The descriptions I have read were all mathematical, focusing on the computational graph with the magical backpropagation (which frankly is just memoizing intermediate computations). The descriptions also went out of their way to discourage terms like "synapses" and rather use "units".
I remember in 2012 for my MS thesis on Deep Neural Networks spending several pages on Boltzmann Machines and the physics-inspired theories of Geoffrey Hinton.
My undergraduate degree was in physics.
So, yes, I think this is an absolutely stunning award. The connections between statistical entropy (inspired by thermodynamics) and also of course from biophysics of human neural networks should not be lost here.
Anyways, congratulations to Geoffrey Hinton. And also, since physics is the language of physical systems, why not expand the definition of the field to include the "physics of intelligence"?
From their official explanation page (https://www.nobelprize.org/uploads/2024/09/advanced-physicsp...): "With ANNs the boundaries of physics are extended to host phenomena of life as well as computation."
> I have never heard that Hopfield nets or Bolzmann machines were given any major weight in the history.
Either way, these had a lot of influence on the early work, which does permeate into the modern stuff. There's this belief that all the old stuff is useless and I just think that's wrong. There's a lot of hand engineered stuff that we don't need anymore, but a lot of the theory and underlying principles are still important.
Each layer was trained similar to the encoder part of an autoencoder. This way the layerwise transformations were not random, but roughly kept some of the original datas properties. Up to here training was done without the use of labelled data. After this training stage was done, you had a very nice initialization for your network and could train it end to end according to your task and target label.
If I recall correctly, the neural layers output was probabilistic. Because of that you couldn't simply use back propagation to learn the weights. Maybe this is the connection to John Hopkins work. But here my memory is a bit fuzzy.
From the people dissing the award here it seems like even a particularly benign internet community like HN has little notion of ML with ANN:s before Silicon Valley bought in for big money circa 2012. And media reporting from then on hasn't exactly helped.
ANN:s go back a good deal further still (as the updated post does point out) but the works cited for this award really are foundational for the modern form in a lot of ways.
As for DL and backpropagation: Maybe things could have been otherwise, but in the reality we actually got, optimizing deep networks with backpropagation alone never got off the ground on it's own. Around 2006 Hinton started getting it to work by building up layer-wise with optimizing Restricted Boltzmann Machines (the lateral connections within a layer are eliminated from the full Boltzmann Machine), resulting in what was termed a Deep Belief Net, which basically did it's job already but could then be fine-tuned with backprop for performance, once it had been initialized with the stack of RBM:s. An alternative approach with layer-wise autoencoders (also a technique essentially created by Hinton) soon followed.
Once these approaches had shown that deep ANN:s could work though, the analysis showed pretty soon that the random weight initializations used back then (especially when combined with the historically popular sigmoid activation function) resulted in very poor scaling of the gradients for deep nets which all but eliminated the flow of feedback. It might have generally optimized eventually, but after way longer wait than was feasible when run on the computers back then. Once the problem was understood, people made tweaks to the weight initialization, activation function and otherwise the optimization, and then in many cases it did work going directly to optimizing with supervised backprop. I'm sure those tweaks are usually taken for granted to the point of being forgotten today, when one's favourite highly-optimized dedicated Deep Learning library will silently apply the basic ones without so much as being requested to, but take away the normalizations and the Glorot or whatever initialization and it could easily mean a trip back to rough times getting your train-from-scratch deep ANN to start showing results.
I didn't expect this award, but I think it's great to see Hinton recognized again, and precisely because almost all modern coverage is to lazy to track down earlier history than the 2010s, not least Hopfield's foundational contribution, I think it is all the more important that the Nobel foundation did.
So going back to the original question above: there are so many bad, confused versions of neural network history going around that whether or not this one is widely accepted isn't a good measure of quality. For what it's worth, to me it seems a good deal more complete and veridical than most encountered today.
Here are some examples
https://snntorch.readthedocs.io/en/latest/tutorials/tutorial...
Would the multi-head attention (Wv) not be equivalent to the chemical gradient changes?
(there are multiple matrices in multi-head attention, one for each attention head and what I imagine would be the equivalent of different gradients
This allows each attention head to learn different representations and focus on different aspects of the input sequence.)
And then the output produced after applying the concatenated (W0 or output projection), be equivalent to the different electrical outputs such as the spikes and passed to the next neuron equivalent or attention head?
Hopfield networks led to Boltzmann machines. Deep learning started with showing that deep neural networks were viable in Hinton's 2006 Science paper, where he showed that by pre-training with a Restricted Boltzmann machine (essentially a stacked self-supervised auto-encoder) as a form of weight initialization, it was possible to effectively train neural networks with more than 2 layers. Prior to that finding, people found it was very hard to get backprop to work with more than 2 layers due to the activation functions people were using and problematic weight initialization procedures.
So long story short, while neither of them are in widespread use today, they led to demonstrating that neural networks were a viable technology and provided the FIRST strategy for successfully training deep neural networks. A few years later, people figured out ways to do this without the self-supervised pre-training phase by using activation functions with better gradient flow properties (ReLUs), better weight initialization procedures, and training on large datasets using GPUs. So without the proof of concept enabled by Restricted Boltzmann Machines, deep learning may not have become a thing, since prior to that almost all of the AI community (which was quite small) was opposed to neural networks except for a handful of evangelists (Geoff Hinton, Yoshua Bengio, Yann LeCun, Terry Sejnowski, Gary Cottrell, and a handful of other folks).