I had the pleasure of chatting with @TheTuringPost about all things Axiom and AI4Math! Tune in: - How I define AGI vs domain specific ASI, the plate metaphor, can a chatty or poetic model prove the Riemann Hypothesis, and math’s incredible transfer learning power to coding and engineering - Why an AI mathematician needs both proofs and constructions capability, mapping to the two branches of Axiom: formal proving, and specialized discoveries - Data scarcity compared to code, the chicken-and-egg problem of autoformalization, Axiom’s bold synthetic data bets, and how we think about new knowledge generation - Problem-Solving vs Theory-Building, why "Theory Building" is harder to benchmark than the International Math Olympiad (IMO), and why literature search / retrieval, so emphasized by LLMs today, is unsatisfying https://t.co/HxRoCSOa2S
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