Some of the world’s most ambitious and consequential experiments are taking place not in particle accelerators or space telescopes but in silicon, training ginormous neural networks. The results have been transformative, yet much of the progress has been driven by empirical advances, with theoretical understanding struggling to keep pace. This meeting will explore how the tools and insights of theoretical physics can deepen our understanding of modern artificial intelligence. We will bring together physicists and computer scientists, with a shared goal of illuminating the principles underlying successful machine learning methods—and ultimately guiding the development of better architectures and algorithms.
For the 2019 version of this conference, see here
For the 2023 version of this conference, see here
For the Aspen Center for Physics website, see here
For questions, please contact us.
Adam Brown
Ethan Dyer
Michael Douglas
Paul Ginsparg
Dmitry Krotov
Cengiz Pehlevan
Eva Silverstein
Jascha Sohl-Dickstein
Sara Solla