Résumé |
Generative AI represents a groundbreaking development within the broader Machine Learning Revolution, significantly
influencing technology, science, and society. In this talk, I will focus on the state-of-the-art diffusion models, which are
currently used to generate images, videos, and sounds. They are very fascinating algorithms for physicists, as they are very
much connected to concepts from stochastic thermodynamics, particularly time-reversed Langevin dynamics. Diffusion
models initiate from a simple white noise input and evolve it through a Langevin process to generate complex outputs such
as images, videos, and sounds. I will show that statistical physics provides principles and methods to characterise this
generation process. Specifically, I will discuss how phenomena such as the transition from memorization to generalization
and the emergence of features can be understood through the lens of symmetry breaking, phase transitions, and disordered
systems. |