debates about computers and minds pit critics of purely formal
>ry> symbol processing (which UTM's perform) against proponents of
>ry> computation (which all TM's perform)... intentionality,
>ry> symbol-grounding, meaning, etc. (of the type desired by Searle, Harnad,
>ry> Penrose and others) is necessary for (human-like) minds, and such
>ry> semantics is Turing-computable.
>ry>
>ry> Richard Yee
>ph> From: Pat Hayes
>ph> Date: Wed, 22 Apr 92 15:30:28 MDT
>ph> To: tim@arti1.vub.ac.be (Tim Smithers)
>ph> Subject: Re: Smithers on Dyer on the physical symbol hypothesis (PSH)
>ph>
>ph> Dear Tim Smithers,
>ph>
>ph> First, the PSH is as much a hypothesis as, say, the hypothesis of
>ph> continental drift. Nobody could observe continental drift or
>ph> conduct a 'broad experimental investigation' of its validity.
>ph> It is a general idea which makes sense of a large number of
>ph> observations and provides a framework within which many empirical
>ph> results can be fitted. Most of the hypotheses of science are like
>ph> this: they aren't tested by little well-designed experiments, and
>ph> indeed couldn't be. There are whole areas of investigation,
>ph> such as cosmology, which couldn't be done in this simplistic
>ph> textbook way of (idea->design experiment->test->next idea),
>ph> and whole methodologies, such as ecological psychology, which
>ph> explicitly reject it. People who have been trained to perform
>ph> little experiments to test (often rather silly) little ideas
>ph> [cannot] lay [exclusive] claim to the use of words like 'hypothesis'.
>ph>
>ph> And in any case, the whole practice of AI can be regarded as the
>ph> empirical testing of the hypothesis. Of course those who are working
>ph> under its aegis do not constantly question it, but take it as an
>ph> assumption and see how much science can be developed under it.
>ph> That is the way that science makes progress, in fact, as Kuhn has
>ph> argued convincingly. The world has plenty of serious people who reject
>ph> the PSH and are using other frameworks to develop and test theories of
>ph> mentality, and a large number of vocal and argumentative critics, so
>ph> there is no risk of its not being tested.
>ph>
>ph> Turning now to your second paragraph. You accuse Dyer of making
>ph> arguments which are 'in principle possible but in practice right
>ph> out of the window'. This, in a discussion which flows from a
>ph> hypothesis in which a human being memorises the code of a
>ph> program which can pass the Turing Test in Chinese, while preserving
>ph> his equanimity to the extent that he can simultaneously discuss
>ph> the code! If we are to reject unrealistic examples, then we can
>ph> all surely agree that the whole matter is a complete waste of
>ph> time, and just forget about it, starting now.
>ph>
>ph> Pat Hayes
>
>sh> But if the drowning is "virtual" (i.e., a computer-simulated
>
>sh> person is "drowned" in computer-simulated water)
>
>sh> there is no drowning at all going on, no matter how formally
>
>sh> equivalent the symbols may be to real drowning.
>
>sh> ... as an intuition pump you can
>
>sh> implement pain or proprioception by computation alone to the
>
>sh> same degree that you can implement flying or heating by
>
>sh> computation alone.)...
>
>sh> I just don't think computation alone can either fly or think.
>
>sh> By the way, minds and computers may both respond to syntax,
>
>sh> but only minds respond to meaning. Computers are merely
>
>sh> INTERPRETABLE as if they responded to meaning...
>
>sh> Searle simply points out to us that if he
>
>sh> himself implemented the program (by memorizing the symbols
>
>sh> and symbol manipulation rules) he would not understand Chinese,
>
>sh> hence neither would any computer that implemented the same
>
>sh> program.
>
>sh> the structures and processes
>
>sh> underlying our capacity to categorize inputs (beginning with sensory
>
>sh> projections).... will turn out to be
>
>sh> largely nonsymbolic, but perhaps symbols can be grounded in the
>
>sh> capacity those nonsymbolic structures and processes give us to pick out
>
>sh> the objects they are about.
>
>ph> Here's an example adapted from one of Brian [Smith's]. Take a set
>
>ph> of rules which encode (a formal system for) arithmetic, together with
>
>ph> a formal predicate 'lengthof', and the rules
>
>ph>
>
>ph> lengthof('0') -> 1
>
>ph> lengthof(n<>x) -> lengthof(n) + lengthof(x)
>
>ph>
>
>ph> Now, these rules make 'lengthof(n)' evaluate to (a numeral which means)
>
>ph> the number of digits in the formal representation of n: ie, the length
>
>ph> of that numeral in digits. Notice this is the ACTUAL length of that
>
>ph> piece of syntax. Now, is this 'formal'? It is according to your
>
>ph> definition, and perhaps you are happy with that, but it has some marks
>
>ph> which successfully refer to physical properties of part of the world.
>
>
>sh> But note that in your example above, even though the computation yields
>
>sh> a symbol that is interpretable as the number of symbols in the string,
>
>sh> this is in principle no different from a computation that yields a
>
>sh> symbol that is interpretable as the number of planets in the solar
>
>sh> system. It is just a systematic correspendence (and hence interpretable
>
>sh> as such)
>
>sh> ... But "interpretable as meaning X" (as in the case of a book,
>
>sh> interpretable by a thinking mind) is not the same as "meaning X" (as in
>
>sh> the case of thoughts, in a mind). Failing to distinguish the two seems
>
>sh> to be another instance of conflating physical inner/outer and mental
>
>sh> inner/outer, as discussed earlier.
>
>
>ph> My intuition tells me clearly that when I debug a piece of code by
>
>ph> pretending to be an interpreter and running through it "doing" what it
>
>ph> "tells" me to do, that the program is not being run, and certainly not
>
>ph> run on, or by, me. So we are left with your intuition vs. my intuition,
>
>ph> and they apparently disagree.
>
>
>sh> But isn't the real question whether there is any relevant difference
>
>sh> between what you think is a "real" implementation by a machine and what
>
>sh> you think is a "pseudo-implementation" by a person? Certainly the
>
>sh> computer is not stepping through the states consciously and
>
>sh> deliberately, as you are. But is there anything else that's different?
>
>sh> If we speak only of the "motions gone through" and their I/O conditions
>
>sh> in the two cases, they are exactly the same. In the case of the
>
>sh> machine, the motions are mechanical; no choice is involved. In the case
>
>sh> of the person, their elective. But so what?
>
>sh> Even apart from the vexed
>
>sh> questions associated with free will and causality, what is there about
>
>sh> taking IDENTICAL motions under identical I/O conditions and making
>
>sh> their causal basis mindless and mechanical that could possibly effect a
>
>sh> transition INTO the mental (rather than OUT of it, which is the much
>
>sh> more obvious feature of the transition from the human implementation to
>
>sh> the machine one)?
>
>sh> It's always useful, in this sort of hermeneutic puzzle, to de-interpret
>
>sh> and reduce things to gibberish as much as possible
>
>sh> Suppose the computer was doing all the requisite
>
>sh> summation in binary, and you were too,
>
>sh> and all it did, and all you did, was compare zero's and one's and erase
>
>sh> and carry, just like a Turing Machine. Is it still so obvious that
>
>sh> you're not doing everything the computer is doing? If anything, the
>
>sh> computer is doing less than you rather than more (because it has no
>
>sh> choice in the matter). Why should I interpret less as more?
>
>sh> By the way, minds and computers may both respond to syntax, but only
>
>sh> minds respond to meaning. Computers are merely INTERPRETABLE as if they
>
>sh> responded to meaning...
>
>sh> And I bet a lot of the essential features of pain and proprioception
>
>sh> will be in the analog properties of the hardware that implements it,
>
>sh> which will be more like exotic chemistry.
>dm> Let's distinguish between a computer's states' being
>dm> "microinterpretable" and "macrointerpretable." The former case is what
>dm> you assume: that if we consider the machine to be a rewrite system, the
>dm> rewrite rules map one coherently interpretable state into another. Put
>dm> another way, the rewrite rules specify a change in belief states of the
>dm> system. By contrast, the states of a macrointerpretable system "sort of
>dm> line up" with the world in places, but not consistently enough to
>dm> generate anything like a Tarskian interpretation. What I think you've
>dm> overlooked is that almost all computational processes are at best
>dm> macrointerpretable.
>ph> Drew, clearly you have an antisemantic axe to grind, but its not
>
>sh> very sharp.
>
>sh> (1) Does EVERY computer implementing a program have SOME states that are
>
>sh> interpretable as referring to objects, events and states of affairs, the
>
>sh> way natural language sentences are?
