The Royal Swedish Academy of Sciences today awarded the 2024 Nobel Prize in Physics(opens in new window) to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto in recognition of their foundational work in machine learning with artificial neural networks.
Inspired by the human brain, artificial neural networks are computing systems used to process data and learn from it.
Hinton served on the Computer Science Department faculty at Carnegie Mellon University from 1982-87. He received Carnegie Mellon’s 2021 Dickson Prize in Science(opens in new window).
“The Nobel Prize is one of the most significant and cherished public recognitions of researchers today,” said CMU President Farnam Jahanian(opens in new window). “Our extended Carnegie Mellon University community is extraordinarily proud to see Geoffrey Hinton’s talents and pioneering research celebrated in such a meaningful way and grateful for his many scholarly contributions to computer science, AI and society.”
At CMU, he co-authored an influential paper on the backpropagation algorithm, which allows neural networks to discover their own internal representations of data. He demonstrated that the algorithm enabled neural networks to solve problems previously thought to be beyond their reach.
The prize announcement also cites work Hinton did on Boltzmann machines with Terrence Sejnowksi, then at Johns Hopkins University. Later, at the University of Toronto, Hinton and his students made improvements to convolutional neural networks that cut error rates for object recognition in half, reshaping the field of computer vision.
“The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties,” said Ellen Moons, chair of the Nobel Committee for Physics.
At the University of Toronto, Hinton advised Ruslan Salakhutdinov(opens in new window) as he pursued his Ph.D. from 2005-09. Now the UPMC Professor of Computer Science in CMU’s Machine Learning Department (MLD), Salakhutdinov cited Hinton as his key influence.
“I wouldn’t be where I am today without Geoff and his guidance,” Salakhutdinov said. “Geoff basically discovered this unique algorithm that could train these deep networks efficiently. This laid the groundwork for a lot of the deep learning models and architecture, and it inspired a lot of people to start looking into it – it was all driven by Geoff.”
Salakhutdinov continues to work on generative models at Carnegie Mellon, including large language models, AI agents, deep learning and decision making. He said that Hinton thought fondly of CMU.
“When he was at CMU, it was amazing. He would go to CMU and see all of these students and researchers in the labs working hard and believe that they were creating the future, and that unique environment was something that he loved,” Salakhutdinov said.
As the director of MLD at CMU, Zico Kolter is guiding the School of Computer Science through the research revolution brought on by generative artificial intelligence tools. His own research revolves around machine learning, optimization and control, with much of the work centered on making deep learning algorithms safer, more robust and more modular. Kolter previously demonstrated how it’s possible to circumvent the safeguards of large language models.
“The field Geoff helped spawn — deep learning — has become one of the biggest things in our society,” Kolter said. “Almost all modern AI systems are based on deep learning. Geoff was foundational to deep learning.”
Born 1947 in London, Hinton received his Ph.D. in 1978 from The University of Edinburgh.
As an ACM Turing Award winner (along with Yoshua Bengio and Yann LeCun for their revolutionary work on deep neural networks), Hinton is the second person ever to earn both a Turing Award and a Nobel Prize — the first being another Carnegie Mellon professor, Herb Simon(opens in new window).
The Nobel Prize comes with an award of 11 million Swedish Kronor, or $1 million, split between the winners.