What is the piece about abstraction that category theory lacks?
Biggest question in AI, and no one has a clue. We need new mathematical abstractions of abstraction. No, not category theory.pic.twitter.com/3f22R6ue6f
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Was wondering this myself. Kind of hoping the response is more arrows.
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I think there's low-hanging fruit in cognitive semantics such as usage-based approaches to grammar, conceptual metaphor etc. And DL/RL work has progressed on basic situated agents to where it may be worthwile to revisit grounding abstractions in experience using those approaches.
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In what ways are purely mathematical models of AI architectures insufficient for efficiently explaining the relationship between intermediate fine scale effects of model parametrization and terminal states?
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“Contextual” category theory maybe? It seems we treat everything in this Franken-equilibrium states approach when the real problem is we don’t have a way to produce a satisfying taxonomy of /bf{dynamical} classes. They have to exists in certain limited contexts. I can feel em
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Something between category theory and information theory perhaps.
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Biggest questions for me are finding/defining types and functors in differentiable programming. They probably need to be fuzzier than what category theorists are used to.
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Perhaps renormalization group applied to understanding which approximate summary statistics will still have divergent terms when used to approximate stuff infinitely far away from the actual distributions they summarize, and how to systematically suppress those divergences.
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This seems too specific to cover all of abstraction.
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