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Bayle Shanks: Artificial Intelligence

Background

In 2003, I received a B.S. in Symbolic System with Honors from Stanford University, with a concentration in A.I.

Areas of interest in A.I.

  • data structures and representations

  • commonsense semantic knowledge representation and reasoning

    • logical and other symbolic approaches

      • differences between classical logic and how humans represent knowledge and reason
        Formally sound and complete propositional logical theorem proving is (co-)NP-hard.
        But people reason. So:

        • What kinds of mistakes do people make? (soundness)

        • What kinds of (formally incorrect) shortcuts do people use? (soundness)

        • What do they find difficult? (completeness)

      • paraconsistent logic

      • defeasible reasoning

        • defeasible inheritance networks

        • non-monotonic logic and other defeasible alternative logics

      • alternative logics that seem to accord better with 'commonsense intuition'

        • paradoxes of material implication

        • relevance logic

        • intensional logic

      • reasoning under uncertainty

        • bayesian networks

    • connectionist and other non-symbolic and bottom-up approaches

    • automated ontology/epistemology

      • which semantic knowledge representations are tractably 'learnable'?

      • unsupervised machine learning

        • hierarchical concept learning

          • clustering

          • dimensionality reduction

        • automated theorem discovery/theory formation

  • autoassociative semantic memory

  • cognitive architectures (i.e. putting it all together)

  • automated programming/learnable representations of computer programs

    • classes of Turing-universal architectures/classes of programming languages

    • classes types of sub-Turing systems

  • hierarchial reinforcement learning

  • attention

  • creativity (i.e. the ability to generate complex data structures, concepts, and hypotheses; i define this in contrast to the ability to reason about relationships between concepts that have already been generated or given)

  • symbolic and semantic reasoning on top of low-level connectionist architectures

In general, I’m interested in working towards flexible, general purpose, human-level A.I.

I agree mostly with Push Singh’s research (anti-)programme: http://wayback.archive.org/web/20020601133916/http://web.media.mit.edu/~push/why-ai-failed.html

My study of cognitive psychology and neuroscience is related; I hope that by finding general principals of the computational architecture of the brain, we can determine the types of cognitive architectures most likely to be fruitful. For example, the massively parallel nature of the brain and the 100-step rule suggest that cognitive architectures should at least incorporate a component with massively parallelism and short serial execution paths.

In the longer term, if we can’t figure out how to do A.I. on our own, we can reverse-engineer the human brain and see how it thinks. I don’t expect that this will be possible within my lifetime, however.

Activities

Major contributor to AIWiki (defunct).

Random notes

  • /notes-cog-ai (beware: these notes were written for personal use; they are not necessarily readable)

Potential future projects

  • How do humans hold inconsistent beliefs? How do humans do what A.I. calls "commonsense reasoning"? This is related to paraconsistent logics, confabulation after brain injury, and to conspiracy theories.