Why John Hopfield and Geoffrey Hinton won 2024 Nobel Prize in Physics

Scientists John Hopfield and Geoffrey Hinton won the 2024 Nobel Prize in Physics for discoveries and inventions that enable machine learning within artificial neural networks, the award-giving body said on Tuesday.

The award comes with a prize sum of 11 million Swedish crowns ($1.1 million), which is shared between the winners if there are several. The physics prize is awarded by the Royal Swedish Academy of Sciences.

Why did the duo win Nobel Prize?
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 Hopfeld created an associative memory that can store and reconstruct images and other types of patterns in data.


Read More: Physics Nobel Prize 2024 awarded to John J. Hopfield & Geoffrey E. Hinton

Geoffrey Hinton invented a method that can autonomously fnd properties in data, and so perform tasks such as identifying specifc elements in pictures.

When we talk about artifcial intelligence, we often mean machine learning using artifcial neural networks. This technology was originally inspired by the structure of the brain. In an artifcial neural network, the brain’s neurons are represented by nodes that have diferent values.

These nodes infuence each other through connections that can be likened to synapses and which can be made stronger or weaker.

The network is trained, for example by developing stronger connections between nodes with simultaneously high values

This year’s laureates have conducted important work with artifcial neural networks from the 1980s onward.

John Hopfeld invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels.

Read More: Nobel Prize in Physics: Who are John J Hopfield and Geoffrey E Hinton?

The Hopfeld network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by fnding values for the connections between the nodes so that the saved images have low energy.

When the Hopfeld network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to fnd the saved image that is most like the imperfect one it was fed with.

Geoffrey Hinton used the Hopfeld network as the foundation for a new network that uses a diferent method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components.

The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.

“The laureates’ work has already been of the greatest beneft. In physics we use artifcial neural networks in a vast range of areas, such as developing new materials with specifc properties,” said Ellen Moons, Chair of the Nobel Committee for Physics.