Methodological and conceptual progresses obtained in the last decades in statistical physics and in probability theory, in particular on the analysis of high-dimensional random landscapes, on high-dimensional out of equilibrium dynamics, and on disordered systems, provide theoretical frameworks to tackle difficult challenges in AI. At the same time, AI offers new ways of studying physical systems. This is just the right time to build up on these concurrent opportunities, and develop a strong synergy combining physics and machine learning.



