When Swedish inventor Alfred Nobel wrote his will in 1895, he allocated funds to reward those who “have brought the greatest benefit to mankind.” The resulting Nobel Prizes have now gone to the discoverers of penicillin, X-rays and the structure of DNA – and, as of today, to two scientists who laid the foundation for modern artificial intelligence decades ago.

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, chair of the Nobel Committee for Physics and a physicist at Karlstad University, praised the work of the two laureates, who “used fundamental concepts in statistical physics to design artificial neural networks” that “find patterns at large “Can datasets.” She mentioned applications of her research in astrophysics and medical diagnostics, as well as everyday technologies like facial recognition and language translation. She even alluded to the changes and challenges that AI could bring in the future. However, she did not mention ChatGPT, the widespread automation and resulting global economic disruption or prosperity, or the possibility of eliminating all diseases with AI, as technology managers usually do.

Hopfield and Hinton’s respective research laid the foundation for the generative AI revolution, which Google CEO Sundar Pichai has likened to the use 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 developed a way for programs to recognize patterns from a series of examples. These two methods and the resulting advances enabled this century’s machine learning revolution, based on machines that recognize, store and reproduce statistical patterns from vast amounts of data such as genetic sequences, weather forecasts and Internet text.

The Nobel Committee focused its remarks on the fundamental aspects of artificial neural networks: the ability to feed unimaginably large and complex amounts of data into an algorithm that then detects, in a more or less undirected manner, previously unseen and consequential patterns in that data. As a result, drug discovery, neuroscience, renewable energy research and particle physics are undergoing fundamental changes. Last year, a biomedical researcher at Harvard told me, “We can really make discoveries that wouldn’t be possible without using AI.” All kinds of non-chatbot algorithms on the internet, social media, e-commerce, and Media websites use neural networks. In a presentation for today’s award, theoretical physicist Anders Irbäck, another member of the committee, pointed out how these neural networks have been applied to astrophysics, materials science, climate modeling and molecular biology.

Following the announcement, journalists eagerly asked about generative AI and ChatGPT, and Hinton – who has often expressed fears of an AI apocalypse – compared their impact to that of the Industrial Revolution. “We have no experience of what it’s like to have things smarter than us,” said Hinton, who attended the ceremony. But the two committee members who gave answers, Moons and Irbäck, dismissed questions about “GPT,” sidestepping Hinton’s Doomerism.

In other words, today’s award should not fuel the AI ​​hype cycle. It’s a tribute to the way machine learning research “benefits all of humanity,” to use OpenAI’s phrase, in largely invisible, informed ways that are no less important to that pragmatism. The prize should not be seen as a prediction of a coming science fiction utopia or dystopia, but rather as a recognition of how AI has already changed the world.