This story will be updated with a longer discussion of the Nobel Prize work on Thursday, October 10.

Certain processes in the brain, such as recognition and classification, can be modeled as interactions of artificial neurons or “nodes” in a highly interconnected network. This physics-inspired approach to human learning was recognized with the 2024 Nobel Prize in Physics. John Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto share this year’s prize for their work on artificial neural networks, which have become the basis of many artificial intelligence technologies such as facial recognition and chatbots.

An artificial neural network is a collection of nodes, each of which has a value that depends on the values ​​of the nodes to which it is connected. In the early 1980s, Hopfield showed that a kind of memory capable of recognizing images could be imprinted on these networks through an energy minimization process. Building on this work, Hinton showed how the couplings between nodes can be tuned (or “trained”) to perform specific tasks such as data sorting and classification. Together, the contributions of these physicists are paving the way for the machine learning revolution sweeping the world today.

—Michael Schirber

Michael Schirber is corresponding editor for Physics magazine based in Lyon, France.

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