Official winning chart of the 2024 Nobel Prize in Physics

The Royal Swedish Academy of Sciences announced on Oct. 8 that it has awarded the 2024 Nobel Prize in Physics to two artificial intelligence pioneers - U.S. scientist John Hopfield and Canadian scientist Geoffrey Hinton - for their foundational discoveries and inventions in machine learning using artificial neural networks, which have helped computers to learn in a way that is closer to the the way the human brain learns, laying the foundation for the development of AI.

The award recognizes the growing importance of AI in the way people live and work. Ellen Moons, Chairman of the Nobel Committee for Physics, said, “The work of the laureates has already brought enormous benefits. In physics, we use artificial neural networks in a wide range of areas, such as developing new materials with specific properties.”

In a post on Platform X, the Nobel Prize committee said that Dr. Hopfield's and Dr. Hinton's breakthroughs are “grounded in the physical sciences” and that “they show us a completely new way to use computers to help and guide us in tackling the many challenges facing society.”

Born just outside London, Hinton has lived and worked in the United States and Canada for most of his life since the late 1970s.

Hinton began working on neural networks as a graduate student at the University of Edinburgh in the early 1970s, at a time when few researchers thought the idea would work. It wasn't until 2012 that he finally made a breakthrough with his students.In 2013, Hinton joined Google, and by May 2023, he left the company. Since then, he has continued to publicly call for caution in the treatment of AI technology, and has become a representative of the “AI Alignment School”, which aims to steer AI systems to behave in accordance with their designers' interests and desired goals.

In 2019, Hinton, along with Université de Montréal computer science professor Joshua Bengio and Meta chief AI scientist Yann LeCun, won the Turing Award for their work on neural networks, which is often referred to as the “Nobel Prize in computing “.

Born in 1933 in Chicago, Illinois, Hopfield began his career in 1958 at Bell Labs, where he focused on the properties of solid matter. 1961 saw him move to the University of California, Berkeley, as an assistant professor, and in 1964 he joined the physics faculty at Princeton University. Sixteen years later, he went to the California Institute of Technology as a professor of chemistry and biology and returned to Princeton in 1997 to take a position in the Department of Molecular Biology.

In the 1980s, Hopfield's work focused on how brain processes guide machines to preserve and replicate patterns, and in 1982 he developed a model of neural networks to describe how the brain associates, recalls, and remembers, known as Hopfield's network, which allowed machines to “store” memories using artificial neural networks The Hopfield network, which forms the basis of all current neural networks, was developed by the Royal Swedish Academy of Sciences (RAS).

According to an announcement from the Royal Swedish Academy of Sciences, Sinton used the Hopfield network as the basis for a new network that takes a different approach: the Boltzmann machine, which trains a machine by feeding it examples of what is likely to happen when the machine is running. The Boltzmann machine can be used to classify images, or to create new examples of the type of pattern being trained. Hinton continued to build on this foundation, helping to launch the explosion of machine learning. His pioneering research in neural networks paved the way for AI systems like ChatGPT.

What's even more noteworthy is that OpenAI co-founder and former chief scientist Ilya Sutskever was also a student of Hinton's. Together with another computer scientist, Alex Krizhevsky, the two of them also invented the convolutional neural network AlexNet.