The floodgates are open. Right on the heels of OpenAI\u2019s new large language model producing the biggest math breakthrough in two decades, the company just released no less than 372 results from the same model. Each resolves or makes substantial progress on a major open question in mathematics or theoretical computer science, the company says<\/p>\n
OpenAI revealed the results in a GitHub repository<\/a> at 6 P.M. EDT. The deluge will take mathematicians months to parse through and understand\u2014including to determine whether the proofs contain novel and important ideas or are mostly mash-ups of existing techniques. But many of the results have already been verified in Lean\u2014a program<\/a>ming language that validates a proof\u2019s logic\u2014and are therefore all but certain to be correct.<\/p>\n Among the results claimed are a solution to the four-dimensional Kakeya conjecture, improvements on some of the world\u2019s most important computer algorithms and actual progress toward math\u2019s scariest problem, the Riemann hypothesis<\/p>\n If you’re enjoying this article, consider supporting our award-winning journalism bysubscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today<\/p>\n A spokesperson for OpenAI told Scientific American<\/i> that the new model\u2014which the company has not released to the public\u2014produced almost every one of the results in response to a single prompt handed to a single AI agent. This would be a striking difference from OpenAI\u2019s earlier blockbuster solution to the Navier-Stokes problem, which was produced through the collective efforts of a 10,000-strong agentic swarm that cost millions of dollars in computing power. If true, it would mean that unprecedented mathematical power could soon be accessible to anyone. But OpenAI\u2019s reputation, among mathematicians, for bold claims and little transparency is causing considerable skepticism.<\/p>\n \u201cUntil and unless they release the model and people can replicate their results, I think you should treat any claims about one-shotting problems with a single agent as unverified,\u201d says Andrew Sutherland, a mathematician at the Massachusetts Institute of Technology. \u201cWe should ask for receipts.\u201d (The OpenAI spokesperson also told Scientific American<\/i> that some results might have taken multiple attempts.)<\/p>\n OpenAI\u2019s handling of the Navier-Stokes solution\u2019s release has proved so controversial that, on September 21, the company announced<\/a> it was assembling an independent advisory group of mathematicians to provide recommendations on responsibly publishing AI-generated results. The recommendations that the group developed<\/a> call for a company that releases such results to make public the model, exact prompt and compute time behind each one. In spite of the group\u2019s recommendations, though, OpenAI is choosing only to reveal the average<\/i> compute time for a problem, with some additional statistics\u2014and no prompts.<\/p>\n The OpenAI spokesperson told Scientific American that the team is taking the advisory group\u2019s guidelines seriously and doing its best to comply but added that the company is not bound by these recommendations<\/p>\n The recommendations are particularly critical<\/a> of AI companies using \u201cproprietary internal models\u201d that aren\u2019t broadly accessible to mathematicians. OpenAI told Scientific American that the company is working<\/a> to release the model as quickly and responsibly as possible<\/p>\n As the Navier-Stokes dispute unfolded, comments from OpenAI personnel led some mathematicians to believe the company\u2019s model had produced additional results but that it was hesitating to publicize them. This raises a vital question: Is it more harmful to unload heaps of unexplained proofs on the community or withhold them entirely?<\/p>\n \u201cIf we want to know the answers to these math questions, I see no reason why we should ask the company to keep them secret from us,\u201d says Daniel Litt, a mathematician at the University of Toronto. \u201cTo me, it\u2019s going to be a good thing for mathematics.\u201d<\/p>\n On the other hand, the famed mathematician Terence Tao has vocally criticized OpenAI and other \u201cfrontier\u201d AI labs for the \u201cinsane\u201d pace of their AI-generated results<\/p>\n Such results may even be coming too fast for the labs themselves\u2014the OpenAI spokesperson told Scientific American that many of the company\u2019s newly released results are not yet understood by its own mathematicians. Still, they don\u2019t plan on slowing down. The company wants to put as powerful a tool as possible in mathematicians\u2019 hands<\/p>\n Moreover, OpenAI maintains that it can\u2019t slow down because these math problems are an indispensable test to show that their AI really is getting smarter<\/p>\n But for the practitioners who see mathematical truth as an end rather than a means, the consequences of this race are becoming harder and harder to predict<\/p>\n \n En rapport:<\/strong> \n Rejoindre Justfine Infotech<\/strong> et d\u00e9velopper de v\u00e9ritables comp\u00e9tences num\u00e9riques en IA, automatisation, d\u00e9veloppement web, marketing digital, bureautique, e-commerce, travail ind\u00e9pendant et cybers\u00e9curit\u00e9.<\/p>\n Programmes disponibles :<\/strong> WhatsApp :<\/strong>On supporting science journalism<\/h2>\n
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