>
>sh> (2) Are ALL states in EVERY computer implementing a program interpretable
>
>sh> as referring... (etc.)?
>
>sh> (3) What is the relation of such language-like referential
>
>sh> interpretability and OTHER forms of interpretability of states of a
>
>sh> computer implementing a program?
>
>sh> (4) What is the relation of (1) - (3) to the software hierarchy, from
>
>sh> hardware, to machine-level language, to higher-level compiled
>
>sh> languages, to their English interpretations?
>
>sh> My answer would be that not all states of a computer implementing a
>
>sh> program need be interpretable, and not all the interpretable states
>
>sh> need be language-like and about things in the world (they could be
>
>sh> interpretable as performing calculations on numbers, etc.), but ENOUGH
>
>sh> of the states need to be interpretable SOMEHOW, otherwise the computer
>
>sh> is just performing gibberish (and that's usually not what we use
>
>sh> computers to do, nor do we describe them as such), and THAT's the
>
>sh> interpretability that's at issue here.
>dm> More examples: What do the states of a video game refer to? The Mario
>dm> brothers? Real asteroids?
>
>sh> They are interpretable as pertaining (not referring, because there's no
>
>sh> need for them to be linguistic) to (indeed, they are hard-wireable to)
>
>sh> the players and moves in the Mario Brothers game, just as in chess. And
>
>sh> the graphics control component is interpretable as pertaining to (and
>
>sh> hard-wireable to the bit-mapped images of) the icons figuring in the
>
>sh> game. A far cry from uninterpretable squiggles and squoggles.
>dm> Take almost any example, a chess program, for instance. Suppose that
>dm> the machine is evaluating a board position after a hypothetical series
>dm> of moves. Suppose the evaluation function is a sum of terms. What does
>dm> each term denote? It is not necessary to be able to say. One might, for
>dm> instance, notice that a certain term is correlated with center control,
>dm> and claim that it denotes "the degree of center control," but what does
>dm> this claim amount to? In many games, the correlation will not hold, and
>dm> the computer may as a consequence make a bad move. But the evaluation
>dm> function is "good" if most of the time the machine makes "good moves."
>
>sh> I'm not sure what an evaluation function is,
>
>sh> but again, I am not saying
>
>sh> every state must be interpretable. Even in natural language there are
>
>sh> content words (like "king" and "bishop") that have referential
>
>sh> interpretations and function words ("to" and "and") that have at best
>
>sh> only syntactic or functional interpretations. But some of the internal
>
>sh> states of a chess-plying program surely have to be interpretable as
>
>sh> referring to or at least pertaining to chess-pieces and chess-moves, and
>
>sh> those are the ones at issue here.
>dm> The chess program keeps a tree of board positions. At each node of this
>dm> tree, it has a list of moves it is considering, and the positions that
>dm> would result. What does this list denote? The set of moves "worth
>dm> considering"? Not really; it's only guessing that these moves are worth
>dm> considering. We could say that it's the set the machine "is
>dm> considering," but this interpretation is trivial.
>
>sh> And although I might make that interpretation for convenience in
>
>sh> describing or debugging the program (just as I might make the
>
>sh> celebrated interpretation that first got Dan Dennett into his
>
>sh> "intentional stance," namely, that "the computer thinks it should get
>
>sh> it's queen out early"), I would never dream of taking such
>
>sh> interpretations literally: Such high level mentalistic interpretations
>
>sh> are simply the top of the as-if hierarchy, a hierarchy in which
>
>sh> intrinsically meaningless squiggles and squoggles can be so interpreted
>
>sh> that (1) they are able to bear the systematic weight of the
>
>sh> interpretation (as if they "meant" this, "considered/believed/thought"
>
>sh> that, etc.), and (2) the interpretations can be used in (and even sometimes
>
>sh> hard-wired to) the real world (as in interpreting the squiggles and
>
>sh> squoggles as pertaining to chess-men and chess-moves).
>dm> We can always impose a trivial interpretation on the states of the
>dm> computer. We can say that every register denotes a number, for
>dm> instance, and that every time it adds two registers the result denotes
>dm> the sum. The problem with this idea is that it doesn't distinguish the
>dm> interpreted computers from the uninterpreted formal systems, because I
>dm> can always find such a Platonic universe for the states of any formal
>dm> system to "refer" to. (Using techniques similar to those used in
>dm> proving predicate calculus complete.)
>
>sh> I'm not sure what you mean, but I would say that whether they are
>
>sh> scratches on a paper or dynamic states in a machine, formal symbol
>
>sh> systems are just meaningless squiggles and squoggles unless you project
>
>sh> an interpretation (e.g., numbers and addition) onto them.
>
>sh> The fact that
>
>sh> they will bear the systematic weight of that projection is remarkable
>
>sh> and useful (it's why we're interested in formal symbol systems at all),
>
>sh> but certainly not evidence that the interpretation is intrinsic to the
>
>sh> symbol system;
>
>sh> it is only evidence of the fact that the system is
>
>sh> indeed a nontrivial symbol system (in virtue of the fact that it is
>
>sh> systematically interpretable). Nor (as is being discussed in other
>
>sh> iterations of this discussion) are coherent, systematic "nonstandard"
>
>sh> alternative interpretations of formal symbol systems that easy to come
>
>sh> by.
>dm> If no other argument convinces you, this one should: Nothing prevents
>dm> a computer from having inconsistent beliefs. We can build an expert
>dm> system that has two rules that either (a) cannot be interpreted as
>dm> about medical matters at all; or (b) contradict each other. The system,
>dm> let us say, happens never to use the two rules on the same case, so
>dm> that on any occasion its advice reflects a coherent point of view.
>dm> (Sometimes it sounds like a homeopath, we might say, and sometimes like
>dm> an allopath.) We would like to say that overall the computer's
>dm> inferences and pronouncements are "about" medicine. But there is no way
>dm> to give a coherent overall medical interpretation to its computational
>dm> states.
>
>sh> I can't follow this: The fact that a formal system is inconsistent, or
>
>sh> can potentially generate inconsistent performance, does not mean it is
>
>sh> not coherently interpretable: it is interpretable as being
>
>sh> inconsistent, but as yielding mostly correct performance nevertheless.
>
>sh> [In other words, "coherently interpretable" does not mean
>
>sh> "interpretable as coherent" (if "coherent" presupposes "consistent").]
>ph> Your inconsistent-beliefs point misses an important issue. If that
>ph> expert system has some way of ensuring that these contradictory rules
>ph> never meet, then it has a consistent interpretation, trivially: we can
>ph> regard the mechanism which keeps them apart as being an encoding of a
>ph> syntactic difference in its rule-base which restores consistency.
>ph> Maybe one set of rules is essentially written with predicates with an
>ph> "allo-" prefix and the others with a "homeo-". You might protest that
>ph> this is cheating, but I would claim not: in fact, we need a catalog of
>ph> such techniques for mending consistency in sets of beliefs, since
>ph> people seem to have them and use them to 'repair' their beliefs
>ph> constantly, and making distinctions like this is one of them (as in,
>ph> "Oh, I see, must be a different kind of doctor"). If on the other hand
>ph> the system has no internal representation of the distinction, even
>ph> implicit, but just happens to never bring the contradiction together,
>ph> then it is in deep trouble ....
>dm> I suspect Searle would welcome this view, up to a point. It lends
>dm> weight to his claim that semantics are in the eye of the beholder.
>dm> ... However, the point
>dm> at issue right now is whether semantic interpretability is part of the
>dm> definition of "computer." I argue that it is not; a computer is what
>dm> it is regardless of how it is interpreted. I buttress that
>dm> observation by pointing out just how unsystematic most interpretations
>dm> of a computer's states are. However, if I can win the argument about
>dm> whether computers are objectively given, and uninterpreted, then I
>dm> can go on to argue that unsystematic interpretations of their states
>dm> can be objectively given as well.
>
>sh> If you agree with Searle that computers can't be distinguished from
>
>sh> non-computers on the basis of interpretability, then I have to ask you
>
>sh> what (if anything) you DO think distinguishes computers from
>
>sh> non-computers?
>ph> If we include (as we should) linguistic input, it seems clear that
>ph> structures and processes [underlying our capacity to categorize] will
>ph> be largely symbolic... vision and other perceptual modes involve
>ph> symbols from an early stage...
>ph> No, you have missed the point of the [internal length] example.
>ph> in this example, the systematicity is between the
>ph> syntax of one numeral and the actual (physical?) length of another.
