As someone who works in the area, this provides a decent summary of the most popular research items. The most useful and impressive part is the set of open problems at the end, which just about covers all of the main research directions in the field.
The skepticism I'm seeing in the comments really highlights how little of this work is trickling down to the public, which is very sad to see. While it can offer few mathematical mechanisms to infer optimal network design yet (mostly because just trying stuff empirically is often faster than going through the theory, so it is more common to retroactively infer things), the question "why do neural networks work better than other models?" is getting pretty close to a solid answer. Problem is, that was never the question people seem to have ever really been interested in, so the field now has to figure out what questions we ask next.
This is encouraging. The title is a bit much. "Potential points of attack for understanding what deep learning is really doing" would be more accurate but less attention-grabbing.
It might lead to understanding how to measure when a deep learning system is making stuff up or hallucinating. That would have a huge payoff. Until we get that, deep learning systems are limited to tasks where the consequences of outputting bullshit are low.
> measure when a deep learning system is making stuff up or hallucinating
That's a great problem to solve! (Maybe biased, because this is my primary research direction). One popular approach is OOD detection, but this always seemed ill-posed to me. My colleagues and I have been approaching this from a more fundamental direction using measures of model misspecification, but this is admittedly niche because it is very computationally expensive. Could still be a while before a breakthrough comes from any direction.
Here's where I'm missing understanding: for decades the idea of neural networks had existed with minimal attention. Then in 2017 Attention Is All You Need gets released and since then there is an exponential explosion in deep learning. I understand that deep learning is accelerated by GPUs but the concept of a transformer could have been used on much slower hardware much earlier.
The inflection point was 2012, when AlexNet [0], a deep convolutional neural net, achieved a step-change improvement in the ImageNet classification competition.
After seeing AlexNet’s results, all of the major ML imaging labs switched to deep CNNs, and other approaches almost completely disappeared from SOTA imaging competitions. Over the next few years, deep neural networks took over in other ML domains as well.
The conventional wisdom is that it was the combination of (1) exponentially more compute than in earlier eras with (2) exponentially larger, high-quality datasets (e.g., the curated and hand-labeled ImageNet set) that finally allowed deep neural networks to shine.
The development of “attention” was particularly valuable in learning complex relationships among somewhat freely ordered sequential data like text, but I think most ML people now think of neural-network architectures as being, essentially, choices of tradeoffs that facilitate learning in one context or another when data and compute are in short supply, but not as being fundamental to learning. The “bitter lesson” [1] is that more compute and more data eventually beats better models that don’t scale.
Consider this: humans have on the order of 10^11 neurons in their body, dogs have 10^9, and mice have 10^7. What jumps out at me about those numbers is that they’re all big. Even a mouse needs hundreds of millions of neurons to do what a mouse does.
Intelligence, even of a limited sort, seems to emerge only after crossing a high threshold of compute capacity. Probably this has to do with the need for a lot of parameters to deal with the intrinsic complexity of a complex learning environment. (Mice and men both exist in the same physical reality.)
On the other hand, we know many simple techniques with low parameter counts that work well (or are even proved to be optimal) on simple or stylized problems. “Learning” and “intelligence”, in the way we use the words, tends to imply a complex environment, and complexity by its nature requires a large number of parameters to model.
Comparing Deep Learning with neuroscience may turn out to be erroneous. They may be orthogonal.
The brain likely has more in common with Reservoir Computing (sans the actual learning algorithm) than Deep Learning.
Deep Learning relies on end to end loss optimization, something which is much more powerful than anything the brain can be doing. But the end-to-end limitation is restricting, credit assignment is a big problem.
Consider how crazy the generative diffusion models are, we generate the output in its entirety with a fixed number of steps - the complexity of the output is irrelevant. If only we could train a model to just use Photoshop directly, but we can't.
Interestingly, there are some attempts at a middle ground where a variable number of continuous variables describe an image: <https://visual-gen.github.io/semanticist/>
If you think a 2 year old is doing deep learning, you're probably wrong.
But if you think natural selection was providing end to end loss optimization, you might be closer to right. An _awful lot_ of our brain structure and connectivity is born, vs learned, and that goes for Mice and Men.
> Intelligence, even of a limited sort, seems to emerge only after crossing a high threshold of compute capacity. Probably this has to do with the need for a lot of parameters to deal with the intrinsic complexity of a complex learning environment.
Real intelligence deals with information over a ludicrous number of size scales. Simple models effectively blur over these scales and fail to pull them apart. However, extra compute is not enough to do this effectively, as nonparametric models have demonstrated.
