Summary: Professors John J. Hopfield and Geoffrey E. Hinton have been awarded the 2024 Nobel Prize in Physics for their groundbreaking work on artificial neural networks that laid the foundation for modern machine learning.

Hopfield invented a network that retrieves stored images by adjusting its “energy” based on physical principles, while Hinton extended this model into the Boltzmann machine, allowing it to classify and generate complex patterns. Her work has driven advances in AI and developed tools that learn from data and recognize patterns.

These contributions have profoundly influenced both neuroscience and computer science.

Important facts

• John Hopfield developed a neural network inspired by atomic spin physics.
• Geoffrey Hinton advanced the field with the Boltzmann machine and improved pattern recognition.
• Their discoveries are central to today’s machine learning and artificial intelligence.

Source: SfN

Professor John J. Hopfield of Princeton University and Professor Geoffrey E. Hinton of the University of Toronto, Canada, have been awarded the 2024 Nobel Prize in Physics for their fundamental discoveries and inventions that enable machine learning with artificial neural networks. Hopfield received the SfN Swartz Prize for Theoretical and Computational Neuroscience in 2012.

John Hopfield invented a network that uses a method of storing and restoring patterns. We can think of the nodes as pixels.

The Hopfield Network uses physics that describes the properties of a material based on its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a way that corresponds to the energy in the spin system of physics and is trained by finding values ​​for the connections between nodes so that the stored images are low energy.

When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes updating their values ​​so that the network’s energy drops. So the network works incrementally to find the stored image that most closely resembles the incomplete image it was fed.

Geoffrey Hinton used the Hopfield network as the basis for a new network using a different method: The Boltzmann machine. This makes it possible to learn to recognize characteristic elements in a specific data type.

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 most likely to occur when the machine is running.

The Boltzmann machine can be used to classify images or create new examples of the type of pattern it was trained on. Hinton built on this work and helped usher in the current explosive development of machine learning.

The Society for Neuroscience honored Hopfield with the Swartz Prize in 2012 for his influence on neuroscience and for creating a new framework for understanding how neurons interact to create learning and memory.

The Swartz Prize in Theoretical and Computational Neuroscience is awarded to an individual whose activities have made a significant cumulative contribution to theoretical models or computational methods in neuroscience or who has made particularly notable recent advances in theoretical or computational neuroscience . The prize is sponsored by the Swartz Foundation.

Founded in 1739, the Royal Swedish Academy of Sciences is an independent organization whose overall goal is to promote science and strengthen its influence in society.

The Academy takes particular responsibility for the natural sciences and mathematics, but strives to promote exchange between different disciplines.

The amount of the Nobel Prize for 2024 is set at 11.0 million Swedish krona (SEK) (~1.1 million US dollars) and will be divided equally between the laureates.

About this AI and Nobel Prize news

Author: Press office
Source: SfN
Contact: Press office – SfN
Picture: The image is protected by copyright Fig. Niklas Elmehed © Nobel Prize Outreach