nemesis :blackhole: (npub10zr…yg26) The question on my mind is how do we know that "all of math" won't turn out to be a narrow problem in the same sense? Naively, it seems like the main question is whether the higher-level abstractions which guide mathematical thought can be implicitly encoded in some general-purpose neural architecture or whether the ML system needs to be able to encode them explicitly. Maybe every neural architecture has a finite capacity to store higher-level abstractions.
(To be explicit, the higher-level abstractions in geometry would be concepts like orthocenter or nine-point circle, etc. which allow you to reason about the problem in a compressed state. From their paper, it sounds like some of these abstractions were hard-coded (like "construct an excircle"), but I can believe that the LLM was also learning its own abstractions in some unintelligible form.)
☃️merry chrimist☃️ /
npub1pt6…6mf6
2024-01-19 19:16:36
in reply to nevent1q…t3kh
Author Public Key
npub1pt6l3a97fvywrxdlr7j0q8j2klwntng35c40cuhj2xmsxmz696uqfr6mf6Published at
2024-01-19 19:16:36Event JSON
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