First Proof has released the results of its second batch benchmark, assessing the ability of AI systems to autonomously solve naturally occurring...
Greetings from Berkeley, where we’ve welcomed a band of quantum computing theorists for a summer cluster focused on NISQ (noisy intermediate-scale...
Hats off to the many members of the worldwide Simons Institute community who have been making a splash in the last weeks.
First Proof has released the results of its second batch benchmark, assessing the ability of AI systems to autonomously solve naturally occurring mathematical research problems. Their solutions were evaluated by thirty expert mathematicians in a gathering last week at Harvard’s Center for Mathematical Sciences and Applications.
Given points in n-dimensional space drawn independently from an arbitrary (unknown) distribution and labeled red or blue so that the colors can be separated by an (unknown) intersection of k half-spaces, can you efficiently compute (learn) a rule to separate the colors? Santosh Vempala (Georgia Tech) describes a simple algorithm that improves on known results.
Not all convex functions have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this talk from our recent workshop on The Role of TCS in Modern Machine Learning, Robert Schapire (Microsoft Research) presented joint work with Miro Dudík and Matus Telgarsky aiming to develop a theory for understanding such minimizers at infinity.
In his keynote address at the workshop on Governance at the Technological Frontier: Translating Research into Policy for AI Oversight, California State Senator Jerry McNerney spoke about prospects and challenges for enacting meaningful AI regulation via the legislative process. Senator McNerney holds a PhD in Mathematics.
Hats off to the many members of the worldwide Simons Institute community who have been making a splash in the last weeks.
Greetings from Berkeley, where we’ve welcomed a band of quantum computing theorists for a summer cluster focused on NISQ (noisy intermediate-scale quantum) computers and complexity-based evidence of quantum advantage.
The Simons Institute’s 2022 short documentary, Until the Sun Engulfs the Earth: Lower Bounds in Computational Complexity, is being featured this month in Labocine’s April 2026 issue, mathēmatiká. The documentary asks how we know that a problem is impossible to solve.
Today, data sharing is the cornerstone of many modern applications. A common concern in such data-sharing pipelines is privacy: organizations are responsible for protecting the privacy of their data, whether it represents user data or enterprise trade secrets. In her talk from the recent workshop on Trust in Decentralized Systems, Giulia Fanti (Carnegie Mellon) discussed emerging challenges related to learning from private, federated data.
Brendan McMahan (Google) presents a framework of principles that helps bring precision to discussions of privacy and AI, and examines the theory and practice required to apply them in real scenarios.
Greetings from Berkeley, where last week we had a doubleheader of workshops associated with our quantum and machine learning pods.