October 8, 2024
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Nobel Prize in Physics awarded for groundbreaking achievements in machine learning
The 2024 Nobel Prize in Physics was awarded to John Hopfield and Jeffrey Hinton for their development of technology that laid the foundations for revolutionary advances in artificial intelligence.
The Nobel Committee announced that John Hopfield and Jeffrey Hinton have been awarded this year’s Nobel Prize in Physics for their “fundamental discoveries and inventions that make machine learning possible through artificial neural networks.”
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The human brain is generally considered to be the most powerful and flexible computer in the known universe, with billions of interconnected neurons that generate consciousness. But scientists have been seeking to change this for decades through machine learning approaches that emulate the brain’s adaptive computational capabilities. The 2024 Nobel Prize in Physics was awarded on Tuesday to American scientist John Hopfield and British scientist Geoffrey Hinton. They each used the tools of physics to develop artificial neural networks that laid the foundation for much of today’s cutting-edge artificial intelligence (AI). ) application.
Hinton told the Royal Swedish Academy of Sciences by phone while in California that he was “surprised” to learn of the award. After decades of efforts to advance AI, he is now one of the most prominent advocates for better safeguards, stepping down from an influential position at Google last year and People can now speak more freely about risks. “(AI) will be comparable to the industrial revolution,” he said in a phone interview. “But instead of surpassing people in physical strength, we will surpass people in intelligence. We have no experience of what it’s like to have things smarter than we are. It’s going to be great in that way… But we also have to worry about a lot of possible bad outcomes, especially the threat of these things getting out of control.”
Inspired by the anatomy of the brain, artificial neural networks represent neurons with nodes that have different values. These nodes form a network of connections that resembles the brain’s natural neural synapses, and can be made stronger or weaker by training on any dataset. This adaptive response allows artificial neural networks to better recognize patterns in the data and subsequently make future predictions. That is, it can be learned without being explicitly programmed.
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Hopfield, a professor at Princeton University in New Jersey, devised an associative memory, the so-called Hopfield network, that stores and recreates images and other patterns in data. Professor Hinton at the University of Toronto combined Hopfield’s method with another approach that excels at pattern recognition: the Boltzmann machine. Hinton’s method can be used, for example, to classify images or create new instances of observed patterns. This technology has helped fuel explosive advances in artificial intelligence that have transformed countless areas of human activity. Hopfield and Hinton both began their pioneering efforts in the 1980s.
“The breakthrough of this year’s physics laureates is built on the fundamentals of physical science,” the Nobel committee said on X (formerly Twitter). “They have shown us a whole new way of using computers to help and guide our efforts to address the many challenges facing society.”
Ellen Moons, a professor at Sweden’s Karlstad University and chair of the Nobel Committee for Physics, outlined the promise and dangers of AI in remarks at a press conference in Stockholm on Tuesday. “The winners’ discoveries and inventions form the building blocks of machine learning that can help humans make faster and more reliable decisions, such as when diagnosing medical conditions. While there are benefits, its rapid development also raises concerns for our future. Humanity collectively has a responsibility to use this new technology in a safe and ethical manner for the best benefit of humanity. I owe it.”
