AI FOR SCHOOLS
(An ancient web page, with links to newer pages)

Aaron Sloman
http://www.cs.bham.ac.uk/~axs
School of Computer Science
The University of Birmingham, UK

This file is
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/aiforschools.html

NEWS 4 Jul 2007

Slides for Open Day presentation: ''What is Artificial Intelligence?''
Available here:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/whatsai.openday.pdf

NEWS 15 Mar 2007

I have formulated a proposal for a syllabus introducing Artificial Intelligence as a school subject, taught in the last two years of school, here
http://www.cs.bham.ac.uk/~axs/courses/alevel-ai.html

NOTE 1: This document was originally produced (in great haste) for circulation to attendees at a meeting for school careers advisers in Bromsgrove on Tuesday 9th June 1998.

NOTE 2: As of mid July 1999 the Poplog system, including several AI programming languages, is available free of charge, along with a great deal of AI and Cognitive Science teaching material at
http://www.cs.bham.ac.uk/research/poplog/freepoplog.html

For samples of the teaching materials see this Examples web site:
http://www.cs.bham.ac.uk/research/projects/poplog/examples

What is Artificial Intelligence?

Introduction: What is AI?

My colleague Russell Beale once suggested a useful introductory definition of Artificial Intelligence (AI) for people who know nothing about it:
"AI can be defined as the attempt to get real machines to behave like the ones in the movies."

This may give an inkling of what a lot of AI research involves, but it leaves out important facets of AI, especially its scientific aspects. No short definition adequately captures the variety of research goals and topics covered by AI, so I'll offer a description rather than a definition.

AI is a relatively new discipline (born in the middle of the 20th century). It is increasingly frequently mentioned in newspapers, magazines, on TV, in films, and in various kinds of computer entertainments, yet it is not widely understood. Some people even foolishly think it has already failed and been abandoned, whereas in fact it is steadily growing in academe and industry, though the work is not always labelled as "Artificial Intelligence". That is because some of the important ideas and techniques have been absorbed into software engineering.

Some other people equally foolishly make the opposite claim that AI is progressing so fast that there is a danger that within a few years robots will be taking over and perhaps keeping humans as pets. This fails to take account of the really challenging difficulties in understanding many of the capabilities of humans and other animals that we cannot yet replicate in software systems or robots, for instance the visual and motor control abilities of a squirrel in a tree-top or the language learning capabilities of human toddlers.

AI is not taught or even mentioned in many schools and relatively few universities offer undergraduate degree courses in AI. Yet it is a central part of one of the most profound scientific and intellectual developments of the last century: the study of information, how it can be acquired, stored, manipulated, extended, used, and transmitted, whether in machines, or humans or other animals.

Physics and chemistry study matter, energy, forces, and the various ways they can be combined and transformed. Biology, geology, medicine, and many other sciences and engineering disciplines build on this by studying more and more complex systems built from physical components. All this research requires an understanding of naturally occurring and artificial machines which operate on forces, energy of various kinds, and transform or rearrange matter.

But some of the machines, both natural and artificial, also manipulate knowledge.

It is now clear that a new kind of science is required for the study of the principles by which

We could call it the science of knowledge or the science of intelligence.

This is what AI is about. Not only artificial systems, but also human beings and many living organisms acquire, manipulate, store, use and transmit information. They are also driven or controlled by it: e.g. made happy by praise, made sad by bad news, made afraid by noises in the dark, made jealous by seeing the behaviour or possessions of others, and so on. From this standpoint it is not surprising that in recent years the study of emotions has been growing in importance in AI.

So AI, despite its unfortunate name, is about natural information processing systems as well as artificial systems, and not just about how they perceive learn and think, but also about what they want and how they feel. It has already had a profound impact on the study of human minds.

AI Overlaps with several other disciplines.

If we construe AI in this way (as studying how information is acquired, processed, stored, used, etc. in intelligent animals and machines) then it obviously overlaps with several older disciplines, including, for instance, psychology, neuroscience, philosophy, logic, and linguistics.