The key is injecting a sensible inductive bias into the model. Nonparametric models require this to be done explicitly, but this is almost impossible unless you're God. A better way is to express the bias as a "post-hoc query" in terms of the trained model and its interaction with the data. The only way to train such a model is iteratively, as it needs to update its bias retroactively. This can only be accomplished by a nonlinear (in parameters) parametric model that is dense in function space and possesses parameter counts proportional to the data size. Every model we know of that does this is called "a neural network".
> but I think most ML people now think of neural-network architectures as being, essentially, choices of tradeoffs that facilitate learning in one context or another when data and compute are in short supply, but not as being fundamental to learning.
I feel like you are downplaying the importance of architecture. I never read the bitter lesson, but I have always heard more as a comment on embedding knowledge into models instead of making them to just scale with data. We know algorithmic improvement is very important to scale NNs (see https://www.semanticscholar.org/paper/Measuring-the-Algorith...). You can't scale an architecture that has catastrophic forgetting embedded in it. It is not really a matter of tradeoffs, some are really worse in all aspects. What I agree is just that architectures that scale better with data and compute do better. And sure, you can say that smaller architectures are better for smaller problems, but then the framing with the bitter lesson makes less sense.
Thanks for posting a through and accurate summary of the historical picture. I think it is important to know the past trajectory to extrapolate to the future correctly.
For a bit more context: Before 2012 most approaches were based on hand crafted features + SVMs that achieved state of the art performance on academic competitions such as Pascal VOC and neural nets were not competitive on the surface. Around 2010 Fei Fei Li of Stanford University collected a comparatively large dataset and launched the ImageNet competition. AlexNet cut the error rate by half in 2012 leading to major labs to switch to deeper neural nets. The success seems to be a combination of large enough dataset + GPUs to make training time reasonable. The architecture is a scaled version of ConvNets of Yan Lecun tying to the bitter lesson that scaling is more important than complexity.
> I think most ML people now think of neural-network architectures as being, essentially, choices of tradeoffs that facilitate learning in one context or another when data and compute are in short supply, but not as being fundamental to learning.
Is this a practical viewpoint? Can you remove any of the specific architectural tricks used in Transformers and expect them to work about equally well?
I think this question is one of the more concrete and practical ways to attack the problem of understanding transformers. Empirically the current architecture is the best to converge training by gradient descent dynamics. Potentially, a different form might be possible and even beneficial once the core learning task is completed. Also the requirements of iterated and continuous learning might lead to a completely different approach.
> The conventional wisdom is that it was the combination of (1) exponentially more compute than in earlier eras with (2) exponentially larger, high-quality datasets (e.g., the curated and hand-labeled ImageNet set) that finally allowed deep neural networks to shine.
I'd thought it was some issue with training where older math didn't play nice with having too many layers.
Sigmoid-type activation functions were popular, probably for the bounded activity and some measure of analogy to biological neuron responses. They work, but get problematic scaling of gradient feedback outside their most dynamic span.
My understanding of the development is that persistent layer-wise pretraining with RBM or autoencoder created an initiation state where the optimization could cope even for more layers, and then when it was proven that it could work, analysis of why led to some changes such as new initiation heuristics, rectified linear activation, eventually normalizations ... so that the pretraining was usually not needed any more.
One finding was that the supervised training with the old arrangement often does work on its own, if you let it run much longer than people reasonably could afford to wait around for just on speculation contrary to observations in CPU computations in the 80s--00s. It has to work its way to a reasonably optimizable state using a chain of poorly scaled gradients first though.
A much earlier major win for deep learning was AlexNet for image recognition in 2012. It dominated the competition and within a couple years it was effectively the only way to do image tasks. I think it was Jeremy Howard who wrote a paper around 2017 wondering when we’d get a transfer learning approach that worked as well for NLP as convnets did for images. The attention paper that year didn’t immediately dominate. The hardware wasn’t good enough and there wasn’t consensus on belief that scale would solve everything. It took like five more years before GPT3 took off and started this current wave.
I also think you might be discounting exactly how much compute is used to train these monsters. A single 1ghz processor would take about 100,000,000 years to train something in this class. Even with on the order of 25k GPUs training GPT3 size models takes a couple months. The anemic RAM on GPUs a decade ago (I think we had k80 GPUs with 12GB vs 100’s of GBs on H100/H200 today) and it was actually completely impossible to train a large transformer model prior to the early 2020s.
I’m even reminded how much gamers complained in the late 2010s about GPU prices skyrocketing because of ML use.
