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Abstract for Seminar

During the second half of the 20th Century, many Artificial Intelligence researchers made wildly over-optimistic claims about how soon it would be possible to build machines with human-like intelligence. Some even predicted super-human intelligent machines, which might be a wonderful achievement or a disaster, depending on your viewpoint. But we are still nowhere near machines with the general intelligence of a child, or a chimpanzee, or even a squirrel, although many machines easily outperform humans in very narrowly defined tasks, such as playing certain board games, checking mathematical proofs, solving some mathematical problems, solving various design problems, and some factory assembly-line tasks.

This talk attempts to explain what we have to understand in order to make greater progress in modelling and replicating human minds. The main factor that limits progress is not technology but our inability to understand what the problems are: what collection of capabilities needs to be replicated in order to produce a human-like system. I.e. we need to understand human and animal minds far better than we do. This requires much deeper understanding of processes such as perception, learning, problem-solving, self-awareness, motivation and self-control. For instance many people working on machine vision, or on learning, assume that the tasks of vision or the varieties of learning are restricted to the kinds of mechanisms and architectures that they already know how to implement, at least in outline.

I'll try to indicate how we need to extend our understanding of possible architectures for information-processing virtual machines and will sketch some ideas for architectures that will be needed to combine a wide variety of human capabilities. This has many implications for the scientific study of humans, and also practical implications, not only in robotics but also for instance in the teaching of mathematics, and in therapy. It also has profound implications for philosophy of mind.

Some of the ideas are available in presentations here

and papers here

Computer Science @ Kent

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