What is new in AI is that the development of computers has given us new ways of investigating the problems. Previously psychologists and brain scientists could only observe and do experiments on existing information processing systems, such as human beings and other animals; and philosophers could only theorise in the abstract about how mind and language ought to work. Now, however, we can go beyond those methods and also "play God", that is we can design new kinds of working systems to demonstrate the implications of our theories and check out whether they can explain the facts they are intended to explain.

That new possibility arises because computers were designed specifically to acquire, store, manipulate and use information, unlike older machines which were designed to transform energy or apply forces to manipulate matter or to produce chemical transformations.

Computers, unlike previous machines, enable us to express our theories about how minds work in the form of working computer programs that enable a machine to do some of the things people do, e.g. to communicate, learn, solve problems, understand input from TV cameras, control artificial limbs, etc.

Some of the things we have learnt

Designing machines with such capabilities has proved far more difficult than many of the early researchers expected. In part that is because many tasks which at first seemed simple turned out to have hidden depths. For instance, seeing is not just a matter of recognising patterns in visual images, but involves making sense of the environment, including understanding all the many ways it can help or hinder us. Similarly, the ability to understand and use a natural language, like English, or French, or Urdu, turned out to be far more complex than some of the early researchers thought.

To study and model these complexities, we have had to invent entirely new ways of thinking about the processes involved, including developing new languages in which to express the ideas, such as new kinds of programming language. (AI researchers often find the languages used by other programmers, e.g. Pascal, C, C++, Java, too restrictive.)

Even "stupid" people have considerable intelligence

This research has helped to reveal a great deal of shallowness in our normal thinking about minds, consciousness, perception, learning, language, and so on. In particular, we now understand that the kinds of people we might normally describe as ``stupid'' are far more sophisticated than any machine we currently know how to design. I know of no robot which could be trusted to clear dishes and cutlery from a dinner table and wash them at the kitchen sink, yet the majority of people can do this, without being specially intelligent.

It has proved much easier to design and implement machines which do the sorts of things which we previously thought required special intelligence, like the ability to play chess, do algebra, or perform calculations. These sorts of task fit more readily into a computer's mechanisms for manipulating large numbers of precisely defined symbols very rapidly, according to precisely defined rules.

We now understand much better that many commonplace human and animal abilities (e.g. a squirrel leaping among branches of a tree, a bird building its nest, a child listening to a story) involve a very deep kind of intelligence and important and subtle kinds of knowledge, which our theories do not yet accommodate. Likewise animal intelligence includes things like desires, enjoyment, suffering, and various forms of consciousness, all of which play an important role in their information processing, but which we hardly understand as yet.

Many AI researchers are trying to find ways of extending the concepts, theories, mechanisms and models in AI to include all those things. Their work includes trying to find ways of programming computers so that they have the kinds of richness and flexibility required for animal abilities. The design of artificial neural nets, flexible rule interpreters, and various kinds of self-organising software systems are among the approaches being followed.

As mentioned above, some of us are also investigating ways of building AI systems with the sorts of mechanisms involved in motivation, moods and emotions, as well as the more obviously required capabilities like perception, reasoning, problem solving and motor control. (My own work in this area, along with work by colleagues, can be found here.)

It should be clear from all this that insofar as AI includes the study of perception, learning, reasoning, remembering, motivation, emotions, self-awareness, communication, etc. it overlaps with many other disciplines, especially psychology, philosophy and linguistics. But it also overlaps with computer science and software engineering, because it includes the design of new kinds of information processing systems, either to model those in humans or to solve practical problems (e.g. software controlling a robot or factory, or software helping a child to learn about arithmetic).

Brains and computers: AI and neural nets

Some people in AI have been impressed by the fact that the mechanisms of brains are very different in detail from those in computers, even though they may be doing similar sorts of things (storing, transforming, using information). This has led to the investigation of neural nets partly inspired by ideas about how brains work.