>ph> This is not the same kind of connection as that between some symbols
>ph> and a piece of the world that they can be interpreted as referring to.
>ph> It requires no external interpreter to make it secure, the system
>ph> itself guarantees that this interpretation will be correct. It is a
>ph> point that Descartes might have made: I don't need to be connected to
>ph> an external world in any way in order to be able to really count.
>ph> What is different in having a machine that can run
>ph> algorithms from just being able to run algorithms? I take it as
>ph> obvious that something important is...
>ph> Clearly if you insist that [reducing to gibberish]
>ph> can always be done to computer insides but not always to human
>ph> insides, then you are never going to see meaning in a machine.
>dm> We're talking about whether semantic interpretability is part of the
>dm> *definition* of computer. For that to be the case, everything the
>dm> computer does must be semantically interpretable. Does it cease to be a
>dm> computer during the interludes when its behavior is not interpretable?
>dm> I assumed that your original claim was that a computer had to
>dm> correspond to an interpreted formal system (where, in the usual case,
>dm> the users supply the interpretation). But that's not what you meant...
>dm> now it's clear that neither you nor Pat is proposing anything of
>dm> this sort. Instead, you seem to agree with me that a computer is a
>dm> physical embodiment of a formal automaton, plus a kind of loose,
>dm> pragmatic, fault-prone correspondence between its states and various
>dm> world states. Given this agreement, let's simplify. Clearly, the
>dm> semantic interpretation is no part of the definition of computer. We
>dm> can identify computers without knowing what interpretation their users
>dm> place on them.
>dm> The [videogame] "players and moves" mostly don't exist, of course,
>dm> since they include entities like King Koopa and Princess Toadstool. The
>dm> child playing the game thinks (sort of) that the pictures on the screen
>dm> refer to a certain universe. Or maybe they *constitute* a universe.
>dm> It's hard to be precise, but I hope by now vagueness doesn't bother
>dm> you. Of course, the engineer that wrote the game knows what's
>dm> *really* going on. The input signals refer to presses of control
>dm> buttons by the game player. Output signals refer to shapes on a
>dm> screen. But it would be absurd to say that the game player's version
>dm> of the semantics is only an illusion, and the real purpose of the
>dm> system is to map buttons pushes onto screen alterations.
>dm> You're forgetting which side of the argument you're on. *I'm* arguing
>dm> that such interpretations are epiphenomenal. *You're* arguing that
>dm> the interpretation is the scaffolding supporting the computerhood of
>dm> the system.
>sh> If you agree with Searle that computers can't be distinguished from
>sh> non-computers on the basis of interpretability, then I have to ask you
>sh> what (if anything) you DO think distinguishes computers from
>sh> non-computers?
>dm> I refer you to Chalmers. A brief summary: A system is a computer if
>dm> its physical states can be partitioned into classes that obey a
>dm> transition relation. -- Drew McDermott
> To all contributors to the "What is Computation?" Symposium:
>
> Please let me know which of you are (and are NOT) interested in
> publication. In the meanwhile, we can continue for a few more
> iterations before involking cloture. Perhaps the prospect of publication
> will change the style of interaction from this point on, perhaps not...
>
>sh> normally the only way to know whether or not a
>
>sh> system has a mind is to BE the system.
>
>ph> If we include (as we should) linguistic input, it seems clear that
>
>ph> structures and processes [underlying our capacity to categorize] will
>
>ph> be largely symbolic... vision and other perceptual modes involve
>
>ph> symbols from an early stage...
>
>
>sh> The only problem with "including" (as you put it) linguistic input is
>
>sh> that, without grounding, "linguistic input" is just meaningless
>
>sh> squiggles and squoggles. To suppose it is anything more is to beg the
>
>sh> main question at issue here.
>
>sh> To categorize is to sort the objects in the world, beginning with their
>
>sh> sensory projections
>
>sh> It is true that we can sort names and descriptions
>
>sh> too, but unless these are first grounded in the capacity to sort and
>
>sh> name the objects they refer to, based on their sensory projections,
>
>sh> "names and descriptions" are just symbolic gibberish...
>
>sh> But your point about the correspondence between the internal numerical
>
>sh> symbol for the length of an internal sequence can be made without
>
>sh> referring to the mental.
>
>sh> There is certainly a correspondence there,
>
>sh> and the interpretation is certainly guaranteed by causality, but only
>
>sh> in a slightly more interesting sense than the interpretation that every
>
>sh> object can be taken to be saying of itself "Look, here I am!"
>
>sh> Symbols that aspire to be the language of thought cannot just have a few
>
>sh> fixed connections to the world.
>
>sh> The systematicity that is needed has to
>
>sh> have at least the full TT power of natural language -- and to be
>
>sh> grounded it needs TTT-scale robotic capacity.
>
>ph> What is different in having a machine that can run
>
>ph> algorithms from just being able to run algorithms? I take it as
>
>ph> obvious that something important is...
>
>
>sh> I think you're missing my point. The important thing is that the
>
>sh> algorithm be implemented mindlessly, not that it be implemented
>
>sh> mechanically (they amount to the same thing, for all practical
>
>sh> purposes). I could in principle teach a (cooperative) two-year old who
>
>sh> could not read or write to do rote, mechanical addition and
>
>sh> multiplication. I simply have him memorize the finite set of
>
>sh> meaningless symbols (0 - 9) and the small set of rules (if you see "1"
>
>sh> above "3" and are told to "add" give "4", etc.). I would then have a
>
>sh> little human calculator, implementing an algorithm, ...
>
>sh> Now let me tell you what WOULD be cheating: If any of what I had the
>
>sh> child do was anything but SYNTACTIC, i.e., if it was anything other than
>
>sh> the manipulation of symbols on the basis of rules that operate only on
>
>sh> their (arbitrary) shapes: It would be cheating if the child (mirabile
>
>sh> dictu) happened to know what "odd" and "even" meant, and some of the
>
>sh> calculations drew on that knowledge instead of just on the mechanical
>
>sh> algorithm I had taught him. But as long it's just mechanical syntax,
>
>sh> performed mindlessly, it makes no difference whatsoever whether it is
>
>sh> performed by a machine or stepped through (mechanically) by a person.
>
>sh> Now if you want to appreciate the real grip of the hermeneutical circle,
>
>sh> note how much easier it is to believe that an autonomous black box is
>
>sh> "really" understanding numbers if it is a machine implementing an
>
>sh> algorithm mechanically rather than an illiterate, non-numerate child,
>
>sh> who is just playing a symbolic game at my behest.
>
>sh> THAT's why you want to
>
>sh> disqualify the latter as a "real" implementation, despite the fact that
>
>sh> the same syntactic algorithm is being implemented in both cases, without
>
>sh> any relevant, nonarbitrary differences whatsoever.
>
>ph> Clearly if you insist that [reducing to gibberish]
>
>ph> can always be done to computer insides but not always to human
>
>ph> insides, then you are never going to see meaning in a machine.
>
>
>sh> I am sure that whatever is REALLY going on in the head can also be
>
>sh> deinterpreted, but you mustn't put the cart before the horse: You
>
>sh> cannot stipulate that, well then, all that's really going on in the
>
>sh> head is just symbol manipulation, for that is the hypothesis on trial
>
>sh> here!
>
>sh> {Actually, there are two semi-independent hypotheses on trial: (1) Is
>
>sh> anything NOT just a computer doing computation? and, (2) Are minds just
>
>sh> computers doing computation? We agree, I think, that some things are
>
>sh> NOT computers doing computation, but you don't think the mind is one of
>
>sh> those noncomputational things whereas I do.]
>
>sh> I had recommended the exercise of deinterpreting the symbols so as to
>
>sh> short circuit the persuasive influence of those properties that are
>
>sh> merely byproducts of the interpretability of the symbols, to see
>
>sh> whether there's anything else left over. In a grounded TTT-scale robot
>
>sh> there certainly would be something left over, namely, the robotic
>
>sh> capacity to discriminate, categorize and manipulate the objects, events
>
>sh> and states of affairs that the symbols were about. Those would be there
>
>sh> even if the symbols were just gibberish to us. Hence they would be
>
>sh> grounding the interpretations independently of our mentalistic
>
>sh> projections.
>ph> I take it as observationally obvious [1] that stones don't have minds,
>ph> [2] that (most) humans do, and that such things as [3] cats and mice
>ph> and perhaps some [4] complex computational systems are [5] best
>ph> described as having partial, simple, or primitive minds... and I don't
>ph> think that there is such a sharp division between mere biology or
>ph> mechanism and real intensional thought.