As others pointed out, the explosion of interest started with the deep convolutional networks that were applied in image problems. What I always thought was interesting was that prior to that, NNs were largely dismissed as interesting. When I took a course on them around the year 2000 that was the attitude most people took. It seems like what it took to spark renewed interest was ImageNet and seeing what you get when you have a ton of training data to throw at the problem and fast processors to help. After that the ball kept rolling with the subsequent developments around specific network architectures. In the broader community AlexNet is viewed as the big inflection point, but in the academic community you saw interest simmering a couple years earlier - I began to see more talks at workshops about NNs that weren’t being dismissed anymore, probably starting around 2008/09.
I agree with your larger point but dismissed is rather too strong. They were considered fiddly to train, prone to local minima, long training time, no clear guidelines about what the number of hidden layers and number of nodes ought to be. But for homework (toy) exercises they were still ok.
In comparison, kernel methods gave a better experience over all for large but not super large data sets. Most models had easily obtainable global minimum. Fewer moving parts and very good performance.
It turns out, however, that if you have several orders of magnitude more data, the usual kernels are too simple -- (i) they cannot take advantage of more data after a point and start twiddling the 10th place of decimal of some parameters and (ii) are expensive to train for very large data sets. So bit of a double whammy. Well, there was a third, no hardware acceleration that can compare with GPUs.
Kernels may make a comeback though, you never know. We need to find a way to compose kernels in a user friendly way to increase their modeling capacity. We had a few ways of doing just that but they weren't great. We need a breakthrough to scale them to GPT sized data sets.
In a way DNNs are "design your own kernels using data" whereas kernels came in any color you liked provided it was black (yes there were many types, but it was still a fairly limited catalogue. The killer was that there was no good way of composing them to increase modeling capacity that yielded efficiently trainable kernel machines)
> I understand that deep learning is accelerated by GPUs but the concept of a transformer could have been used on much slower hardware much earlier
But they don't give the same results at those smaller scales. People imagined, but no one could have put into practice because the hardware wasn't there yet. Simplified, LLMs is basically Transformers with the additional idea of "and a shitton of data to learn from", and for making training feasible with that amount of data, you do need some capable hardware.
The same thing happened with matrices. We had matrices for 400 years, but the field of linear algebra and especially numerical linear algebra exploded only with advent of computers.
In olden days, the correct way to solve a linear system of equations was to use theory of minors. With advent of computers, you suddenly had a huge theory of gaussian elimination, or Krylov spaces and what not.
Deep-learning hinges on highly redundant solution space (highly redundant weights), along with normalized weights (optimization methodology is commoditized). The original neural network work had no such concepts.
Agreed, there is probably a theoretical world where we got enough money/compute together and had this explosion happen earlier.
Or perhaps a world where it happened later. I think a big part of what enabled the AI boom was the concentration of money and compute around the crypto boom.
the concept of a transformer could have been used on much slower hardware much earlier.
It could have been done in the early 1970s -- see "Paper tape is all you need" at https://github.com/dbrll/ATTN-11 and the various C-64 projects that have been posted on HN -- but the problem was that Marvin Minsky "proved" that there was no way a perceptron-based network could do anything interesting. Funding dried up in a hurry after that.
I'm sure it's an oversimplification to blame the entire 1970s AI winter on Minsky, considering they couldn't have gotten much further than the proof-of-concept stage due to lack of hardware. But his voice was a loud, widely-respected one in academia, and it did have a negative effect on the field.
Don't understimate the massive data you need to make those networks tick. Also, impracticable in slow training algorithms, beyond if they were in GPUs or CPUs.
Deep learning works at a very high level because 'it can keep learning from more data' better than any other approaches. But without the 'stupid amount of data' that is available now, the architecture would be kind of irrelevant. Unless you are going some way to explain both sides of the model-data equation I don't feel you have a solid basis to build a scientific theory, e.g. 'why reasoning models can reason'. The model is the product of both the architecture and training data.
My fear is that this is as hopeless right now as explaining why humans or other animals can learn certain things from their huge amount of input data. We'll gain better empirical understanding, but it won't ever be fundamental computer science again, because the giga-datasets are the fundamental complexity not the architecture.
Well, "There Will Be a Scientific Theory of Deep Learning" looks like flag planting - an academic variant of "I told you so!", but one that is a citation magnet.
It's actually really fascinating that there isn't a scientific theory of deep learning, especially as it's a product of human engineering as opposed to e.g. biology or particle physics.
There are very good reasons why it took this long, but can be summed up as: everyone was looking in the wrong place. Deep learning breaks a hundred years of statistical intuition, and you don't move a ship that large quickly.
tbf, we've learned (ha!) more from smashing teeny tiny particles and "looking" at what comes out than from say 40 years of string theory. Sometimes doing stuff works, and the theory (hopefully) follows.
