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The list of Nobel laureates reads like a collection of humanity’s greatest treasures: Albert Einstein, Marie Curie, Francis Crick, Toni Morrison. As of this morning, it also includes two physicists whose research, in the 1980s, laid the foundations for modern artificial intelligence.

Earlier today, the 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton for using “tools from physics to develop methods that are the foundation of today’s powerful machine learning.” Hinton is sometimes referred to as a “godfather of AI,” and today’s prize—one that is intended for those whose work has conferred “the greatest benefit to humankind”—would seem to mark the generative-AI revolution, and tech executives’ grand pronouncements about the prosperity that ChatGPT and its brethren are bringing, as a fait accompli.

Not so fast. Committee members announcing the prize, while gesturing to generative AI, did not mention ChatGPT. Instead, their focus was on the grounded ways in which Hopfield and Hinton’s research, which enabled the statistical analysis of enormous datasets, has transformed physics, chemistry, biology, and more. As I wrote in an article today, the award “should not be taken as a prediction of a science-fictional utopia or dystopia to come so much as a recognition of all the ways that AI has already changed the world.”

AI models will continue to change the world, but AI’s proven applications should not be confused with Big Tech’s prophecies. Machines that can “learn” from large datasets are the stuff of yesterday’s news, and superintelligent machines that replace humans remain the stuff of yesterday’s novels. Let’s not forget that.

Illustration by The Atlantic. Source: Science & Society Picture Library / Getty.

AI’s Penicillin and X-Ray Moment

By Matteo Wong

Today, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for groundbreaking statistical methods that have advanced physics, chemistry, biology, and more. In the announcement, Ellen Moons, the chair of the Nobel Committee for Physics and a physicist at Karlstad University, celebrated the two laureates’ work, which used “fundamental concepts from statistical physics to design artificial neural networks” that can “find patterns in large data sets.” She mentioned applications of their research in astrophysics and medical diagnosis, as well as in daily technologies such as facial recognition and language translation. She even alluded to the changes and challenges that AI may bring in the future. But she did not mention ChatGPT, widespread automation and the resulting global economic upheaval or prosperity, or the possibility of eliminating all disease with AI, as tech executives are wont to do.

Read the full article.

What to Read Next

Today’s Nobel Prize announcement focused largely on the use of AI for scientific research. In an article last year, I reported on how machine learning is making science faster and less human, in turn “challenging the very nature of discovery.” Whether the future will be awash with superintelligent chatbots, however, is far from certain. In July, my colleague Charlie Warzel spoke with Sam Altman and Ariana Huffington about an AI-based health-care venture they recently launched, and came away with the impression that AI is becoming an “industry powered by blind faith.”

P.S.

A couple weeks ago, I had the pleasure of speaking with Terence Tao, perhaps the world’s greatest living mathematician, about his perceptions of today’s generative AI and his vision for an entirely new, “industrial-scale” mathematics that AI could one day enable. I found our conversation fascinating, and hope you will as well.

— Matteo

When the Swedish inventor Alfred Nobel wrote his will in 1895, he designated funds to reward those who “have conferred the greatest benefit to humankind.” The resulting Nobel Prizes have since been awarded to the discoverers of penicillin, X-rays, and the structure of DNA—and, as of today, to two scientists who, decades ago, laid the foundations for modern artificial intelligence.

Today, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for groundbreaking statistical methods that have advanced physics, chemistry, biology, and more. In the announcement, Ellen Moons, the chair of the Nobel Committee for Physics and a physicist at Karlstad University, celebrated the two laureates’ work, which used “fundamental concepts from statistical physics to design artificial neural networks” that can “find patterns in large data sets.” She mentioned applications of their research in astrophysics and medical diagnosis, as well as in daily technologies such as facial recognition and language translation. She even alluded to the changes and challenges that AI may bring in the future. But she did not mention ChatGPT, widespread automation and the resulting global economic upheaval or prosperity, or the possibility of eliminating all disease with AI, as tech executives are wont to do.

Hopfield’s and Hinton’s respective research did lay the groundwork for the generative-AI revolution that Google CEO Sundar Pichai has compared to the harnessing of fire. In 1982, Hopfield invented a way for computer programs to store and recall patterns, reminiscent of human memory, and three years later, Hinton devised a way for programs to detect patterns from a set of examples. Those two methods and subsequent advances enabled this century’s machine-learning revolution, which is built upon machines that detect, store, and reproduce statistical patterns from huge amounts of data, such as genetic sequences, weather forecasts, and internet text.

The Nobel committee focused its remarks on the foundational aspects of artificial neural networks: the ability to feed unfathomably large and complex amounts of data into an algorithm that will then, more or less undirected, detect previously unseen and consequential patterns in those data. As a result, drug discovery, neuroscience, renewable-energy research, and particle physics are fundamentally changing. Last year, a biomedical researcher at Harvard told me, “We can really make discoveries that would not be possible without the use of AI.” All sorts of nonchatbot algorithms across the internet, on social-media and e-commerce and media websites, use neural networks. In a presentation about today’s award, the theoretical physicist Anders Irbäck, another committee member, noted how these neural networks have been applied in astrophysics, materials science, climate modeling, and molecular biology.

Following the announcement, journalists were eager to ask about generative AI and ChatGPT, and Hinton—who has frequently voiced fears of an AI apocalypse—likened its influence to that of the Industrial Revolution. “We have no experience of what it’s like to have things smarter than us,” Hinton, who called into the ceremony, said. But the two committee members giving answers, Moons and Irbäck, demurred on questions about “GPT” and danced around Hinton’s doomerism.

Today’s award, in other words, should not feed the AI-hype cycle. It is a celebration of the ways in which machine-learning research “benefits all of humanity,” to borrow OpenAI’s phrase, in largely unseen, grounded ways that are no less important for that pragmatism. The prize should not be taken as a prediction of a science-fictional utopia or dystopia to come so much as a recognition of all the ways that AI has already changed the world.