>
>sh> normally the only way to know whether or not a
>
>sh> system has a mind is to BE the system.
>
>ph> If one takes this stark a view of the other-minds question then it
>ph> seems to me hard to avoid solipsism; and I may not be able to refute
>ph> solipsism, but I'm not going to let anyone ELSE persuade me its true.
>ph> The question is how formal symbols in a computational system might
>ph> acquire meaning. But surely the words in the English sentences spoken
>ph> to a machine by a human do not need to have their meaningfulness
>ph> established in the same way. To take English spoken by humans - as
>ph> opposed to formalisms used by machines - as having content surely does
>ph> not beg any of the questions we are discussing.
>
>sh> To categorize is to sort the objects in the world,
>
>sh> beginning with their sensory projections
>
>ph> But surely by insisting on beginning thus, YOU are begging the question!
>ph> Consider the word "everyone". What kind of "sensory projection" could
>ph> provide the suitable "grounding" for the meaning of this? And try
>ph> "whenever", "manager" or "unusual". Language is full of words whose
>ph> meaning has no sensory connections at all.
>ph> [There is] a huge collection of computational phenomena throughout
>ph> which interpretation is similarly guaranteed by causality [as in the
>ph> length of the internal string example]. This was Brian Smith's point:
>ph> computation is, as it were, permeated by meanings causally linked to
>ph> symbols.
>
>sh> Symbols that aspire to be the language of thought
>
>sh> cannot just have a few fixed connections to the world.
>
>ph> Let us suppose that you are basically right about the need for
>ph> grounding to guarantee meaning. I believe you are, and have made
>ph> similar points myself in my "naive physics" papers, although I think
>ph> that English can ground things quite successfully, so I have more
>ph> confidence in the TT than you do. But now, how much grounding does it
>ph> take to sufficiently fix the meanings of the symbols of the formalisms?
>ph> Surely not every symbol needs to have a direct perceptual accounting.
>ph> We have all kinds of mechanisms for transferring meanings from one
>ph> symbol to another, for example.
>ph> But more fundamentally, beliefs relating several concepts represent
>ph> mutual constraints on their interpretation which can serve to enforce
>ph> some interpretations when others are fixed. This seems to be a central
>ph> question: just how much attachment of the squiggles to their meanings
>ph> can be done by axiomatic links to other squoggles?
>ph> A human running consciously through rules, no matter how "mindlessly,"
>ph> is not a computer implementing a program. They differ profoundly, not
>ph> least for practical purposes. For example, you would need to work very
>ph> hard on keeping a two-year-old's attention on such a task, but the
>ph> issue of maintaining attention is not even coherent for a computer.
>ph>
>ph> I see an enormous, fundamental and crucial difference between your
>ph> "mindless" and "mechanical." The AI thesis refers to the latter, not
>ph> the former. To identify them is to abandon the whole idea of a
>ph> computer... to deny this is to deny that computation is real.
>ph>
>ph> a human running through an algorithm does not constitute
>ph> an IMPLEMENTATION of that algorithm. The difference is precisely what
>ph> computer science is the study of: how machines can perform algorithms
>ph> without human intervention.
>ph> Surely if you concede that the head's machinations can be
>ph> de-interpreted, then indeed you have conceded the point; because then
>ph> it would follow that the head was performing operations which did not
>ph> depend on the meanings of its internal states.
>ph> what I don't follow is why you regard the conversational behavior of a
>ph> successful passer of the TT clearly insufficient to attach meaning to
>ph> its internal representations, while you find Searle's response to the
>ph> "Robot Reply" quite unconvincing. If we are allowed to look inside the
>ph> black box and de-interpret its innards in one case, why not also the
>ph> other? Why is robotic capacity so magical in its grounding capacity but
>ph> linguistic capacity, no matter how thorough, utterly unable to make
>ph> symbols signify? And I don't believe the differences are that great,
>ph> you see. I think much of what we all know is attached to the world
>ph> through language. That may be what largely differentiates us from the
>ph> apes: we have this incredible power to send meaning into one another's
>ph> minds.
>ph> The arithmetic example, while very simple, provides an interesting
>ph> test for your hermeneutic intuitions. Take two different addition
>ph> algorithms. One is the usual technique we all learned involving adding
>ph> columns of numbers and carrying the tens, etc. The other has a bag
>ph> and a huge pile of pebbles and counts pebbles into the bag for each
>ph> number, then shakes the bag and counts the pebbles out, and declares
>ph> that to be the sum. A child might do that. Would you be more inclined
>ph> to say that the second, pebble-counting child understood the concept of
>ph> number? You can no doubt recognise the path I am leading you along.
>ph>
>ph> Pat Hayes
>
>sh> You may be surprised to hear that this a perfectly respectable
>
>sh> philosophical position (held, for example, by Paul Churchland and
>
>sh> many others)
>
>sh> (and although the parenthetic phrase about "understanding how
>
>sh> consciousness works" comes perilously close to begging the
>
>sh> question).
>
>sh> But you will also be surprised to hear that this is not a
>
>sh> philosophical discussion (at least not for me)! I'm not
>
>sh> interested in what we will or won't be able to know for sure
>
>sh> about mental states once we reach the Utopian scientific
>
>sh> state of knowing everything there is to know about them
>
>sh> empirically. I'm interested in how to GET to that Utopian
>
>sh> state.
>
>sh> Yes, but if you have been following the discussion of the symbol
>
>sh> grounding problem you should by now (I hope) have encountered
>
>sh> reasons why such (purely symbolic) mechanisms would not be
>
>sh> sufficient to implement mental states, and what in their stead
>
>sh> (grounded TTT-passing robots) might be sufficient.
>
>sh> Fortunately, my reply to Mike can be very short: Real neurons don't
>
>sh> just implement computations, and symbolically simulated neurons are not
>
>sh> neurons. Set up a continuum from a real furnace heating (or a real
>
>sh> plane flying, or a real planetary system, moving) to a computational
>
>sh> simulation of the same and tell me where the real heating (flying,
>
>sh> moving) starts/stops. It's at the same point (namely, the stepping-off
>
>sh> point from the analog world to its symbolic simulation) that a real
>
>sh> TTT-passing robot (with its real robot-brain) and its computationally
>
>sh> simulated counterpart part paths insofar as really having a mind is
>
>sh> concerned. -- Stevan Harnad
>ph> This Searlean thesis that everything is a
>ph> computer is so damn silly that I take it simply as absurd. I don't feel
>ph> any need to take it seriously since I have never seen a careful
>ph> argument for it, but even if someone produces one, that will just amount
>ph> to a reductio tollens disproof of one of its own assumptions.
>
>sh> There either IS somebody home in there,
>
>sh> experiencing experiences, thinking thoughts, or NOT. And if not, then
>
>sh> attributing a mind to it is simply FALSE, whether or not it is the "best
>
>sh> description" (see Oded Maler's point about things vs. descriptions).
>
>sh> Nor is there a continuum from the mental to the nonmental (as there
>
>sh> perhaps is from the living to the nonliving). There may be higher and
>
>sh> lower alertness levels, there may be broader and narrower experiential
>
>sh> repertoires or capacities, but the real issue is whether there is
>
>sh> anybody home AT ALL, experiencing anything whatever, and that does
>
>sh> indeed represent a "sharp division" -- though not necessarily between
>
>sh> the biological and the nonbiological.
>
>sh> for there also happens to be a FACT of the matter: There either IS
>
>sh> somebody home in there, experiencing experiences, thinking thoughts, or
>
>sh> NOT. And if not, then attributing a mind to it is simply FALSE
>ph> From: Pat Hayes (hayes@cs.stanford.edu)
>ph> Date: Tue, 28 Apr 92 18:04:15 MDT
>ph> No, thats exactly where I disagree. A human running consciously through
>ph> rules, no matter how 'mindlessly', is not a computer implementing a
>ph> program. They differ profoundly, not least for practical purposes. For
>ph> example, you would need to work very hard on keeping a two-year-old's
>ph> attention on such a task, but the issue of maintaining attention is not
>ph> even coherent for a computer.
>ph> From: Pat Hayes
>ph> Date: Sun, 19 Apr 92 15:08:06 MDT
>ph> [...] Searle talks about the
>ph> distinction between a model and the real thing, but the moral of the
>ph> classical work on universality (and of CS practice - not just in
>ph> Silicon Valley, by the way!) is exactly that a computational simulation
>ph> of a computation IS a computation. Thus, a LISP interpreter running
>ph> LISP really is running LISP: it's no less really computation than if one
>ph> had hardware devoted to the task.
