Little did he know that hours later, OpenAI was going to post 722 mathematical manuscripts an unreleased AI model had produced. The firehose of new work lends new urgency to the question: How is AI changing mathematical discovery? In their race for headline-grabbing breakthroughs, AI companies typically do not disclose their methods and data.
That competition among AI companies threatens to overwhelm human collaboration and understanding. This tension had been playing out in miniature on a website Bloom runs that tracks progress on more than 1,000 problems originally posed and collected by Paul Erdős, one of the 20th century’s most prolific mathematicians. Here, people used to collaborate to discuss open problems, or ones for which the best solution either hasn’t been found or isn’t known to the greater math community.
But increasingly, visitors were competing to close open problems with AI proofs that they often didn’t understand. Bloom, a mathematician at the University of Manchester in England, doesn’t want to spend his days refereeing AI-generated work. So, soon after we spoke, and before he knew that OpenAI would be announcing its latest results, Bloom posted that he’d no longer tag problems as open or solved, and he also closed comments.
It’s definitely exciting that AI can tackle so many open problems so quickly. But can it help mathematicians understand the answers and build on them? Just two days after OpenAI’s deluge of results, the peer-reviewed edition of a Google DeepMind study became public.
First reported in a preprint earlier this year, it offers an inside look at how one AI system tackles open math problems. The system called AlphaProof Nexus solved 9 Erdős problems and made a number of other interesting mathematical advances, researchers report October 8 in Science. The report includes a detailed description of how the results were obtained.
Plus, human mathematicians vetted many of the proofs. That’s “quite different” from how OpenAI and Anthropic are handling their math results, Bloom says. To tackle a collection of 353 open Erdős problems, they used multiple agents powered by the Gemini language model.
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