The Royal Swedish Academy of Sciences has awarded the 2024 Nobel Prize in Physics to John J. Hopfield, Princeton University, USA, and Geoffrey E. Hinton, University of Toronto, Canada. Both laureates are recognized for their pioneering work in machine learning, especially the use of artificial neural networks. Their research, based on the principles of physics, forms the basis of modern machine learning systems. Hopfield developed an associative memory system capable of storing and reconstructing data patterns, while Hinton introduced methods that allow networks to autonomously discover data properties and perform tasks such as image recognition.

Artificial neural networks and physics

Artificial neural networks are computer systems based on brain neurons. These neurons, represented as nodes, influence each other through synapse-like connections, adjusting their strength based on training. This year’s laureates have been instrumental in shaping the use of these networks in machine learning since the 1980s. Their contributions laid the foundation for today’s advanced AI technologies.

Contributed by John J. Hopfield

John J. Hopfield’s significant contribution was his invention of a network that can store and reconstruct patterns. By applying principles from physics, particularly atomic spin, his network is designed to function by minimizing energy, similar to systems in nature. The network updates its nodes to progressively detect the stored image when it is rendered incomplete or distorted.

Influence of Geoffrey E. Hinton

Geoffrey E. Hinton extended Hopfield’s work by developing the Boltzmann machine, a neural network that can identify features in data. Using statistical physics, Hinton’s invention allows the network to learn by analyzing common examples, allowing it to recognize and generate patterns. His research was instrumental in the rapid advancement of machine learning. The prize of 11 million Swedish kronor will be divided equally between the laureates

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