>ph> The key is that Searle-in-the-room is not doing everything the
>ph> computer 'does', and is not going through the same series of
>ph> states. For example, suppose the program code at some point calls
>ph> for the addition of two integers. Somewhere in a computer running
>ph> this program, a piece of machinery is put into a state where a
>ph> register is CAUSED to contain a numeral representing the sum of
>ph> two others. This doesn't happen in my head when I work out, say,
>ph> 3340 plus 2786, unless I am in some kind of strange arithmetical
>ph> coma.
>
>sh> Your AI symbol systems, be they ever so interpretable AS IF they had
>
>sh> someone home, no more have someone home than symbolic fires, be they
>
>sh> ever so interpretable as burning, burn.
>
>sh> I agree with Dave Chalmers's criteria for determining what computation
>
>sh> and computers are, but, as I suggested earlier, the question of whether
>
>sh> or not COGNITION is computation is a second, independent one, and on
>
>sh> this I completely disagree.
>dc> I've been following the recent "What is computation?" discussion with
>dc> some bemusement, as it seems to me that most of the discussion is just
>dc> irrelevant to the question at hand. There are at least three questions
>dc> here that have to be distinguished:
>dc>
>dc> (1) When is a given computation physically implemented?
>dc> (2) Does computational structure determine mental properties?
>dc> (3) Does computational structure determine semantic content?
>dc>
>dc> I take it that the original challenge was to answer question (1),
>dc> giving appropriate criteria so that e.g. John Searle's wall doesn't end
>dc> up implementing every computation.
>dc> In my earlier contribution to this
>dc> discussion, I outlined an appropriate criterion:
>dc>
>dc> > (*)A physical system implements a given computation when there
>dc> > exists a mapping from physical states of the system onto the
>dc> > formal states in the computation such that the causal
>dc> > state-transition relations between the physical states mirror
>dc> > the formal state-transition relations between the corresponding
>dc> > computational states.
>dc>
>dc> This criterion seems to do everything that's required, and nobody seems
>dc> to have problems with it (except for Brian Smith's comment; see below).
>dc> Your (Stevan's) response to this was:
>dc>
>sh> I agree with Dave Chalmers's criteria for determining what
>sh> computation and computers are, but, as I suggested earlier, the
>sh> question of whether or not COGNITION is computation is a second,
>sh> independent one, and on this I completely disagree.
>dc>
>dc> You then invoke the Chinese-room argument, thus, somewhat inevitably,
>dc> setting off the discussion of questions (2) and (3) that overwhelmed
>dc> the original question. Well and good, perhaps, but irrelevant to the
>dc> question at hand. If Searle is right, then *whatever* computation is,
>dc> it doesn't suffice for mentality.
>dc> > The computationalist claim is that cognition *supervenes* on
>dc> > computation, i.e. that there are certain computations such that
>dc> > any implementation of that computation will have certain cognitive
>dc> > properties.
>dc> There is probably some more to be said here -- e.g. about the precise
>dc> requirements on the state-transition relations, and whether there
>dc> should be a stronger requirement of causality than simple sustaining of
>dc> counterfactuals; and also problems about just what counts as a given
>dc> input or output -- but those questions fall into the "technical"
>dc> basket. I don't think that there are serious objections to the view
>dc> here.
>dc> (2) Does computational structure determine mental properties?
>dc>
>dc> ...the question here should really be seen as: for a given mental
>dc> property M, is there a computation C such that any physical system that
>dc> implements C will possess M. A believer in "strong AI" or
>dc> "computationalism", or whatever you want to call this view, says yes,
>dc> at least for some subset of mental properties. (There is obviously a
>dc> problem for mental properties that even in the human case depend partly
>dc> on what's happening outside the body, e.g. knowledge, and somewhat
>dc> controversially belief. Computational structure won't determine any
>dc> mental properties that internal physical structure doesn't, so we'll
>dc> stick to "intrinsic" properties for now, but see (3) below.)
>dc> Why should computational structure determine mental properties, given
>dc> the criterion (*) for computational structure? Because (*) says that
>dc> computational structure is a variety of *causal* structure. In fact, it
>dc> seems that for just about any pattern of causal structure that we want
>dc> to capture, we can specify a computation such that any implementation
>dc> of the computation has the requisite causal structure. (This is a long
>dc> story, though.) So on this view, computationalism coheres very well
>dc> with functionalism, the view that mentality is dependent on causal
>dc> structure.
>dc> Why should mentality be dependent on causal structure? Mostly because
>dc> it seems unreasonable that it should depend on anything else. Mentality
>dc> seems obviously to be dependent on *some* aspect of physical makeup,
>dc> and the intuition behind functionalism is simply that physical
>dc> properties that don't contribute to causal organization are going to be
>dc> irrelevant to mental life. E.g. if we gradually replaced neural tissue
>dc> with silicon modules that play an identical causal role, it seems
>dc> counterintuitive that mentality would gradually fade out.
>dc> Note that we
>dc> now have two separate questions:
>dc>
>dc> (2a) Does causal structure fix mental properties?
>dc> (2b) Does computational structure fix causal structure?
>dc>
>dc> The usual functionalist arguments, e.g. above, support (2a), and the
>dc> criterion in (1) is designed precisely to support (2b). It's possible
>dc> that one might even accept (2a) and (2b) but still not be a
>dc> computationalist, because one held that the causal structures on which
>dc> mentality depends can't be specified computationally (e.g. because
>dc> they're inherently analog). I suspect that your (Stevan's) view may
>dc> fall into this category. I think there are good reasons why this view
>dc> can't be sustained, tied up with the universal nature of computation
>dc> and Church's thesis, but these are too complex to get into here.
>dc> I'll bring up the Chinese room just for completeness. If Searle is
>dc> right about the Chinese room, then computational structure simply
>dc> doesn't determine mental properties, and computation suddenly becomes a
>dc> whole lot less important to cognitive science.
>dc> But of course the computationalist doesn't accept Searle's argument.
>dc> (The Systems reply is the right reply, but let's not get into that.)
>dc> (2.5) Interlude: On phenomenal properties and semantic content.
>dc>
>dc> These discussions of the big questions about Mind tend to focus on
>dc> phenomenal properties (or "consciousness", or "qualia", or whatever)
>dc> and rightly so, as these are where the really hard questions arise.
>dc> However, not every mental property is a phenomenal property. In
>dc> particular, it seems to many people, me included, that intentional
>dc> properties such as belief are best individuated by their role in the
>dc> causation of behaviour, rather than by the way they feel. Beliefs may
>dc> have qualia associated with them, but these qualia don't seem to be
>dc> essential to their status as beliefs.
>dc> Your position seems to be, on the contrary, that qualia are
>dc> determinative of semantic content. Take Joe, sitting there with some
>dc> beliefs about Joan of Arc. Then a hypothetical system (which is at
>dc> least a conceptual possibility, on your view and mine) that's
>dc> physically identical to Joe but lacks qualia, doesn't believe anything
>dc> about Joan of Arc at all. I suggest that this seems wrong. What can
>dc> qualia possibly add to Joe's belief to make them any more about Joan
>dc> than they would have been otherwise? Qualia are very nice things, and
>dc> very important to our mental life, but they're only a matter of *feel*
>dc> -- how does the raw feel of Joe's belief somehow endow it with semantic
>dc> content?
>dc> I suggest that there is some kind of conceptual confusion going on
>dc> here, and that phenomenal and semantic properties ought to be kept
>dc> separate. Intentional states ought to be assimilated to the class of
>dc> psychological properties, with their semantic content conceptually
>dc> dependent on their role in our causal economy, and on their causal
>dc> relations to entities in the external world.
>dc> (3) Does computational structure determine semantic content?
>dc>
>dc> Now that we've got semantic content separated from phenomenal feel, we
>dc> can address this as a semi-independent issue.
>dc>
>dc> The first thing to note is that some people (yourself included, in
>dc> places) have suggested that semantic content is *constitutive* of
>dc> computational structure. This is an interesting question, which has to
>dc> be kept separate from (3). I endorse Drew McDermott's line on this.
>dc> Computation is a *syntactic* concept (give or take some possible
>dc> semantics at the inputs and the outputs). If you look at the original
>dc> papers, like Turing's, you don't see anything about semantics in there
>dc> -- a Turing machine is characterized entirely by its syntactic
>dc> structure. Now, it may turn out that computational structure ends up
>dc> *determining* semantic content, at least to some extent, but that
>dc> doesn't make semantics constitutive of computational structure.
