Description
This 5-day program will explore the intersection of AI and fundamental theoretical physics. The event will feature two components, a symposium and a workshop, centered around two complementary themes: AI for theoretical physics and theoretical physics for AI.
The program will begin on April 7 and 8 with a large symposium with speakers and panel discussions focusing on the promise of AI to accelerate progress in theoretical physics. These talks will address the possibilities and challenges associated with AI ‘doing science.’ The event will bring together physicists, engineers, AI researchers, and entrepreneurs to collect different perspectives on what the future of theoretical physics will look like, the engineering challenges we should expect along the way, what tools and collaborations will be needed to help get us there, and what exciting steps are already underway.
Registration for the symposium is available on the symposium website.
The symposium will be followed by a workshop on April 9, 10, 11 focusing on developing a theoretical framework for AI enabling the development of reliable, robust, and interpretable AI models for physics. Recent advances in theoretical foundations of AI, inspired by techniques from string theory, quantum field theory (QFT), and statistical physics, have uncovered parallels between AI systems and physical theories, utilizing methods like renormalization group (RG) flows, Feynman path integrals etc. to deepen understanding of deep neural networks (DNNs), generative AI (e.g., LLMs and diffusion models), and scaling laws. Key topics include physics-informed optimization and learning, the role of RG and QFT for DNNs and generative AI, and the application of physics to AI interpretability. Through interdisciplinary dialogue, the event aims to foster collaborations, advance the theoretical foundations of AI, and explore its potential in areas like theoretical physics and mathematics. Speakers:
This 5-day program will explore the intersection of AI and fundamental theoretical physics. The event will feature two components, a symposium and a workshop, centered around two complementary themes: AI for theoretical physics and theoretical physics for AI.The program will begin on April 7 and 8 with a large symposium with speakers and panel discussions focusing on the promise of AI to accelerate progress in theoretical physics. These talks will address the possibilities and challenges associated with AI ‘doing science.’ The event will bring together physicists, engineers, AI researchers, and entrepreneurs to collect different perspectives on what the future of theoretical physics will look like, the engineering challenges we should expect along the way, what tools and collaborations will be needed to help get us there, and what exciting steps are already underway.
Registration for the symposium is available on the symposium website.
The symposium will be followed by a workshop on April 9, 10, 11 focusing on developing a theoretical framework for AI enabling the development of reliable, robust, and interpretable AI models for physics. Recent advances in theoretical foundations of AI, inspired by techniques from string theory, quantum field theory (QFT), and statistical physics, have uncovered parallels between AI systems and physical theories, utilizing methods like renormalization group (RG) flows, Feynman path integrals etc. to deepen understanding of deep neural networks (DNNs), generative AI (e.g., LLMs and diffusion models), and scaling laws. Key topics include physics-informed optimization and learning, the role of RG and QFT for DNNs and generative AI, and the application of physics to AI interpretability. Through interdisciplinary dialogue, the event aims to foster collaborations, advance the theoretical foundations of AI, and explore its potential in areas like theoretical physics and mathematics. Speakers:
- David Berman (Queen Mary University of London)
- Blake Bordelon (Harvard University)
- Jordan Cotler (Harvard University)
- Hugo Cui (Harvard University)
- Alessandro Favero (EPFL)
- Ro Jefferson (Utrecht University)
- Yonatan Kahn (University of Toronto)
- Dmitry Krotov (IBM)
- Bruno Loureiro (École Normale Supérieure in Paris)
- Luisa Lucie-Smith (The University of Hamburg)
- Cengiz Pehlevan (Harvard University)
- Rob Spekkens (Perimeter Institute)
Scientific Organizers:
- Anindita Maiti (Perimeter Institute)
- Matt Johnson (Perimeter Institute)
- Sabrina Pasterski (Perimeter Institute)
Advisory Committee:
- Achim Kempf (University of Waterloo)
- Cengiz Pehlevan (Harvard University)
- Hiranya Peiris (University of Cambridge)
- Roger Melko (University of Waterloo)