Summarizer

> why do neural networks work better than other models The only people for whom this is an open question are the academics - everyone else understands it's entirely because of the bagillions of parameters.

No it isn't, and it's frustrating when the "common wisdom" tries to boil it down to this. If this was true, then the models with "infinitely many" parameters would be amazing. What about just training a gigantic two-layer network? There is a huge amount of work trying to engineer training procedures that work well. The actual reason is due to complex biases that arise from the interaction of network architectures and the optimizers and persist in the regime where data scales proportionally to model size. The multiscale nature of the data induces neural scaling laws that enable better performance than any other class of models can hope to achieve.

Also massive human work done on them, that wasn't done before. Data labeling is pretty big industry in some countries and I guess dropping 200 kilodollars on labeling is beyond the reach of most academics, even if they would not care about ethics of that.

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. 0. https://en.wikipedia.org/wiki/AlexNet 1. https://en.wikipedia.org/wiki/Bitter_lesson

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.

Indeed. I would add a third factor to compute and datasets: the lego-like aspect of NN that enabled scalable OSS DL frameworks. I did some ML in mid 2000s, and it was a PITA to reuse other people code (when available at all). You had some well known libraries for SVM, for HMM you had to use HTK that had a weird license, and otherwise looking at experiments required you to reimplement stuff yourself. Late 2000s had a lot of practical innovation that democratized ML: theano and then tf/keras/pytorch for DL, scikit learn for ML, etc. That ended up being important because you need a lot of tricks to make this work on top of "textbook" implementation. E.g. if you implement EM algo for GMM, you need to do it in the log space to avoid underflow, DL as well (gorot and co initialization, etc.).

> 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".

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 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.

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.

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.