Humans are uniquely endowed in biology with a singular cognitive ability to pass on millennia of accumulated knowledge to our descendants, not solely through our genetic endowment, but also through the manufacture of abstract representations: art, culture, language and writing, music, science, and technology. This ability to transfer non-genetic information has been greatly amplified with the invention of decentralized information repositories, such as the traditional web or the emerging cryptographically more secure blockchain economy. In this talk, I will describe a long-term research program on analyzing four central capabilities that underlie our ability to manufacture representations:
Objectification: our ability to denote arbitrary entities as ``objects”, from elementary particles to our society, the U.S. economy, or an entire galaxy or a (meta)universe.
Social Interaction: our ability to confer agenthood on both inanimate and animate objects, treating objects not as passive containers of data, but actors defined by social interactions.
Metaphors: our ability to use analogies to map unfamiliar situations – understanding an alien civilization with an unknown language -- to ones we are familiar with from our experience.
Diagrammatic representations: our ability to draw figures, record a tune as a formal musical score, or write equations reveal how diagrams are a universal cognitive calculus for us.
We use the abstract mathematics of category theory to characterize universal properties that define these four core capabilities. Objects in a category are modeled as actors, whose properties emerge from their social interaction with other objects, modeled as morphisms. Functors and natural transformations play the role of metaphors in human language. Adjunctions across categories plays the role of analogies. Diagrams are special functors that map from indexing categories to domain categories and define universal constructions for reasoning about interactions. We illustrate our framework by manufacturing novel representations for modeling causality and conditional independence, creativity and imagination, learning, language, and decision-making.
Sridhar Mahadevan has been thinking about AI every waking day for the past 40 years, since he wrote his first machine learning program in 1982 at the Indian Institute of Technology, Kanpur. He was elected Fellow of AAAI for significant contributions in several areas of machine learning. He has published widely across many subfields of AI, including a book on robot learning in 1993 (Springer), and a book on representation discovery in 2007 (Morgan-Claypool). He is currently working on his third book on Universal AI. He is a Director at Adobe Research, and a Research Professor at the University of Massachusetts, Amherst.