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Simons Institute Director Venkat Guruswami interviews Dan Spielman (Yale) and Nikhil Srivastava (Simons Institute), two of the organizers of First Proof, a project that aims to measure the capabilities of AI systems in the context of research mathematics.

A Sloan Research Fellowship is one of the most prestigious awards available to early-career researchers.

Let me tell you about two lists of questions — one for humans, and one for AI — and how they were made.

Frustrated by the AI industry’s claims of proving math results without offering transparency, a team of leading academics has proposed a better way.

Large language models struggle to solve research-level math questions. It takes a human to assess just how poorly they perform.

Have reports of AI replacing mathematicians been greatly exaggerated? Artificial intelligence has attained an impressive series of feats — solving problems from the International Math Olympiad, conducting encyclopedic surveys of academic literature, and even finding solutions to some longstanding research questions. Yet these systems largely remain unable to match top experts in the conceptual frontiers of research math.

When machine learning systems bridge from prediction to intervention — such as in statistical profiling of job seekers — seemingly minor modeling decisions can have profound consequences for who ultimately receives support. In her talk during the workshop on Bridging Prediction and Intervention Problems in Social Systems, Frauke Kreuter (LMU Munich and University of Maryland) examined how different choices in the data science pipeline affect not just predictive accuracy, but the actual composition of individuals flagged for intervention.

In many areas of machine learning, theory and practice have undergone a dramatic divergence; there is extremely little theory to guide our understanding of much of modern AI. Some of what’s likely called for is revolutionary new theory. In this Richard M. Karp Distinguished Lecture, however, Katrina Ligett (Hebrew University) explored a more conservative idea: that we sometimes approach the theory of learning in a way that leaves money on the table.

Greetings from Berkeley, where our program on Federated and Collaborative Learning is in full swing. In January, we hosted our winter Scientific Advisory Board meeting, preceded by a Theory Day with talks by some of our board members. We also had two groups of Circles participants here for a week of collaboration. And this was in addition to a workshop, a boot camp, a program reunion, and a Richard M. Karp Distinguished Lecture.

The successes of generative AI and large language models involve both powerful observable behavior and deep internal representations of the world that they construct for their own uses. How do these internal representations work, and to what extent are they similar to or different from the representations of the world that we build as humans? In this talk, Jon Kleinberg explores these questions through the lens of generative AI, drawing on examples from game-playing, geographic navigation, and other complex tasks.