Gaps include the inability of current AI systems to make discoveries of the kinds made by ancient mathematicians, such as Euclid, Archimedes and others, including discoveries in geometry and topology, long before the development of modern logic, algebra, formal logic, and proof theory.

The information-processing abilities required seem to be closely related to the abilities of pre-verbal humans to make proto-mathematical discoveries, and many forms of intelligence in other species, including nest-building birds, squirrels, elephants, orangutans and many others.

Current AI vision systems cannot support the uses of vision in discovery of deep mathematical features of geometry and topology, including discovery of impossibilities and necessary connections (related to but different from perception of positive and negative action-affordances). (An example involving rubber bands.)

And they lack the meta-cognitive, reflective abilities required to organise, communicate and defend such discoveries if challenged -- precursors to mathematical proof(?).

Current AI language learning mechanisms cannot match the language creating mechanisms used by young humans, demonstrated dramatically by deaf children in Nicaragua.

Current AI models of emotion and motivation lack the depth and variety of biological mechanisms involved in affective states and processes, including a passionate interest in mathematics, long term grief, and many other short and long term states and processes relating to things cared about, including personal relationships, moral concerns, and deep, enduring social attitudes. The CogAff project attempts to address some of these issues. http://www.cs.bham.ac.uk/research/projects/cogaff/

This is very different from, and much more difficult than, producing machines with shallow mimicry of human responses.

The fashionable emphasis on "embodied cognition", "enactivism", and "situated cognition", focuses on real but fairly shallow products of evolution. This ignores the pressure to evolve increasingly disembodied forms of cognition to meet increasingly complex and varied challenges produced by new more complex physical forms and capabilities, geographical and temporal spread of important information and other resources.

That emphasis also ignores requirements to apply meta-cognitive processes to oneself and to others, and abilities to invent, implement, test, debug, and modify novel and increasingly complex engineering solutions to practical problems. Designers of ancient pyramids could not plan a new creation by physically interacting with the materials tools, labourers and temporary structures used during execution of the plans. Physical models can help as thinking tools but really creative designers can envision structures and processes that have never previously existed.

It is likely that the vast majority of important evolutionary transitions in information processing, and the resulting products in animal brains, have not yet been discovered, and that some of them cannot be detected by current scanning mechanisms (which don't reveal subneural processes, e.g. the chemistry of a synapse).

Human engineers have increasingly learnt about the importance of many varieties of virtual machinery during the last 70 years or so. It is likely that biological evolution discovered more complex and more powerful varieties of virtual machinery long before we did: including forms of virtual machinery that give rise to problems about the nature of consciousness, and many forms of self-awareness, and increasingly complex information-processing architectures, some of which the BICA community have identified and attempted to emulate.

The tutorial will present the conjectured roles of multiple construction kits produced by evolution, often straddling different species. Without some of the later construction kits current species could not exist. Some of the construction-kits are required mainly for physical (e.g. physiological) structures. Others are required for construction of new forms of information processing. There may be important examples that human scientists have not yet (re-)discovered.