>dc> This issue is confused somewhat by the fact that in common parlance,
>dc> there are two different ways in which "computations" are
>dc> individuated. This can be either syntactically, in terms of e.g.
>dc> the Turing machine, FSA, or algorithm that is being individuated,
>dc> or semantically: e.g. "the computation of the prime factors of
>dc> 1001", or "the computation of my tax return". These different
>dc> uses cross-classify each other, at least to some extent: there
>dc> are many different algorithms that will compute my tax return.
>dc> I suggest that the really fundamental usage is the first one;
>dc> at least, this is the notion of computation on which "strong AI"
>dc> relies. The semantic individuation of computation is a much more
>dc> difficult question; this semantic notion of computation is
>dc> sufficiently ill-understood that it can't serve as the foundation
>dc> for anything, yet (and it would be more or less circular to try
>dc> to use it as the foundation for "strong AI"). Whereas the syntactic
>dc> notion of computation is really quite straightforward.
>dc> That being said, is it the case that computational structure, as
>dc> determined by (*) above, is determinative of semantic content.
>dc> i.e. for any given intentional state with content M, is there a
>dc> computation such that any implementation of that computation has a
>dc> state with that content?
>dc> If content is construed "widely" (as it usually is), then the answer is
>dc> fairly straightforwardly no. Where I have beliefs about water, my
>dc> replica on Twin Earth has beliefs about twin water (with a different
>dc> chemical composition, or however the story goes). As my replica is
>dc> physically identical to me, it's certainly computationally identical to
>dc> me. So semantic content is not determined by computational structure,
>dc> any more than it's determined by physical structure.
>dc> However, we can still ask whether *insofar* as content is determined by
>dc> physical structure, it's determined by computational structure. A lot
>dc> of people have the feeling that the aspect of content that depends on
>dc> external goings-on is less important than the part that's determined by
>dc> internal structure. It seems very likely that if any sense can be made
>dc> of this aspect of content -- so-called "narrow content" -- then it will
>dc> depend only on the causal structure of the organism in question, and so
>dc> will be determined by computational structure. (In fact the link seems
>dc> to me to be even stronger than in the case of qualia: it at least seems
>dc> to be a *conceptual* possibility that substituting silicon for neurons,
>dc> while retaining causal structure, could kill off qualia, but it doesn't
>dc> seem to be a conceptual possibility that it could kill off semantic
>dc> content.) So if computations can specify the right kinds of causal
>dc> structure, then computation is sufficient at least for the narrow part
>dc> of semantic content, if not the wide part.
>dc> Incidentally, I suggest that if this discussion is to be published,
>dc> then only those parts that bear on question (1) should be included.
>dc> The world can probably survive without yet another Chinese-room
>dc> fest. This should reduce the material to less than 20% of its
>dc> current size. From there, judicious editing could make it quite
>dc> manageable.
>dc>
>dc> --Dave Chalmers
>
>sh> The purpose of defining computation is to put content into statements
>
>sh> such as "X is computation," "Y is not computation," "X can be done by
>
>sh> computation," "Y cannot be done by computation." As long as computation
>
>sh> is used vaguely, ambiguously, idiosyncratically or abitrarily,
>
>sh> statements like the above (some of which I'll bet you've made yourself)
>
>sh> are empty. In particular, if anyone ever wanted to say that "Everything
>
>sh> is rain" or "Rain is rain only if you think of it that way" or
>
>sh> "Thinking is just rain," you'd find you'd want to pin that definition
>
>sh> down pretty quick.
>ph> Date: Fri, 17 May 91 10:24 PDT
>ph> From: Hayes@MCC.COM (Pat Hayes)
>ph>
>ph> There is a mistake here (which is also made by Putnam (1975, p. 293)
>ph> when he insists that a computer might be realized by human clerks; the
>ph> same mistake is made by Searle (1990), more recently, when he claims
>ph> that the wall behind his desk is a computer)...
>ph>
>ph> Searle, J. R. (1990) Is the Brain a Digital Computer?
>ph> Presidential Address. Proceedings of the American Philosophical
>ph> Association.
>
>sh> There is a systematic misunderstanding here. I proposed semantic
>
>sh> interpretability as part of the definition of computation. A computer
>
>sh> would then be a device that can implement arbitrary computations.
>
>sh> We should keep it in mind that two semi-independent questions are
>
>sh> under discussion here.
>
>sh> The first has nothing to do with the mind. It just
>
>sh> concerns what computers and computation are.
>
>sh> The second concerns whether just a computer implementing a computer
>
>sh> program can have a mind.
>
>sh> I think the word "structure" is equivocal here. A computer simulation
>
>sh> of the solar system may have the right causal "structure" in that the
>
>sh> symbols that are interpretable as having mass rulefully yield
>
>sh> symbols that are interpretable as gravitational attraction and
>
>sh> motion. But there's no mass, gravity or motion in there, and
>
>sh> that's what's needed for REAL causality. In fact, the real
>
>sh> causality in the computer is quite local, having to do only
>
>sh> with the physics of the implementation (which is irrelevant to
>
>sh> the computation, according to functionalism). So when you
>
>sh> speak equivocally about a shared "causal structure," or about
>
>sh> computational structure's being a "variety of causal structure," I
>
>sh> think all you mean is that the syntax is interpretable AS IF
>
>sh> it were the same causal structure as the one being modelled
>
>sh> computationally. In other words, it's just more ungrounded,
>
>sh> extrinsic semantics.
>
>sh> I think I can safely say all this and still claim (as I do) that
>
>sh> I accept the Church/Turing Thesis that computation can simulate
>
>sh> anything, just as natural language can describe anything.
>
>sh> We just mustn't confuse the simulation/description with the real
>
>sh> thing, no matter how Turing-Equivalent they might be. So if we
>
>sh> would never mix up an object with a sentence describing it, why
>
>sh> should we mix up an object with a computer simulating it?
>
>sh> I certainly couldn't agree with you on computation without dissociating
>
>sh> myself from this part of your view. But let me, upon reflection, add
>
>sh> that I'm not so sure your criterion for computation does the job (of
>
>sh> distinguishing computation/computers from their complement) after all
>
>sh> (although I continue to share your view that they CAN be distinguished,
>
>sh> somehow): I don't see how your definition rules out any analog system
>
>sh> at all (i.e., any physical system). Is a planetary system a computer
>
>sh> implementing the laws of motion? Is every moving object implementing a
>
>sh> calculus-of-variational computation? The requisite transition-preserving
>
>sh> mapping from symbols to states is there (Newton's laws plus boundary
>
>sh> conditions). The state transitions are continuous, of course, but you
>
>sh> didn't specify that the states had to be discrete (do they?).
>
>sh> And what about syntax and implementation-independence, which are surely
>
>sh> essential properties of computation? If the real solar system and a
>
>sh> computer simulation of it are both implementations of the same
>
>sh> computation, the "supervenient" property they share is certainly none
>
>sh> of the following: motion, mass, gravity... -- all the relevant
>
>sh> properties for being a real solar system. The only thing they seem to
>
>sh> share is syntax that is INTERPRETABLE as motion, mass, gravity, etc.
>
>sh> The crucial difference continues to be that the interpretation of being
>
>sh> a solar system with all those properties is intrinsic to the real solar
>
>sh> system "computer" and merely extrinsic to the symbolic one. That does
>
>sh> not bode well for more ambitious forms of "supervenience." (Besides, I
>
>sh> don't believe the planets are doing syntax.)
>
>sh> I think the word "structure" is equivocal here. A computer simulation
>
>sh> of the solar system may have the right causal "structure" in that the
>
>sh> the symbols that are interpretable as having mass rulefully yield
>
>sh> symbols that are interpretable as gravitational attraction and motion.
>
>sh> But there's no mass, gravity or motion in there, and that's what's
>
>sh> needed for REAL causality. In fact, the real causality in the computer
>
>sh> is quite local, having to do only with the physics of the implementation
>
>sh> (which is irrelevant to the computation, according to functionalism).
>
>sh> So when you speak equivocally about a shared "causal structure," or
>
>sh> about computational structure's being a "variety of causal structure," I
>
>sh> think all you mean is that the syntax is interpretable AS IF it were
>
>sh> the same causal structure as the one being modelled computationally. In
>
>sh> other words, it's just more, ungrounded, extrinsic semantics.
>
>sh> There is a straw man being constructed here. Not only do all
>
>sh> Functionalists agree that mental states depend on causal structure, but
>
>sh> presumably most nonfunctionalist materialists do too (neurophysical
>
>sh> identity theorists, for example, just think the requisite causal
>
>sh> structure includes all the causal powers of -- and is hence unique to
>
>sh> -- the biological brain).
