Nobel Prize in Physics 2024: John Hopfield and Geoffrey Hinton Win for Pioneering Machine Learning

Nobel Prize in Physics 2024: Two of the researchers who developed the machine learning techniques that are behind the AI boom have won the 2024 Nobel Prize in Physics.

John Hopfield from Princeton University in New Jersey and Geoffrey Hinton at the University of Toronto in Canada share the 11 million Swedish kroner (US$1 million) prize announced in Stockholm on 8 October.

Both used physics to come up with the methods that power artificial neural networks which are brain inspired layered structures that learn abstract concepts.

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Their discoveries “form the building blocks of machine learning, that can aid humans in making faster and more reliable decisions”, said Nobel committee chair Ellen Moons, a physicist at Karlstad University, Sweden, during the announcement. “Artificial neural networks have been used to advance research across physics topics as diverse as particle physics, material science and astrophysics.”

In 1982 Hopfield a theoretical biologist with a physics background came up with a network that described connections between nodes as physical forces1. By storing patterns as a low energy state of the network the system could recreate the image when prompted with a similar pattern. It became known as associate memory because of its similarity to the brain trying to remember a rarely used word or concept.

Hinton a computer scientist later used principles from statistical physics which is used to describe systems with too many parts to track individually to further develop the ‘Hopfield network’. By building probabilities into a layered version of the network he created a tool that could recognise and classify images or generate new examples of the type it was trained on.

These were different from computation before as the networks could learn from examples including from unstructured data that would have been impossible for software based on step by step calculations.

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The networks are “grossly idealized models that are as different from real biological neural networks as apples are from planets” Hinton wrote in Nature in 2000. But they worked and have been built upon. Neural networks that mimic human learning are the basis of many state of the art AI tools from large language models (LLMs) to machine learning algorithms that can analyse large amounts of data including the protein-structure-prediction model AlphaFold.

Speaking by telephone at the announcement, Hinton said that learning he had won the Nobel was “a bolt from the blue”. “I’m flabbergasted, I had no idea this would happen,” he said. He added that advances in machine learning “will have a huge influence, it will be comparable with the industrial revolution. But instead of exceeding people in physical strength, it’s going to exceed people in intellectual ability”.

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