Nobel Prize in Physics 2024 Lecture

Daphne Klemme
photos of the winners of the 202 Nobel prize in Physics

On Monday, April 21, 2025, the Yale Physics Department held the third annual Nobel Prize in Physics Lecture. This lecture, the result of an idea presented at a 2022 meeting of the Undergraduate Student Advisory Committee, is a way to connect with the undergraduate community by having a talk at their level about the science behind the winners of the Nobel Prize in Physics. This year’s lecture was presented by John Sous, assistant professor of Applied Physics, who was introduced by David Poland, Director of Undergraduate Studies.

The Nobel Prize in Physics 2024 was awarded jointly to John J. Hopfield, Princeton University, and Geoffrey Hinton, University of Toronto, Toronto, Canada, “for foundational discoveries and inventions that enable machine learning with artificial neural networks”.

From the Nobel Prize Press release:

They trained artificial neural networks using physics

This year’s two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.”

John Sous

Prof. John Sous

After a brief introduction by David Poland, John Sous started the talk by defining “the defining focus of Physics and of AI”, and argued that “Physics and AI could be viewed as intimately connected” as demonstrated by the 2024 Nobel Prize to Hopfield and Hinton.” He argued that “Physics can be seen as the science of understanding both natural and artificial systems, the latter including AI” as well as arguing that “Physics is a good subject domain to use and advance AI on”. John then explained Hopfield and Hinton’s academic trajectories and their specific contributions: “Hopfield network describes a collection of interacting spins which implement memory as energy minima”; also stating that “Hinton’s Restricted Boltzmann Machines are finite-temperature stochastic units which learn the data distribution so function as generative models”. John concluded with some discussions of his thoughts on the future of research on Physics and AI.

See below for links to the original press release from the Nobel Prize Committee and to the lecture recording as posted on the physics department YouTube channel.

Read the official press release from the Nobel Prize Committee here.

View the video of the event here.