Human AI interaction
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A mathematician reads the AI future
opinion
11 min read
OpenAI claimed in early September that an internal AI system using about 10,000 agents over 88 hours had solved the Navier-Stokes Millennium Prize problem. On October 6, OpenAI reported that its latest model had solved nearly 400 landmark open mathematics problems, averaging three hours of computation per problem. These claims have unsettled mathematicians, particularly researchers in pure mathematics, where understanding and developing a proof can matter as much as obtaining a correct result. Cornell mathematicians held a department-wide town hall to consider how AI could change doctoral research, thesis writing, and hiring. Numerical linear algebra researcher Alex Townsend assembled about 200 open problems in his specialty and planned to release 600 long-standing problems across applied mathematics, hoping researchers could solve them—potentially with AI assistance—before companies do. He argues that mathematicians should focus more on choosing important questions, building theories, interpreting machine-generated proofs, and finding applications. Other mathematicians fear rapid automated problem solving could weaken training paths for students and diminish the human experience of discovery. Cornell logician Justin Moore said he would wait to open a bottle of cognac reserved for the solution of a 19-year-old problem until trusted peers verify and collectively understand the AI-generated proof. He said AI does not write mathematics particularly well.