Some artificial neural nets have developed entirely as practical solutions to engineering problems without much concern for accurate modelling of brain mechanisms. More recent work attempts to move towards more and more accurate models of real neurons, which are incredibly complex and varied.

Some people think that it will never be possible to understand and replicate all the important aspects of brain function unless we replace computers with new kinds of machines, or perhaps build hybrid machines using different technologies. This conclusion is premature. There are two reasons:

So we cannot yet say with confidence that there's ANYTHING brains can do which computers will NEVER be able to do, even though there are many things brains can do which existing computers cannot do (and vice versa!).

AI and simulated evolution

Another recent development related to AI is work on simulated evolution. Biological evolution managed to produce an enormous variety of living organisms closely suited to different sets of needs in different environments. By modelling those processes on computers it turns out that we can sometimes get the computers to evolve solutions to problems that we were previously unable to find.

Genetic algorithms (GAs) are increasingly being used both as a research tool and as a means of getting computers to solve practical problems. They use strings of symbols to encode specifications for designs, or solutions to problems, in something like the way biological systems use strings of molecules in DNA. Transforming and recombining portions of strings enables an evolutionary computation to search for good designs or good solutions, in a manner that is partly analogous to biological evolution.

Genetic Programming (GP) extends these ideas by using structures which are better suited to the problem than strings are. For instance, a GP system may directly manipulate tree-like structures representing rules or computer programs.

This work links up with studies in Artificial Life (Alife), which is concerned with simulated evolution of various kinds of artificial organisms, possibly competing or collaborating with one another.

Evolutionary techniques may use AI in the systems they evolve. Similarly AI systems may use evolutionary techniques to help with some of the harder problem solving tasks.

AI and real evolution
Added 13 Aug 2018

The Meta-Morphogenesis project, begun in 2012, triggered by the Alan Turing Centenary celebrations, attempts to use computational ideas, including what we have learnt from AI, but going beyond that, to investigate changes in information processing in biological organisms between the very simplest single-celled organisms (and possibly pre-biotic entities) and current organisms of all kinds.

The project overview is here:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html

A major theme is evolution of biological construction kits of many kinds
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/construction-kits.html

Some implications

This new multi-disciplinary field brings together a variety of old disciplines in an entirely new way because we are constantly learning new techniques for building working systems that extend and test ideas and theories synthesised from the different disciplines.

This will have increasing practical importance as we continue to develop more and more sophisticated information processing machines performing more and more tasks (at home, at school, in factories, in offices, in hospitals, on the internet...).

This research also helps us deepen our understanding of what we are, how we relate to other kinds of animals and also how we relate to other kinds of machines, including machines of the future, which may become more and more like us. For example, by designing machines with various kinds and degrees of autonomy we can clarify old problems about the nature of free will. Instead of there being one kind of "free will" which you either have or do not have, we find there are many different kinds of freedom, and different people, different animals, and different machines will have different subsets -- different kinds of freedom.

Some of them can then be seen to be more important than others. For instance, it is important to have the freedom to resist external coercion and take decisions and act under the influence of your own desires, preferences, knowledge. It is not so important to have the freedom arbitrarily and randomly to change your own desires, preferences and knowledge.

These considerations are as relevant to some intelligent robots as they are to humans and other animals.

AI and Computer Science

How does AI relate to computer science, another new discipline? In part it is like the relationship between physics and mathematics. Mathematics develops many concepts and techniques which physics uses, but the central goal of physics is to understand the world, not to understand those techniques. Likewise computer science (along with mathematics, electronic engineering and software engineering) develops general theories about information processing, and helps to create powerful general tools (e.g. computers, operating systems, and compilers) which are used in AI, but these are not the central focus of AI. The general concepts, techniques and tools produced by computer science are used by AI researchers in the process of studying something else, the kinds of information processing capabilities which we find in many living organisms, and which might also be created in new machines of many kinds.

