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This is embarrassing. I would say Hopfield networks aren't even very revolutionary in neuroscience, but they're so old I can't tell. In terms of AI... they've b

This is embarrassing. I would say Hopfield networks aren't even very revolutionary in neuroscience, but they're so old I can't tell. In terms of AI... they've been irrelevant for thirty years. I guess you could argue a transformer is a generalized Hopfield network, but of course that's a post-hoc understanding. None of this has anything to do with physics. So what if an energy function lets you approximate the number of macro-states it can capture? Should every mathematics paper with Lagrange multipliers be put up for nomination? Every poll that uses the law of large numbers, and thus, entropy? Surely the computer scientists building the internet need to be included as well, since their work is based in information theory. Or maybe, hear me out, we reserve the Nobel Prize in physics for advances in the physical sciences, understanding physical reality or how to bend it to our will.

seewhydee 2y ago
Had they wanted a good ML relevant physics Nobel, the committee had decades to award a prize to Marshall and Arianna Rosenbluth for the Markov Chain Monte Carlo method. Would have been self-evidently important and relevant to both physics and ML. Too late now -- Arianna died in 2020.
kenjackson 2y ago
Curious, would Peter Shor qualify? I struggle if his work is really just CS or enough physics to be in the discussion.
- jampekka 2y ago
  There were some predictions that Peter Shor could win this year for quantum computation. I'd say his work is a lot closer to physics than Hinton's or Hopfield's.
jampekka 2y ago
Neither Hopfield networks nor Restricted Boltzmann Machines/Deep Belief Networks really panned out for any purposes outside some theoretical niches. The prize was awarded for "AI" and the tenous links to physics of some irrelevant models are just an excuse.
ants_everywhere 2y ago
I agree this is rather bizarre. But let's not forget that the brain is a physical system and that neural networks are part of the reason we understand the brain as well as we do. There was a long period where people like Chomsky thought the brain couldn't learn fast enough and that knowledge had to be innate.
- kgwgk 2y ago
  > neural networks are part of the reason we understand the brain as well as we do We don’t understand much then.
coliveira 2y ago
The goal here is to attribute a very important area in contemporary technology to physicists. This prize advances physics in terms of giving it higher importance in the minds of lay people and journalists.
benrapscallion 2y ago
This is the only plausible reason: “These artificial neural networks have been used to advance research across physics topics as diverse as particle physics, materials science, and astrophysics,” Ellen Moons, chair of the Nobel Committee for Physics, said at a press conference this morning.
- rvnx 2y ago
  Must be, because in the field of artificial intelligence, if these techniques are not in production and considered obsoletes it's for a good reason. It may have been state-of-the-art in 1980s, but now is a bit late. Very smart people in their time though. In current times, a global prize to the transformers folks at least make more sense considering the context (despite it not being Physics).
  - versteegen 2y ago
    The landmark Deep Belief Networks (stacked RBMs) paper in Science was in 2006 [1]. DBNs were completely obsolete quite quickly, but don't deny the immense influence of this line of research. It has over 23k citations, and was my introduction to deep learning, for one. And cited by the Nobel committee. You're completely incorrect to say RBMs were of theoretical interest only. They have had plenty of practical use in computer vision/image modelling up to at least a few years ago (I haven't followed them since). Remember the first generative models of human faces? Edit: Wow, Hinton is still pushing forward the state of the art on RBMs for image modelling, and I am impressed with how much they've improved in the last ~5 years. Nowhere near diffusion models, sure, but "reasonably good". [2] [1] G.E. Hinton and R. Salakhutdinov, 2006, Science. "Reducing the Dimensionality of Data with Neural Networks" [2] "Gaussian-Bernoulli RBMs Without Tears" https://arxiv.org/pdf/2210.10318 https://arxiv.org/pdf/2210.10318
- jovial_cavalier 2y ago
  Many wrenches and powertools were essential in building the LHC, but they didn't give the prize to Black & Decker or DeWalt either.
- jampekka 2y ago
deleted 2y ago
[deleted]

seewhydee 2y ago

Had they wanted a good ML relevant physics Nobel, the committee had decades to award a prize to Marshall and Arianna Rosenbluth for the Markov Chain Monte Carlo method. Would have been self-evidently important and relevant to both physics and ML. Too late now -- Arianna died in 2020.

kenjackson 2y ago

Curious, would Peter Shor qualify? I struggle if his work is really just CS or enough physics to be in the discussion.

jampekka 2y ago
There were some predictions that Peter Shor could win this year for quantum computation. I'd say his work is a lot closer to physics than Hinton's or Hopfield's.

jampekka 2y ago

Neither Hopfield networks nor Restricted Boltzmann Machines/Deep Belief Networks really panned out for any purposes outside some theoretical niches. The prize was awarded for "AI" and the tenous links to physics of some irrelevant models are just an excuse.

ants_everywhere 2y ago

I agree this is rather bizarre. But let's not forget that the brain is a physical system and that neural networks are part of the reason we understand the brain as well as we do. There was a long period where people like Chomsky thought the brain couldn't learn fast enough and that knowledge had to be innate.

kgwgk 2y ago
> neural networks are part of the reason we understand the brain as well as we do We don’t understand much then.

coliveira 2y ago

The goal here is to attribute a very important area in contemporary technology to physicists. This prize advances physics in terms of giving it higher importance in the minds of lay people and journalists.

benrapscallion 2y ago

This is the only plausible reason: “These artificial neural networks have been used to advance research across physics topics as diverse as particle physics, materials science, and astrophysics,” Ellen Moons, chair of the Nobel Committee for Physics, said at a press conference this morning.

rvnx 2y ago
Must be, because in the field of artificial intelligence, if these techniques are not in production and considered obsoletes it's for a good reason. It may have been state-of-the-art in 1980s, but now is a bit late. Very smart people in their time though. In current times, a global prize to the transformers folks at least make more sense considering the context (despite it not being Physics).
- versteegen 2y ago
  The landmark Deep Belief Networks (stacked RBMs) paper in Science was in 2006 [1]. DBNs were completely obsolete quite quickly, but don't deny the immense influence of this line of research. It has over 23k citations, and was my introduction to deep learning, for one. And cited by the Nobel committee. You're completely incorrect to say RBMs were of theoretical interest only. They have had plenty of practical use in computer vision/image modelling up to at least a few years ago (I haven't followed them since). Remember the first generative models of human faces? Edit: Wow, Hinton is still pushing forward the state of the art on RBMs for image modelling, and I am impressed with how much they've improved in the last ~5 years. Nowhere near diffusion models, sure, but "reasonably good". [2] [1] G.E. Hinton and R. Salakhutdinov, 2006, Science. "Reducing the Dimensionality of Data with Neural Networks" [2] "Gaussian-Bernoulli RBMs Without Tears" https://arxiv.org/pdf/2210.10318 https://arxiv.org/pdf/2210.10318
jovial_cavalier 2y ago
Many wrenches and powertools were essential in building the LHC, but they didn't give the prize to Black & Decker or DeWalt either.
jampekka 2y ago

deleted 2y ago

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