>
>sh> "Syntactic" means based only on manipulating physical symbol tokens
>
>sh> (e.g., squiggle, squoggle) whose shape is arbitrary in relation to what
>
>sh> they can be interpreted as meaning. I am sure one can make
>
>sh> squiggle-squoggle systems, with arbitrary formal rules for manipulating
>
>sh> the squiggles and squoggles -- like Hesse's "Glass Bead Game" but even
>
>sh> more absurd, because completely meaningless, hence uninterpretable in
>
>sh> any systematic way -- and one could perhaps even call these
>
>sh> "computations" (although I would call them trivial computations). But I
>
>sh> have assumed that whatever it turns out to be, surely one of the
>
>sh> essential features of nontrivial computations will be that they can
>
>sh> bear the systematic weight of a semantic interpretation (and that
>
>sh> finding an interpretation for a nontrivial symbol system will be
>
>sh> crytographically nontrivial, perhaps even NP-complete).
>
>sh> At some point (mediated by Brentano, Frege and others), the mind/body
>
>sh> problem somehow seems to have split into two: The problem of "qualia"
>
>sh> (subjective, experiential, mental states) and the problem of
>
>sh> "intentionality" (semantics, "aboutness"), each treated as if it were
>
>sh> an independent problem. I reject this bifurcation completely. I
>
>sh> believe there is only one mind/body problem, and the only thing that
>
>sh> makes mental states be intrinsically about anything at all is the fact
>
>sh> that they have experiential qualities.
>
>sh> If there were nothing it was like (subjectively) to have beliefs and
>
>sh> desires, there would be no difference between beliefs and desires that
>
>sh> were just systematically interpretable AS IF they were about X
>
>sh> (extrinsic semantics) and beliefs and desires that were REALLY about X
>
>sh> (intrinsic semantics).
>
>sh> There are qualia, however, as we all know. So even with a grounded
>
>sh> TTT-capable robot, we can still ask whether there is anybody home in
>
>sh> there, whether there is any haver of the beliefs and desires, to whom
>
>sh> they are intrinsically [i.e., subjectively] meaningful and REALLY about
>
>sh> what they are interpretable as being about. And we can still be dead
>
>sh> wrong in our inference that there is somebody home in there -- in which
>
>sh> case the robot's semantics, for all their causal groundedness, would in
>
>sh> reality be no more intrinsic than those of an ungrounded book or
>
>sh> computer.
> dc> Your position seems to be, on the contrary, that qualia are
> dc> determinative of semantic content. Take Joe, sitting there with some
> dc> beliefs about Joan of Arc. Then a hypothetical system (which is at
> dc> least a conceptual possibility, on your view and mine) that's
> dc> physically identical to Joe but lacks qualia, doesn't believe anything
> dc> about Joan of Arc at all. I suggest that this seems wrong. What can
> dc> qualia possibly add to Joe's belief to make them any more about Joan
> dc> than they would have been otherwise? Qualia are very nice things, and
> dc> very important to our mental life, but they're only a matter of *feel*
> dc> -- how does the raw feel of Joe's belief somehow endow it with semantic
> dc> content?
>
>
>sh> But Dave, how could anyone except a dualist accept your hypothetical
>
>sh> possibility, which simply amounts to the hypothetical possibility that
>
>sh> dualism is valid (i.e., that neither functional equivalence nor even
>
>sh> physical identity can capture mental states!)?
> dc> I suggest that there is some kind of conceptual confusion going on
> dc> here, and that phenomenal and semantic properties ought to be kept
> dc> separate. Intentional states ought to be assimilated to the class of
> dc> psychological properties, with their semantic content conceptually
> dc> dependent on their role in our causal economy, and on their causal
> dc> relations to entities in the external world.
>
>
>sh> Apart from real TTT interactions, I don't even know what this passage
>
>sh> means: what does "assimilated to the class of psychological properties
>
>sh> with their semantic content conceptually dependent on their role in our
>
>sh> causal economy" mean? "[T]heir causal relations to entities in the
>
>sh> external world" I can understand, but to me that just spells TTT.
>
>sh> (3) If qualia fade and the system stays TTT-grounded, I would say
>
>sh> aboutness was gone too (what would you say, and what would it amount to
>
>sh> to be WRONG about that, even from a God's-Eye view?)
>dc> It's *computation* that's implementation-independent, not
>dc> solar-system-hood... other implementations of that computation aren't
>dc> solar systems.
>dc>
>dc> The strong-AI hypothesis is that unlike these properties, *cognition*
>dc> is a property that supervenes on abstract causal organization. This may
>dc> or may not be obvious at first glance, but note that unlike digestion
>dc> and solar-system-hood, it's not ruled out at first glance: there
>dc> doesn't seem to be any physical property independent of causal
>dc> organization that's conceptually constitutive of cognition.
>dc> there's a certain [real] causal structure that every
>dc> implementation of a given computation shares. That's precisely what the
>dc> definition of implementation guarantees. When a given 2-state FSA is
>dc> implemented on my computer, for instance, there are real physical
>dc> state-types in the implementation such that being in state A causes a
>dc> transition into state B, and vice versa. When a neuron-by-neuron
>dc> simulation of the brain is implemented on my computer, there are real
>dc> physical states (registers, or memory locations, or whatever) in the
>dc> implementation corresponding to the state of each neuron, and these
>dc> states interact with each other in a causal pattern isomorphic to a
>dc> pattern of interaction among the neurons.
>dc> by "causal structure" I mean, roughly, *organizational* properties of a
>dc> system: i.e., the patterns of interactions between various states,
>dc> without taking into account what those states actually are... This has
>dc> to be kept quite separate from questions about semantics.
>dc> Given any specification of the causal structure of the brain -- even
>dc> all the way down to atoms, or whatever -- then that causal structure
>dc> could in principle be implemented in a different medium, such as
>dc> silicon. We'd just have to set it up so that our little bits of silicon
>dc> are interacting with each other according to the same patterns as the
>dc> neurons, or the atoms or whatever, were interacting with each other...
>dc> a neurophysiological identity theorist would not agree that mental
>dc> states supervene on causal structure.
>dc> Perhaps you don't agree that mental states depend solely on causal
>dc> structure either, because you seem to assign an essential role to I/O
>dc> transducers, and presumably it makes a difference just what kinds of
>dc> physical things -- heat, light, or whatever -- are being transduced.
>dc> Whereas a strict functionalist like myself would hold that at least
>dc> when it comes to fixing phenomenal mental states, the specific physical
>dc> nature of what's being transduced is irrelevant. On this view, a system
>dc> that merely reproduced the causal organization of the transduction in a
>dc> different medium would have the same phenomenal properties.
>dc> I don't think that "Is cognition computation?" is quite the right
>dc> question to ask. The right question, rather, is "Is computation
>dc> sufficient for cognition?" An advocate of strong AI might reasonably
>dc> hold that cognition in the brain is not itself computation, but that
>dc> computation is nevertheless capable of reproducing the relevant
>dc> properties (e.g. causal structure) on which cognition depends...
>dc> I certainly don't think that the brain is a Turing machine, but I think
>dc> that nevertheless Turing machine computations are capable of cognition.
>dc> It's a subtle point, but too often advocates of AI are saddled with
>dc> unnecessary claims such as "the brain is a computer", or "the mind is a
>dc> program".
>dc> The question is only whether semantic content is itself *constitutive*
>dc> of something's being a computation. To that question, the answer seems
>dc> obviously to be no. Construct an arbitrary large Turing machine by
>dc> throwing together quadruples randomly. It's most unlikely that there
>dc> will even *be* a nontrivial semantic interpretation for this. It's
>dc> probably not a very interesting computation, but it's computation.
>dc>
>dc> Most interesting computations will probably turn out to have some kind
>dc> of semantic interpretation -- otherwise why would we bother with them?
>dc> ... But the notion that lies at the foundation of the computationalist
>dc> view about cognition is not "interesting computation", it's
>dc> "computation" straight. Making some sense of the notion of "interesting
>dc> computation" is an interesting question in its own right, but it's
>dc> independent of Searle's original question about what makes something a
>dc> computation.