The skepticism I'm seeing in the comments really highlights how little of this work is trickling down to the public, which is very sad to see. While it can offer few mathematical mechanisms to infer optimal network design yet (mostly because just trying stuff empirically is often faster than going through the theory, so it is more common to retroactively infer things), the question "why do neural networks work better than other models?" is getting pretty close to a solid answer. Problem is, that was never the question people seem to have ever really been interested in, so the field now has to figure out what questions we ask next.
It might lead to understanding how to measure when a deep learning system is making stuff up or hallucinating. That would have a huge payoff. Until we get that, deep learning systems are limited to tasks where the consequences of outputting bullshit are low.
That's a great problem to solve! (Maybe biased, because this is my primary research direction). One popular approach is OOD detection, but this always seemed ill-posed to me. My colleagues and I have been approaching this from a more fundamental direction using measures of model misspecification, but this is admittedly niche because it is very computationally expensive. Could still be a while before a breakthrough comes from any direction.
After seeing AlexNet’s results, all of the major ML imaging labs switched to deep CNNs, and other approaches almost completely disappeared from SOTA imaging competitions. Over the next few years, deep neural networks took over in other ML domains as well.
The conventional wisdom is that it was the combination of (1) exponentially more compute than in earlier eras with (2) exponentially larger, high-quality datasets (e.g., the curated and hand-labeled ImageNet set) that finally allowed deep neural networks to shine.
The development of “attention” was particularly valuable in learning complex relationships among somewhat freely ordered sequential data like text, but I think most ML people now think of neural-network architectures as being, essentially, choices of tradeoffs that facilitate learning in one context or another when data and compute are in short supply, but not as being fundamental to learning. The “bitter lesson” [1] is that more compute and more data eventually beats better models that don’t scale.
Consider this: humans have on the order of 10^11 neurons in their body, dogs have 10^9, and mice have 10^7. What jumps out at me about those numbers is that they’re all big. Even a mouse needs hundreds of millions of neurons to do what a mouse does.
Intelligence, even of a limited sort, seems to emerge only after crossing a high threshold of compute capacity. Probably this has to do with the need for a lot of parameters to deal with the intrinsic complexity of a complex learning environment. (Mice and men both exist in the same physical reality.)
On the other hand, we know many simple techniques with low parameter counts that work well (or are even proved to be optimal) on simple or stylized problems. “Learning” and “intelligence”, in the way we use the words, tends to imply a complex environment, and complexity by its nature requires a large number of parameters to model.
0. https://en.wikipedia.org/wiki/AlexNet
1. https://en.wikipedia.org/wiki/Bitter_lesson
The brain likely has more in common with Reservoir Computing (sans the actual learning algorithm) than Deep Learning.
Deep Learning relies on end to end loss optimization, something which is much more powerful than anything the brain can be doing. But the end-to-end limitation is restricting, credit assignment is a big problem.
Consider how crazy the generative diffusion models are, we generate the output in its entirety with a fixed number of steps - the complexity of the output is irrelevant. If only we could train a model to just use Photoshop directly, but we can't.
Interestingly, there are some attempts at a middle ground where a variable number of continuous variables describe an image: <https://visual-gen.github.io/semanticist/>
Real intelligence deals with information over a ludicrous number of size scales. Simple models effectively blur over these scales and fail to pull them apart. However, extra compute is not enough to do this effectively, as nonparametric models have demonstrated.
The key is injecting a sensible inductive bias into the model. Nonparametric models require this to be done explicitly, but this is almost impossible unless you're God. A better way is to express the bias as a "post-hoc query" in terms of the trained model and its interaction with the data. The only way to train such a model is iteratively, as it needs to update its bias retroactively. This can only be accomplished by a nonlinear (in parameters) parametric model that is dense in function space and possesses parameter counts proportional to the data size. Every model we know of that does this is called "a neural network".
I feel like you are downplaying the importance of architecture. I never read the bitter lesson, but I have always heard more as a comment on embedding knowledge into models instead of making them to just scale with data. We know algorithmic improvement is very important to scale NNs (see https://www.semanticscholar.org/paper/Measuring-the-Algorith...). You can't scale an architecture that has catastrophic forgetting embedded in it. It is not really a matter of tradeoffs, some are really worse in all aspects. What I agree is just that architectures that scale better with data and compute do better. And sure, you can say that smaller architectures are better for smaller problems, but then the framing with the bitter lesson makes less sense.
