Nobel Physics Prize Honors Roots of Modern AI

The Royal Swedish Academy of Science has awarded the 2024 Nobel Prize in Physics to John Hopfield of Princeton University, USA, and Geoffrey Hinton of the University of Toronto, Canada, for “foundational discoveries and inventions that enable machine learning with artificial neural networks.”

In awarding the prize to Hopfield and Hinton, the academy highlighted work from the mid-1980s that is having an almost unimaginable—and still ultimately uncertain—impact some 40 years later. In the field of optics alone, contemporary applications of machine learning, built on the foundational computer-science work of the two laureates, show up in areas as diverse as device design, the search for new laser materials, new ideas on diffractive analog networks for processing data with light, image analysis and a host of other areas. And the burgeoning data demands of AI computation are spurring new interest in energy-efficient optical approaches to data transmission and even neural-net processing itself.

Landmark work in pattern recognition

Soon after the advent of electronic computers, beginning in the 1950s, researchers wondered whether the machines might be pressed into service in various forms of pattern recognition, a fundamental AI task. They also began to speculate on whether approaches adapted from modeling what goes on in the human brain—so-called artificial neural networks (ANNs)—might do the trick.

Early efforts to realize that promise, such as the feedforward network for image interpretation devised by Frank Rosenblatt in 1957, unearthed some of the core concepts. But the outputs of this early work ultimately proved disappointing, leading to the “AI winter” in funding and research opportunities that started in the early 1970s.

A thaw of sorts began in the 1980s, with breakthroughs, including those of the two 2024 Nobel physics laureates, that rekindled interest in AI and machine learning. In 1982, one of the pair, John Hopfield—a theoretical physicist who was already a significant figure in biological physics—extended that background by proposing a simple model for associative memory in a recurrent neural network, which came to be called a Hopfield network.

Hopfield wondered if a large enough network of this type could ultimately enable “emergent” computational abilities analogous to those of the brain. He followed up his initial work with subsequent studies fleshing out how the model might be applied in pattern completion, error correction and various kinds of optimization problems.

Geoffrey Hinton, meanwhile, working with a number of colleagues, used the concepts of statistical mechanics to extend Hopfield’s initial network, creating a so-called Boltzman machine. The application of statistical techniques was a crucial advance. That’s because, unlike the Hopfield model, the Boltzman machine was a generative model, capable of drawing inferences and generating new data instances based on probability distributions.

Even more consequential, perhaps, was Hinton’s development (with David Rummelhart and Ronald Williams) of a workable feedforward neural network, enabled through so-called backpropagation algorithms. These algorithms, which involve a “hidden” layer in the neural network that handles error correction through a loss function and selective weightings, have become a core tool in training so-called deep neural networks, and underlie most of today’s headline-grabbing ANNs such as GPT4.

Enormous influence

It would be difficult to overstate the importance and impact of these findings from four decades ago on the AI advances that are now reshaping science and society. That impact is apparent in high-profile tools ranging from Google Translate to ChatGPT; in face-recognition applications (for both good and ill); in efforts to apply machine intelligence to the interpretation of medical images; and in much more.

In science, meanwhile, AI techniques based on the 2024 laureates’ foundational work have energized discovery in a variety of data-intensive undertakings. These capabilities have proved particularly useful in high-resolution climate modeling, particle physics, and astrophysics. The eye-catching images of the black hole at the center of the Milky Way from the Event Horizon Telescope, for example, would likely have been impossible without the data-processing contribution of ANNs.

It’s difficult to identify an area of optical research that has not been touched by the work of Hopfield and Hinton.

AI tools are also spawning radically new ways of doing research. A case in point: A recent study in Science documented the use of so-called self-driving labs—which involve multi-institution, distributed laboratories combining AI and robotics—to uncover scores of new candidates for laser materials in record time. Elsewhere in optics and photonics, researchers are increasingly turning to libraries of AI software tools to supercharge device design, to refine lens setups for AI applications, to identify tiny flaws in solar cells, to develop new tools for image interpretation and noise reduction in communications, and for a raft of other undertakings. Indeed, it’s difficult to identify an area of optical research that has not been touched by the work of Hopfield and Hinton.

A nod toward AI’s benefits—and threats

Reached by phone during the press conference announcing the prize, one of the laureates, Geoffrey Hinton—who admitted that he was “flabbergasted” by the news—acknowledged the prospects of the AI technology his work has advanced for improving human society.

“I think it will have a huge influence; it will be comparable with the industrial revolution,” he said. “But instead of exceeding people in physical strength, it’s going to exceed people in intellectual ability.” That capability, he said, should prove “wonderful in many respects,” in areas such as health care, industrial efficiency and economic productivity.

Hinton said that the impact of AI should prove “wonderful in many respects”—but, interestingly, he also alluded to some of the dark auguries surrounding AI that have become a staple of debate.

Yet, interestingly, Hinton also alluded to some of the dark auguries surrounding AI that have become a staple of political debate, popular art and science fiction. “We have no experience of what it’s like to have things smarter than us,” he said, adding that “we have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control.”

Even so, Hinton stressed that given the chance, he would do it all again. In response to a question from a reporter about comments Hinton had made in an interview with the New York Times—in which he suggested that, in view of the potential risks posed by unbridled artificial general intelligence, he regretted part of his life’s work—the newly minted laureate noted that there are “two kinds of regret.” One, he said, relates to regretting doing something that one knew at the time one shouldn’t have done; the other involves something that “may, in the end, not turn out well,” but that “you’d do again in the same circumstances.” His regret, he said, was of the second kind.