However, just as the history of physics includes many episodes where mathematics was enriched by the work of theoretical physicists, so also has AI had a great deal of influence on the development of computer science. But equally it is having a deep impact on other disciplines with which it is connected, especially philosophy, psychology and linguistics. I think its impact on other disciplines will continue to grow and diversify, including psychiatry, brain studies, biology, and many forms of engineering.

Studying AI at University

Although AI degrees have been available in a few UK universities since the early 1980s or earlier (e.g. Edinburgh, Sussex), until recently there were very few universities which offered degrees with AI or Cognitive science in the title.

However, since the mid 1990s the importance and interest of the subject has been more widely recognized, and the number of universities offering such degrees has expanded rapidly. In most cases AI is not offered on its own, but in combination with one or more other disciplines such as computer science, or psychology, or mathematics, or an engineering subject, or an arts subject such as philosophy or English.

I don't claim to have a complete list, but the UK universities that now offer undergraduate degrees in AI (or in a few cases using other titles, e.g. "Cognitive science, or "Intelligent systems") include the following:
Aberdeen Aberystwyth (Univ. of Wales), Birmingham, Durham, Edinburgh, Essex, Exeter, Hertfordshire, Leeds, Luton, Manchester, Middlesex, Nottingham, Oxford Brookes, Royal Holloway (London), Sheffield, Staffordshire, Sussex, UMIST, University College London, Westminster
(That list was compiled in the Summer of 1998, and is probably out of date by now.)

Degrees in computer science "with" AI (i.e. less than half of the degree is AI) are available from several other universities, e.g. Southampton.

Many degrees in computer science include one or two course in AI though often these give only a narrow view of the subject. There are probably also some psychology degrees which include some optional AI courses.

There are also some universities which offer postgraduate degrees in AI or cognitive science (e.g. MSc, PhD) even though they don't offer undergraduate degrees in this area.

Not all university teachers will agree with the view of AI presented here, and it is likely that AI degrees will differ considerably in their content because of different local expertise and different views of the subject. E.g. some will be more engineering oriented and some more science oriented. Some of the latter may be closely linked to psychology, others to philosophy, logic or mathematics. Applicants for university degree courses should therefore look carefully at what is on offer before choosing.

Entry requirements

These vary considerably. Some places will require A-level mathematics, while others assume that many really able students have the required potential even if they have not studied mathematics formally. Probably all require at least a C in mathematics at GCSE. Most will require at least one science subject at A level.

Anyone who does a degree in AI must enjoy programming computers as programming exercises and projects are likely to play a significant role in the course. Because the programming tasks in AI degrees usually involve attempting to give computers abilities or skills which humans or animals have, students often find them more interesting than the programming tasks computer science or software engineering courses.

However not all the degree courses require previous programming experience, since very often someone with no such experience can learn programming at university and do well.

Anyone who has not had previous programming experience and is considering doing a degree in AI (or computer science) is strongly advised to spend a little time learning programming not because that is a pre-requisite but because it is a way of discovering before it is too late that you don't like programming.

Usually courses in AI (and cognitive science) include more essay writing than typical science or engineering courses. Some even include a chance to study philosophical problems, e.g. about the relation between mind and body.

Employment prospects

I have not done a systematic investigation, but my impression is that students with degrees in AI find it easier than most graduates to get jobs. They are as employable as computer science graduates. That is because as computers become cheaper and more powerful the requirement and the opportunities to give them more intelligence are growing rapidly.

Many graduates with AI degrees go on to do research, sometimes after switching to a related discipline such as psychology or philosophy.

Reading to find out more

In 1999 I was on a committee appointed by the UK Quality Assurance Agency to produce a "benchmarking" document to be used by panels assessing teaching in Computer Science departments. As part of the work of the panel I produced, in consultation with a number of people at other universities, a slightly more formal document than this one, giving an overview of the scope of AI and some of its main features. It can be read at http://www.cs.bham.ac.uk/~axs/courses/ai.html

A not very long online document written in question-and-answer form attempting to answer the question "What is AI", written by the person who invented the name for this field, can be found at John McCarthy's web site at Stanford University, California.

