Nobel Prize in Physics Goes To Machine Learning Pioneers Hopfield and Hinton (nobelprize.org) 25
John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto were awarded the Nobel Prize in Physics on Tuesday for their groundbreaking work in machine learning. The Royal Swedish Academy of Sciences recognized the scientists for developing artificial neural networks capable of recognizing patterns in large data sets, laying the foundation for modern AI applications like facial recognition and language translation.
Hopfield, 91, created an associative memory system for storing and reconstructing data patterns. Hinton, 76, invented a method for autonomous data property identification. "This year's physics laureates' breakthroughs stand on the foundations of physical science," the Nobel Committee stated. "They have shown a completely new way for us to use computers to tackle many of society's challenges." The laureates will share the 11 million Swedish kronor ($1.1 million) prize.
Hopfield, 91, created an associative memory system for storing and reconstructing data patterns. Hinton, 76, invented a method for autonomous data property identification. "This year's physics laureates' breakthroughs stand on the foundations of physical science," the Nobel Committee stated. "They have shown a completely new way for us to use computers to tackle many of society's challenges." The laureates will share the 11 million Swedish kronor ($1.1 million) prize.
Physics? (Score:5, Insightful)
Re: Physics? (Score:2)
I was thinking the sane thing. But they're trying to stay relevant and be hip. There have been other recent changes that only make sense because of social pressure and not scientific excellence.
I could though defend them a bit in the sense that particle physics is stuck in a bit of a rut. String theory and loop quantum gravity have not gone anywhere. All the test results have been negative. Sabine Hossenfelder has been pointing this out for a while. So they're a bit desperate for something that is a genuine
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The urban legend as to why there's no Nobel Prize for Mathematics is that Nobel's wife had an affair with a mathematician. However, seeing as Alfred Nobel was never married, this legend is extremely Urban...
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That's not it at all. They've gone into great detail as to why it was awarded for physics [nobelprize.org].
TL/DR: Hopman is a physicist. Like, his thesis literally was "A quantum-mechanical theory of the contribution of excitons to the complex dielectric constant of crystals". When he switched to neural networks, he did so from a physics basis: his model was based on energy minimization landscapes. Hinton expanded on that by bringing in statistical physics, originating from the work of Boltzmann - he literally called his
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Or if the TL/DR itself was TL/DR:
Physics gave birth to machine learning, and now heavily relies on it for further discoveries.
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The Nobel Prize categories are physics, chemistry, physiology or medicine, literature and peace, as laid out by Alfred Nobel himself, plus the economics prize added in his memory. Short of going against Alfred Nobel's will and adding new categories, it seems sensible of the Nobel Committee to be somewhat flexible when there are eminently deserving nominees whose work doesn't quite fit into any of the existing ones. Hopfield and Hinton's work may not be traditional physics, but it is rooted in statistical ph
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Short of going against Alfred Nobel's will and adding new categories, it seems sensible of the Nobel Committee to be somewhat flexible when there are eminently deserving nominees whose work doesn't quite fit into any of the existing ones.
Nobel wanted to give an award specifically for physics, so torturing the definition of physics to give awards to those in other fields isn't going against his wishes? There already well respected awards for computer science and math, why does the Nobel committee need to include those categories into physics as well?
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Ellen Moons, a member of the Nobel committee at the Royal Swedish Academy of Sciences, said the two laureates "used fundamental concepts from statistical physics to design artificial neural networks that function as associative memories and find patterns in large data sets."
from https://phys.org/news/2024-10-... [phys.org]
An interesting article regardless of what you think.
Physics is Measurement (Score:2)
It's strange to me that this invention has been categorized as "physics", when it sounds like it has much more to do with mathematics or computer science.
I'm of the opinion that AI is a subset of physics and not mathematics.
AI is fundamentally finding patterns within noisy measurements and learning to make predictions.
The relevant difference here is that AI is based on *measurements*, while mathematics is pure and not tied to anything physical. One could look at non-Euclidean geometry as a good example: space could be curved like a sphere, or space could be curved like a saddle, or space could be not curved (flat). All three lead to interesting results, only
Can't wait for an AI to win (Score:2)
Physics (Score:2)
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"Hopfield Net" (Score:2)
hah! that's the first network we studied in AI class waaaay back in the 90's. "Does it think or does it stink?" That was our mantra while developing.
Training back then took absolute ages though and didn't produce results that were especially useful. Back then, computer IO and peripherals were still pretty limited. OCR was about the most useful application at the time. Certainly nothing with audio.
Of course one of the bigger challenges with developing any neural network is to come up with an efficient
91 years old ? (Score:1)
91 years old ? And in a field that's sort of bleeding edge?
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AI is not bleeding edge at all.
Vast majority of foundational concepts in AI - and ML, which used to be not exactly the same thing - are 50 years old.
They simply missed the raw computing power to apply those ideas in meaningful ways.
PS: Not to say that nothing happened in 50 years of course, but the field is way older than people usually recognises.
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AI is not "bleeding edge". Most of what is used today I learned 35 years ago when studying CS. Sure, there have been performance and size improvements and the actual language pre- and post-processing (which is not AI) is impressive. But the actual capabilities of the current models are nothing special. Even "hallucinations" are an old observation.
Grasping for relevance (Score:1)
The Nobel committee is trying to remain important by riding the AI bubble, but by doing this, they are making themselves less so. Are they trying to turn the science prizes into the peace prize?
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"The Nobel committee is trying to remain important"
Have you seen who they've awarded their "peace" prizes to recently? That ship sailed LONG ago.
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Peace prizes are separate from the science prizes, which is something to which I alluded in my comment.
The peace prizes indeed were rendered meaningless long, long ago, but the science prizes have generally still been worth taking seriously.
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Not sure I agree. They must have had these two on the radar for a while. It would have been better to give them the prize before the current AI craze though.
Hinton seems to be a bit unhinged these days (Score:2)
Probably what happens to an ageing Physicist, many of them start to hallucinate "deep truths". Does not diminish his earlier accomplishments though.
Nobel Prize in Literature (Score:1)