For a bit more context: Before 2012 most approaches were based on hand crafted features + SVMs that achieved state of the art performance on academic competitions such as Pascal VOC and neural nets were not competitive on the surface. Around 2010 Fei Fei Li of Stanford University collected a comparatively large dataset and launched the ImageNet competition. AlexNet cut the error rate by half in 2012 leading to major labs to switch to deeper neural nets. The success seems to be a combination of large enough dataset + GPUs to make training time reasonable. The architecture is a scaled version of ConvNets of Yan Lecun tying to the bitter lesson that scaling is more important than complexity.
Is this a practical viewpoint? Can you remove any of the specific architectural tricks used in Transformers and expect them to work about equally well?
https://news.ycombinator.com/item?id=41732853
I'd thought it was some issue with training where older math didn't play nice with having too many layers.
My understanding of the development is that persistent layer-wise pretraining with RBM or autoencoder created an initiation state where the optimization could cope even for more layers, and then when it was proven that it could work, analysis of why led to some changes such as new initiation heuristics, rectified linear activation, eventually normalizations ... so that the pretraining was usually not needed any more.
One finding was that the supervised training with the old arrangement often does work on its own, if you let it run much longer than people reasonably could afford to wait around for just on speculation contrary to observations in CPU computations in the 80s--00s. It has to work its way to a reasonably optimizable state using a chain of poorly scaled gradients first though.
Even PID loops have a training phase separate from recitation phase.
I also think you might be discounting exactly how much compute is used to train these monsters. A single 1ghz processor would take about 100,000,000 years to train something in this class. Even with on the order of 25k GPUs training GPT3 size models takes a couple months. The anemic RAM on GPUs a decade ago (I think we had k80 GPUs with 12GB vs 100’s of GBs on H100/H200 today) and it was actually completely impossible to train a large transformer model prior to the early 2020s.
I’m even reminded how much gamers complained in the late 2010s about GPU prices skyrocketing because of ML use.
I agree with your larger point but dismissed is rather too strong. They were considered fiddly to train, prone to local minima, long training time, no clear guidelines about what the number of hidden layers and number of nodes ought to be. But for homework (toy) exercises they were still ok.
In comparison, kernel methods gave a better experience over all for large but not super large data sets. Most models had easily obtainable global minimum. Fewer moving parts and very good performance.
It turns out, however, that if you have several orders of magnitude more data, the usual kernels are too simple -- (i) they cannot take advantage of more data after a point and start twiddling the 10th place of decimal of some parameters and (ii) are expensive to train for very large data sets. So bit of a double whammy. Well, there was a third, no hardware acceleration that can compare with GPUs.
Kernels may make a comeback though, you never know. We need to find a way to compose kernels in a user friendly way to increase their modeling capacity. We had a few ways of doing just that but they weren't great. We need a breakthrough to scale them to GPT sized data sets.
In a way DNNs are "design your own kernels using data" whereas kernels came in any color you liked provided it was black (yes there were many types, but it was still a fairly limited catalogue. The killer was that there was no good way of composing them to increase modeling capacity that yielded efficiently trainable kernel machines)
But they don't give the same results at those smaller scales. People imagined, but no one could have put into practice because the hardware wasn't there yet. Simplified, LLMs is basically Transformers with the additional idea of "and a shitton of data to learn from", and for making training feasible with that amount of data, you do need some capable hardware.
In olden days, the correct way to solve a linear system of equations was to use theory of minors. With advent of computers, you suddenly had a huge theory of gaussian elimination, or Krylov spaces and what not.
Or perhaps a world where it happened later. I think a big part of what enabled the AI boom was the concentration of money and compute around the crypto boom.
It could have been done in the early 1970s -- see "Paper tape is all you need" at https://github.com/dbrll/ATTN-11 and the various C-64 projects that have been posted on HN -- but the problem was that Marvin Minsky "proved" that there was no way a perceptron-based network could do anything interesting. Funding dried up in a hurry after that.
What result are you referring to?
I'm sure it's an oversimplification to blame the entire 1970s AI winter on Minsky, considering they couldn't have gotten much further than the proof-of-concept stage due to lack of hardware. But his voice was a loud, widely-respected one in academia, and it did have a negative effect on the field.
My fear is that this is as hopeless right now as explaining why humans or other animals can learn certain things from their huge amount of input data. We'll gain better empirical understanding, but it won't ever be fundamental computer science again, because the giga-datasets are the fundamental complexity not the architecture.
That would be amazing, but personally I’m skeptical.
There is so much to digest here but it's fascinating seeing it all put together!