2024 Nobel prize for physics goes to pair who invented key AI methods

The 2024 Nobel prize in physics has been awarded to John Hopfield and Geoffrey Hinton for his or her work on synthetic neural networks and the basic algorithms that permit machines study, that are key to at the moment’s giant language fashions like ChatGPT.

“I’m flabbergasted, I had no idea this would happen,” Hinton informed the Nobel committee upon listening to the prize announcement. “I’m very surprised.” Hinton, who has been vocal about his fears across the improvement of synthetic intelligence, additionally reiterated that he regretted the work he had executed. “In the same circumstances, I would do the same again, but I am worried that the overall consequences of this might be systems more intelligent than us that eventually take control,” he mentioned.

Whereas AI may not look like an apparent contender for the physics Nobel, the invention of neural networks that may study and their purposes are two areas which can be intimately related to physics, mentioned Ellen Moons, chair of the Nobel Committee for Physics, through the announcement. “These artificial neural networks have been used to advance research across physics topics as diverse as particle physics, material science and astrophysics.”

Many early approaches to synthetic intelligence concerned giving pc applications logical guidelines to comply with to assist clear up issues, however this made it troublesome for them to study new data or encounter conditions they hadn’t seen earlier than. In 1982, Hopfield, at Princeton College, created an structure for a pc referred to as a Hopfield community, which is a set of nodes, or synthetic neurons, that may change the power of their connections with a studying algorithm that Hopfield invented.

That algorithm was impressed by work from physics that finds the power of a magnetic system by describing it as collections of tiny magnets. The method entails iteratively altering the power of the connections between the magnets in an try to discover a minimal worth for the power of the system.

In the identical 12 months, Hinton, on the College of Toronto, started growing Hopfield’s concept to assist create a intently associated machine studying construction referred to as a Boltzmann machine. “I remember going to a meeting in Rochester where John Hopfield talked and I first learned about neural networks. After that, Terry [Sejnowski] and I worked feverishly to work out how to generalise neural networks,” he mentioned.

Hinton and his colleagues confirmed that, in contrast to earlier machine studying architectures, Boltzmann machines may study and extract patterns from giant information units. This precept, when mixed with giant quantities of knowledge and computing energy, has led to the success of many synthetic intelligence techniques at the moment, comparable to picture recognition and language translation instruments.

Nevertheless, whereas the Boltzmann machine proved succesful, it was additionally inefficient and gradual, and it isn’t utilized in trendy techniques at the moment. As a substitute, sooner, trendy machine studying architectures like transformer fashions, which energy giant language fashions like ChatGPT, are used.

On the Nobel award convention, Hinton was bullish on the influence that his and Hopfield’s discoveries would have. “It will be comparable with the industrial revolution, but instead of exceeding people in physical strength, it’s going to exceed people in intellectual ability,” he mentioned. “We have no experience of what it’s like to have things smarter than us. It’s going to be wonderful in many respects… but we also have to worry about a number of bad consequences, particularly the threat of these things getting out of control.”