Another useful online source, provided by one of the founders of AI, is Marvin Minsky's web site at MIT in Cambridge Massachusetts. He has a useful online paper discussing various approaches to AI and the need to combine them.

The New Scientist (a UK popular science weekly) often has articles on AI (even when the phrase "artificial intelligence" is not used).

A collection of web pages on AI produced for the American Association for Artificial Intelligence can be found at http://www.aaai.org/aitopics

You may find something useful in the AI section of eg3.com

BOOKS
There are many text books on AI. Some useful introductory reading can be found in:

Mike Sharples, et al. Computers and Thought, MIT Press, 1989. (This is now available online at the Free Poplog Site
here.)

Stan Franklin, Artificial Minds, Bradford Book (MIT Press) 1995.

Hans Moravec, Mind Children: The Future of Robot and Human Intelligence, Harvard University Press (Cambridge, Mass; London, England), 1988

There are many more technical introductions to AI, including

Nils J Nilsson (1998) Artificial Intelligence, a New Synthesis, Morgan Kaufmann Publishers, San Francisco.

Stuart Russell & Peter Norvig (1995) Artificial Intelligence, A Modern Approach. Prentice Hall.

Elaine Rich and Kevin Knight. (1991). Artificial Intelligence Second Edition, New York: McGraw-Hill.

P.H. Winston, (1984 - 3rd edition 1992). Artificial Intelligence. Addison-Wesley.

Peter Jackson, 1990 Introduction to expert Systems (Second Edition) Addison Wesley

G.F. Luger, W.A. Stubblefield, 1993 Artificial Intelligence: Structures and strategies for complex problem solving, 2nd Ed, Benjamin/Cummings Publishing, Redwood City, Calif. (Not elementary. 3rd edition 2001).

Chris Thornton & Benedict du Boulay (1992) Artificial Intelligence Through Search Kluwer Academic (Paperback version Intellect Books)

Johnson-Laird, P. N. 1993 The computer and the mind: An introduction to cognitive science, Second edition. Fontana. (Overview from the point of view of a psychologist.)

Two older but still very useful books

M.A.Boden, Artificial Intelligence and Natural Man, Second edition 1986. MIT Press

D.W. Hofstadter, Godel, Escher, Bach: An Eternal Golden Braid, Harvester Press, and Penguin books, 1979

My 1978 book on the relationship between philosophy and AI has been out of print for some time but is now available free of charge online:
A. Sloman, The Computer Revolution in Philosophy: Philosophy, Science and Models of Mind, Harvester Press (and Humanities Press), 1978, Hassocks, Sussex,
Online at http://www.cs.bham.ac.uk/research/cogaff/crp

I have a growing collection of slide presentations on AI and related topics, e.g. Philosophy of AI, how AI differs from Software Engineering, and Consciousness in Animals and Machines, in PDF and postscript. They are all available here: http://www.cs.bham.ac.uk/research/cogaff/talks/

ETHICS, AI AND ROBOTS

See this site www.roboethics.org

PRACTICAL TOOLS FOR LEARNING ABOUT AI BY DOING IT

For a taste of an easy to learn AI programming language (Pop-11), which we use at Birmingham, and is also used in the book by Sharples, see The poplog/popforum home page, maintained by Steve Leach (at Hewlett Packard research labs). Pop-11 includes most of the programming constructs used in a variety of other languages, so learning it provides a good introduction to a range of AI languages and more conventional programming languages. Poplog (including Pop-11) is now available free of charge for several types of machines. See The Free Poplog site

My introduction to the the core ideas of the language The Pop-11 "primer" is available online. It also introduces some of the main features of several other AI languages.

This page maintained by Aaron Sloman (A.Sloman@cs.bham.ac.uk)
Last updated: 11 Apr 2010
(Replaced ftp links with http.)
Updated: 15 Mar 2007
(Corrected document title, and added more references)