2024 Nobel Prize Lectures in Physics

John Hopfield and Geoffrey Hinton explain how ideas from physics helped establish computational models that store patterns and learn from data. Hopfield develops a physical perspective on collective computation, including associative memories in which network dynamics recover stored patterns from incomplete or distorted inputs. Hinton examines Boltzmann machines, connecting probabilistic neural networks and learning to concepts from statistical physics. Together, the lectures link energy landscapes, interacting units and stochastic behaviour with mechanisms for representation and computation. They provide the scientific background to the neural-network contributions recognised by the 2024 physics prize and the conceptual route from models of collective systems to machine learning.