>dc> To set out the lay of the land, I agree that there's only one mind-body
>dc> Problem worthy of a capital P, and that's the problem of qualia. That's
>dc> not to say that qualia are the only kind of mental states... there are
>dc> also "psychological states", those characterized by their role in the
>dc> production of behaviour rather than by their phenomenal feel. However,
>dc> there's no more a mind-body Problem about these than there is a
>dc> "life-body Problem"... There's no "further fact" that needs
>dc> explaining... What's special about qualia, and makes them seem unlike
>dc> almost everything else in the world, is that there seems to be a
>dc> further fact in need of explanation, even after one has told the full
>dc> story about the mechanisms and so on.
>dc>
>dc> *All there is* to the fact of a system believing that P is that it has
>dc> the right kind of causal economy, with mechanisms that tend to produce
>dc> P-appropriate behaviour in the right sort of ways, and that are
>dc> causally related to the subject matter of P in the right sort of way.
>dc> The possession of semantic content isn't a further fact over and above
>dc> these mechanisms: it *conceptually supervenes* on the existence of
>dc> those mechanisms, to use the philosophical parlance.
>dc> Obviously, any system that is functioning in the right way is not
>dc> just "as if" alive, it's really alive, qualia or no qualia. The
>dc> same goes for belief. Maybe this means that there's not much
>dc> difference between "as if" believing and "real" believing, but why
>dc> should that bother us? We don't worry about a difference between
>dc> "as if" tables and "real" tables, after all.
>dc>
>dc> Qualia or no qualia, beliefs are still "intrinsic" (modulo questions
>dc> about narrow and wide content), in just the same way that life is
>dc> intrinsic. It's just that they're not *phenomenal*.
>dc> qualia seem to be *the wrong kind of thing* to
>dc> determine that content (except perhaps for certain kinds of perceptual
>dc> content). As I said earlier, my belief about Joan of Arc may have some
>dc> associated (though hard to pin down) qualia, but it's very difficult to
>dc> see how those qualia are *constitutive* of the semantic content of the
>dc> belief. How could the *feel* of the belief possibly make it any more
>dc> about Joan of Arc than it would have been otherwise?
>dc> Your position, I take it, is roughly that: "as if" semantic content
>dc> *plus* qualia *equals* "real" semantic content. My position is that
>dc> qualia seem to contribute almost nothing to fixing the semantic content
>dc> of most beliefs, except perhaps for certain perceptual beliefs. So
>dc> whatever it is that is constitutive of "real" semantic content, qualia
>dc> don't play much of a role. This may mean that there won't be much of a
>dc> "real"/"as if" distinction to worry about (modulo the considerations
>dc> about behavioural equivalence), but that's life.
>dc> Take Joe, sitting there with some beliefs about Joan of Arc. Then a
>dc> hypothetical system (which is at least a conceptual possibility, on
>dc> your view and mine) that's physically identical to Joe but lacks
>dc> qualia, doesn't believe anything about Joan of Arc at all. I suggest
>dc> that this seems wrong. What can qualia possibly add to Joe's belief to
>dc> make them any more about Joan than they would have been otherwise?
>dc>
>dc> Well, I'm only saying that this is a *conceptual* possibility, which
>dc> surely it is on your view and mine, not an empirical possibility... But
>dc> it's entirely coherent to *imagine* a system physically identical to me
>dc> but lacking qualia... So far this view doesn't immediately imply dualism.
>dc> At least, many people who take qualia seriously accept this conceptual
>dc> possibility, but still think that ontologically, qualia aren't anything
>dc> over and above the physical... Personally, I find this view untenable...
>dc> As for the TTT, I suggest carefully distinguishing the *conceptual*
>dc> from the *empirical* dependence of mental properties on TTT-function. I
>dc> take it that you accept empirical but not conceptual dependence (as you
>dc> say, it's conceivable that the TTT might be wrong). By contrast, the
>dc> analytic functionalist holds that... all there is to the notion of a
>dc> system's being in a mental state is that it has a certain causal
>dc> organization, and that it's appropriately related to the environment...
>dc> this is an unsatisfying analysis of phenomenal mental states such as
>dc> qualia, but... goes through quite well for most other mental states,
>dc> such as beliefs.
>dc> Well, I think that it's empirically most unlikely that qualia *would*
>dc> fade [as synthetic parts are swapped for natural ones in the brain],
>dc> as this would mean that phenomenal states and psychological states were
>dc> radically "decoherent" from each other, in a subtle sense... But it's
>dc> certainly a conceptual possibility. So... I'd say that [aboutness]
>dc> would still be there. What would it amount to to be wrong about that?
>dc> The same sort of thing it would amount to to be wrong about a system's
>dc> being alive -- e.g., that one had misanalyzed the functional capacities
>dc> of the system. Aboutness is no more of an extra, free-floating fact
>dc> about a system than life is.
>
>sh> The second concerns whether just a computer implementing a computer
>
>sh> program can have a mind.
>dm> I despair of ever making progress on this question without further
>dm> empirical progress on computational modeling of thought and
behavior.
>dm> The ratio of verbiage produced to opinions changed is depressingly
>dm> small.
>
>sh> Three basic points characterize my disagreement with David Chalmers:
>
>sh> (1) Computational structure is not the same as causal structure. When
>
>sh> a digital computer simulates an airplane, they are computationally
>
>sh> equivalent but they are not causally equivalent. Causal equivalence
>
>sh> would mean having the same causal powers, in the same "medium" (except
>
>sh> for causally irrelevant implementational differences). An internal
>
>sh> combustion and electric plane would be causally equivalent in their
>
>sh> capacity to fly in the air. A simulated airplane and a real airplane
>
>sh> are not causally equivalent but only formally equivalent (in some
>
>sh> respects).
>
>sh> (2) What makes thinking different from flying is NOT that it
>
>sh> "supervenes" on causal structure the way, say, life might, but that it
>
>sh> is UNOBSERVABLE (or rather, observable only to the thinker). This is
>
>sh> what allows us to forget the differences between simulated thinking
>
>sh> and real thinking in a way that we cannot do with simulated flying
>
>sh> and real flying.
>
>sh> (3) The "aboutness" of thinking is not independent of the question of
>
>sh> qualia, it is completely parasitic on it. A system that has no qualia
>
>sh> has no aboutness, because there is no one home in there for the symbols
>
>sh> to be "about" anything TO.
>
>sh> (3) Searle's Chinese Room Argument and my Symbol Grounding Problem
>
>sh> apply only to discrete symbolic computation. Searle could not implement
>
>sh> analog computation (not even transduction) as he can symbolic
>
>sh> computation, so his Argument would be moot against analog computation.
> bm> a physical device is an analog computer to the extent that we
> bm> choose and intend to interpret its behavior as informing us about
> bm> some other system (real or imaginary) obeying the same formal
> bm> rules. To take an extreme example, we could use the planets as an
> bm> analog computer...
>
>
>sh> (2) If all dynamical systems that instantiate differential equations
>
>sh> are computers, then everything is a computer (though, as you correctly
>
>sh> point out, everything may still not be EVERY computer, because of (1)).
>
>sh> Dubbing all the laws of physics computational ones is duly ecumenical,
>
>sh> but I am afraid that this loses just about all the special properties
>
>sh> of computation that made it attractive (to Pylyshyn (1984), for
>
>sh> example) as a candidate for capturing what it is that is special about
>
>sh> cognition and distinguishes it from from other physical processes.
>
>
>sh> (3) Searle's Chinese Room Argument and my Symbol Grounding Problem
>
>sh> apply only to discrete symbolic computation. Searle could not implement
>
>sh> analog computation (not even transduction) as he can symbolic
>
>sh> computation, so his Argument would be moot against analog computation.
>
>sh> A grounded TTT-passing robot (like a human being and even a brain) is
>
>sh> of course an analog system, describable by a set of differential
>
>sh> equations, but nothing of consequence hangs on this level of
>
>sh> generality (except possibly dualism).
>sh> Much of Yee's comment is based an a distinction between formal and
>sh> nonformal "computation," whereas my arguments are based completely on
>sh> computation as formal symbol manipulation. We will need many examples
>sh> of what nonformal computation is, plus a clear delineation of what is
>sh> NOT nonformal computation ... (It would also seem hard to
>sh> pose these questions without talking about computers, as Yee enjoins
>sh> us!)
>sh> One cannot make coherent sense of this [the distinctions being made]
>sh> until the question "What is computation?", as posed in the header
>sh> to this discussion, is answered. Please reply in ordinary language
>sh> before turning again to technical formalisms, because this first pass
>sh> at formalism has merely bypassed the substantive questions that have
>sh> been raised.
>sh> Please give examples of what are and are
>sh> not "non-universal TM computations" and a principled explanation
>sh> of why they are or are not.