Mathematician Proposes AI-Era Changes to Hiring and Evaluation
In a guest post, University of Chicago mathematician Frank Calegari called OpenAI’s October 6 release of more than 700 mathematics papers a watershed in mathematics. He said AI is capable of making groundbreaking contributions across many mathematical areas, but criticized the OpenAI papers’ writing, attribution practices and accuracy, saying some were wrong. Calegari proposed evaluating all work as if it were AI-assisted, emphasizing in-person hiring interviews, research vision and communication, demanding that journals adapt, broadening graduate education without dropping technical skills, improving seminars and taking greater ownership of AI-assisted results. IT之家 separately summarized his hiring and evaluation proposals.
Calegari’s post turns the debate over AI-assisted mathematics into specific proposals for hiring, journal standards, graduate education, seminars and research evaluation, while explicitly arguing that AI can make groundbreaking mathematical contributions.
[This is a guest post by Frank Calegari. This blog post was initially written in a different file format and converted using AI. — T.]
Why we should not be sad
The release by OpenAI on October 6 of over 700 research papers was a watershed in the history of mathematics. AI is now undeniably capable of making groundbreaking contributions across many areas of mathematics. So why are some mathematicians so unhappy?
Some of it is the apparent disregard by AI companies for the practice of mathematics. The OpenAI papers — despite some tremendous ideas — are atrociously written and violate our community expectations of attribution. Some of them are even wrong. We have much more control over how we hire mathematicians, publish papers, and train students than over the behavior of trillion-dollar companies, and that is where our efforts would be better spent.
Much of the reaction seems to reflect a feeling that the subject that they love is under attack, and is being “hollowed out”. The recent OpenAI dump made barely a dent on the general problems of functoriality and reciprocity in the Langlands program. But even if it had solved every problem in the Langlands program, to imagine that would have “solved number theory” would be as naive as saying that the construction of the periodic table solved chemistry. Mathematicians should do a better job explaining that when we talk about the central problems in any area, we are merely discussing the most natural boundaries of our understanding — and that unlocking them is not the end but the first step to finding new playgrounds to explore. Of course, this takes time. I looked for areas of mathematics which were “harder hit”. It is hard not to be struck by Hugo Duminil-Copin’s lament:
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