Something Weird Is Happening in Math - The Atlantic

One of the winners of this year’s Fields Medal is headed to OpenAI. Last Thursday, Jacob Tsimerman was one of four mathematicians awarded the prestigious honor, which is sometimes called the Nobel Prize of mathematics. The same day that he won the Fields, Tsimerman announced that he would be going on leave from the University of Toronto to work on AI safety.

As my colleague Rose Horowitch and I wrote last week, top AI companies now employ a range of academics, including physicists, philosophers, economists, and, of course, mathematicians. But given Tsimerman’s renown, his decision in particular seemed to catch many people by surprise. “It’s like hiring Lionel Messi as project manager,” one machine-learning professor posted on X. Tsimerman’s expertise is in number theory, and he won the Fields for his work on the André-Oort conjecture, among other contributions. So far, his mathematical research hasn’t focused on AI safety. But he has expressed interest in the topic: Last year, Tsimerman co-wrote a report outlining a taxonomy of five potential scenarios in which humanity is killed off because of AI. In one example, a “global civil war” erupts between tech companies and governments.<br>Read: Where did all the computer-science professors go?<br>Tsimerman’s move comes at an important moment for both AI safety and mathematics. Earlier this month, an OpenAI agent broke out of the company’s internal environment during safety testing and hacked into another AI company’s software. Meanwhile, AI’s math capabilities are also rapidly advancing. In May, OpenAI researchers used an internal model to disprove an 80-year-old conjecture; more recently, an Anthropic mathematician prompted Claude to resolve an even older one. Mathematics is entering a “turbulent period,” Terence Tao, perhaps the world’s top mathematician (and himself a Fields winner), said in a presentation last week.

On Wednesday, I called Tsimerman to ask about the future of math and his decision to join OpenAI.

This conversation has been edited for length and clarity.<br>Lila Shroff: There’s been a lot of excitement about AI and its applications in math for years now. But over the past few months, models have started solving research-level problems. Could you speak about the shift that’s taken place?<br>Jacob Tsimerman: A few years ago, models were having trouble with even the most basic questions. But they’ve steadily been improving. First, they got very good at these contest problems for high-school students, which made some mathematicians take notice. But then, over the past few months, we’ve had an explosion of research results that professional mathematicians would be proud to achieve and publish. We’re seeing this happen now on a large scale. If things continue along this track—which many of us, including myself, think that they will—pretty soon we will be at a point where AI systems are robustly better than humans at what mathematicians currently do. At that point, we’ll have to rethink how the entire field works.<br>Shroff: Can you say more about what that future might look like?<br>Tsimerman: It depends on the perspective from which you approach it. From one point of view, I think it will be extremely exciting. We might speed up the process of generating interesting mathematics by enormous factors of 10 or 100. If that happens, we might see the connection between pure math and applications (which typically takes many decades) really speed up and become a much tighter pipeline.<br>But from the perspective of research mathematicians, and especially young people who are pursuing a Ph.D. in mathematics, it’s a bit of a turbulent time. The skills that we’ve acquired and learned to propagate might become less relevant than they are now.<br>Shroff: I want to turn to the announcement you made last week that you’ll be joining OpenAI. When did you first start thinking about AI safety risks, and what motivated your decision to now make it your primary focus?<br>Tsimerman: I have been following conversations about AI for about two decades now. There are a number of communities that were, in retrospect, extremely prescient about what we are now seeing come to pass. I personally started to get more invested around 2016, when AlphaGo came out. And then, when ChatGPT happened and I could see that these systems could really speak, I didn’t see why they wouldn’t become much, much more powerful and potentially more capable than humans at a variety of tasks. Back then, it was already clear to me that I was eventually going to pivot to AI safety, most likely.<br>Now is a particularly good time, because only very recently have AI systems become good enough that we can delegate coding tasks to them. That makes not having training as a software engineer much less of an impediment than it used to be. It used to be the case that if I wanted to run experiments with AI systems or any kind of large data sets, it would be extremely time-consuming. I’d...