Key Points
- John Hopfield and Geoffrey Hinton won the 2024 Nobel Prize in Physics for their pioneering work in AI.
- Hopfield introduced the “Hopfield network,” which mimicked human memory processes. Hinton expanded on this by using statistical physics.
- Their work forms the foundation of modern AI applications, including large language models and algorithms like AlphaFold.
- Hinton believes AI will have a transformative effect, exceeding human intellectual capabilities.
Two researchers whose work laid the foundation for today’s artificial intelligence (AI) revolution have been awarded the 2024 Nobel Prize in Physics. John Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto will share the 11 million Swedish kronor (approximately US$1 million) prize, announced by the Royal Swedish Academy of Sciences on October 8. The researchers were recognized for developing machine-learning techniques that form the backbone of modern AI.
Both scientists drew on physics to develop methods that power artificial neural networks—layered structures inspired by the human brain that enable machines to learn and recognize patterns. These discoveries have not only transformed AI but have also been applied across various scientific fields, from particle physics to astrophysics.
In 1982, John Hopfield, a theoretical biologist with a physics background, introduced a new network model. His system described the connections between network nodes as physical forces. By storing patterns in a low-energy state, the network could recall a stored pattern when prompted with similar input, a process likened to associative memory in the brain. This model became known as the “Hopfield network” and was one of the early methods for mimicking how humans process and remember information.
A computer scientist, Geoffrey Hinton, took Hopfield’s ideas further by applying statistical physics. He introduced probabilities into the network, enabling it to handle complex tasks like recognizing and classifying images or generating new examples based on its training. Hinton’s contributions helped evolve neural networks into powerful tools capable of learning from vast, unstructured datasets—an ability that conventional software lacked.
Hinton’s layered neural networks, which could mimic human learning processes, formed the basis of much of today’s advanced AI, including large language models (LLMs) and machine-learning algorithms like AlphaFold, which predicts protein structures.
Reacting to the announcement, Hinton expressed surprise at the recognition. He remarked that AI’s potential impact on society would be enormous, comparable to the Industrial Revolution. However, AI would exceed human intellectual abilities instead of surpassing humans in physical strength.
