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Headlines · 2

1. 5d
   Terence Tao's 'Math 2.0': AI pushes mathematics into proof abundance

   In an October 9 talk at Caltech's Beckman Auditorium, Fields medalist Terence Tao retitled his lecture from "Machine-Assisted Proofs" to "Math 2.0," arguing that frontier AI has moved mathematics from an era of proof scarcity into one of proof abundance. The report relays claims that OpenAI coordinated roughly 10,000 AI agents to produce a Lean-verified finite-time singularity proof for the Navier-Stokes equations in 88 hours and published 700-plus AI-generated math manuscripts covering 372 results on GitHub, that Anthropic said Claude raised the lower bound for nontrivial zeros of the Riemann zeta function from 41.6% to 67.2%, and that Google DeepMind's AlphaProof has reached IMO gold-medal level. Tao warned via a thought experiment about an AI-designed "perfect cancer drug" that correct but incomprehensible output is a dangerous misalignment, and proposed Math 2.0: human-AI collaboration that feeds back into human understanding, such as tackling "open explanation problems," merging papers into an evolving knowledge network and building large formalized math knowledge bases.

   36氪 人工智能 · 🔥 0
2. Oct 11
   MIT professor says OpenAI's math proofs are beyond human reading

   According to a 36Kr repost of a New Zhiyuan article, OpenAI published 722 math papers at once, said to include a counterexample showing failure of solutions to the Navier-Stokes equations and other claimed results on well-known open problems; the report also notes the papers still await verification. On Bloomberg's Odd Lots podcast, MIT associate dean of engineering education Justin Solomon said the proof is dense with technical detail, counterintuitive inequalities and extreme bounds, and that he stalled after two pages, able only to say it is "probably right." Fields Medalist Terence Tao, in a Caltech "Math 2.0" talk, offered a thought experiment about an AI-designed cancer therapy that passes formal verification and trials but whose mechanism no human understands, raising questions of interpretability and alignment.

   36氪 人工智能 · 🔥 0

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