Prestigious international prize awarded every four years to mathematicians under 40 for outstanding work, often likened to a Nobel Prize for math.
Anthropic released an AI-generated proof of a percolation conjecture that Fields medallist Hugo Duminil-Copin had tried and failed to prove. What it shows about AI research depends on expert checking and on how much of the argument humans built first.
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Scientific American's account from the mathematicians' congress in Philadelphia has a Fields medalist quitting for OpenAI and a Millennium Prize problem falling to an opaque mix of models, with no verification trail reported for either.
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Days after Caltech mathematicians called AI "slop mathematics" research misconduct, OpenAI's research lead said the company would stop sponsoring their competition. Organizers say they are talking to other firms.
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Buckmaster was preparing to publish when OpenAI announced it had solved the problem and offered him a share of the credit. A second mathematician, Andreas Thom, then asked the company whether his own ChatGPT conversations had reached its model.
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OpenAI said on August 1 that its then-unreleased Astra model had advanced sphere packing, one of ten claimed results. In a subject with proved optima in only four dimensions, verification is slow.
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The declaration lands three days after OpenAI's Navier-Stokes claim, in the middle of a credit dispute where the company and the mathematician at the center give accounts that cannot both be right.
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Their joint statement argues that famous open problems were only ever a proxy for understanding, and that benchmarks miss the years of talks and simplifications that follow a proof.
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A statement signed by Terry Tao says the humans controlling the technology will largely decide whether AI helps or wrecks mathematics. A Marginal Revolution post says the statement never argues for that claim, and predicts math funding rises.
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The Fields Medalists behind a new open letter accept that AI can speed up mathematics. Their objection is to solutions announced before anyone has had time to write them up properly or work out whose ideas they used.
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Timothy Gowers calls OpenAI's results extraordinarily impressive and then sorts them. The sorting, not the praise, tells you which of your problems these models can touch.
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