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.
Some types of virtualization, such as virtual memory, are implemented by providing a layer of indirection between what the program sees and what the system implements. This layer of indirection is typically ignored in theoretical analysis but has a real (and, in some cases, increasing) impact on system performance. In this Richard M. Karp Distinguished Lecture in the Algorithmic Foundations for Emerging Computing Technologies program, Martín Farach-Colton covers a variety of cases where the cost of indirection becomes significant, including new architectures, such as for hardware accelerators and shared memory.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Nevertheless, the path toward a full-stack scalable technology is a work in progress. In this talk from our fourth annual Quantum Industry Day, newly minted Nobel laureate John Martinis shows how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits and employing system engineering approaches.
The Simons Institute has received $250K in support from the Google DeepMind x Google.org AI for Math Initiative, which was launched in late October. The Simons Institute will be a member of the newly created consortium, along with Imperial College London, the Institute for Advanced Study, Institut des Hautes Études Scientifiques (IHES), and the Tata Institute of Fundamental Research (TIFR).
Season’s greetings from Berkeley, where we have donned our light jackets and the campus squirrels are newly plump for winter. We just concluded a fantastic semester with two highly energetic programs on Complexity and Linear Algebra, and on Algorithmic Foundations for Emerging Computing Technologies. It was energizing to witness Calvin Lab bustling with collaborations filling up its open spaces.
As part of the Algorithmic Foundations for Emerging Computing Technologies Boot Camp, David Patterson (UC Berkeley) reviews the drivers of computer architecture (Moore’s law, Dennard scaling, domain-specific architectures, the roofline performance model) and upcoming critical challenges (deceleration of memory bandwidth and capacity, power, carbon footprint) and opportunities (chiplets, high-bandwidth memory, high-bandwidth flash).
Recall November 6, 2024 — the day after the U.S. election. I was driving back to my home in Washington, DC, from Ohio with colleagues. I was heartbroken not because of the rebuke to my political party, but because of the accompanying rebuke to scientists and expertise in government. Just hours earlier, I had imagined a very different future. As an AI researcher and policymaker, I had dreamed about landing my AI policy priorities in legislation.
Warm greetings from Berkeley, where our Fall 2025 research programs on Complexity and Linear Algebra, and on Algorithmic Foundations for Emerging Computing Technologies, are in full swing. There is a seminar talk or reading group meeting pretty much every day, and the two programs are also discovering interesting synergies and exploring holding a joint seminar series.
In his presentation in the Complexity and Linear Algebra Boot Camp, Senior Scientist Nikhil Srivastava defines the problem of approximately diagonalizing a given dense matrix, and explains two phenomena that impede the convergence of diagonalization algorithms and complicate their analysis.
In this episode of Polylogues, Science Communicator in Residence Lakshmi Chandrasekaran sits down with two of the senior participants in our Summer 2025 Cryptography program, Yael Tauman Kalai (MIT) and Daniele Micciancio (UC San Diego).