Probably not. It's not clear how to scale other methods to make use of so much compute. They top out sooner, with fewer parameters and less compute.

One way to look at why deep learning is having the impact it does is that unlike other ML methods, it's actually capable of making use of so much compute. It gives us modular ways to add more and more parameters and still fit them effectively.

What always astonishes me that deep learning seems to work on human timescales. For other problems (even if they are polynomial in complexity) we get into infeasible runtimes when we increase the problems complexity by 10x. With deep learning the fuzzy, approximative nature seems to help to grasp the gist of the 10x problem and somehow allows us to reach 95% of the solution in just e.g. 2x the runtime. Heuristics might play in the same league, but the development time kind of scales with the problem, while in deep learning I would put it in the linear or log basket.

A couple thoughts...

First, having 'infinite compute' is a way for researchers to be sure that compute isn't the thing holding back their method. So, DeepMind made a protein folder using all the compute they had available; later others managed to greatly reduce the amount of compute needed to get equivalent results, by re-implementing and innovating on DeepMind's initial write-up.

Second, I think there's a lot of interesting ground to explore in hybridizing ML with more classical algorithms. The end-to-end deep learning approach gives us models that get the best scores on imagenet classification, but are extremely prone to domain shift problems. An alternative approach is to take a 'classical' algorithm and swap out certain parts of the algorithm with a deep network. Then you should often get something more explainable: The network is explicitly filling in a specific parameter estimation, and the rest of the algorithm gives you some quality guarantees based on the quality of that parameter estimator. I saw some nice work along these lines in sparse coding a couple years ago, for example...

No, some DL architectures are unquestionably superior to others especially for some tasks. In the first few years of deep CNN development, several limits quickly became apparent (sensitivity to scale, vanishing gradients, gradient signal loss with some activation functions, and many engineering constraint tradeoffs like poor RAM use). This was addressed by making changes to a simple dense net which begat architectures that suffer less from those limits.

Without question, limits exist in every DL architecture; we just haven't taken the time to diagnose or quantify all the limits yet. Now that attention has shifted to transformers, analysis of DNNs' inherent limits is made substantially more difficult, given transformers' huge size. That, and their multi-million dollar training cost likely will make if infeasible to diagnose or cure each design's inherent limits.

Anyone has some more info about geometric deep learning? It seems to offer some theory why certain architectures work and others dont.

The advantage of DeepMind from more compute resources is cumulative, you can develop better models if you have more compute. So the question really is: If academia and other research institutions had DeepMind's resources, would their output match DeepMind's?

I would bet that 1 or 2 out of 10 would and the 8 or 9 others would vastly underperform for the most part.

Alphafold vs Alphafold 2 success: https://lh3.googleusercontent.com/KKbgSsS1qIoesiy2Ws_WDsDSyG...

Papers say that both are trained on PDB dataset. And still, we see a dramatic gap between old and new Alphafold models. Both were trained by Deepmind, probably with a similar computer power. I think it's obvious that it's not just compute power, method matters a lot.

actually, companies can buy the amount of TPU time that AlphaFold used. Note that the price breaks from "here is a listed price" to "contact sales" at the largest size (TPU v3-2048). I assume that during initial training and experimentation that DM kept 1 or two TPU v3-2048 busy 24/7 for 6 months to a year. That's entirely within the budget of well-heeled companies

ANNs are among the few kinds of programs that perform significantly better on small problems with exaflops than with teraflops. Gurobi, LS-Dyna, or American Fuzzy Lop will produce only marginally better results as you scale from a closet full of computers to a warehouse-scale computer. GCC or linear regression won't even work marginally better.

"The easiest way to make software scalable is to make it sequentially inefficient." -- Bill Gropp (1999)

That's true! But ANNs are sometimes able to do things we haven't found a way to do more efficiently, as in this case.

You can't just throw more compute at an algorithm and have it spit better results! Well, unless your algorithm uses deep learning that is. And that's why teams with a lot of compute use DL, and why team that use DL use a lot of compute

Presumably the team started with existing methods on their fleet of compute and was forced to develop new ones. While development happens your fleet also grows and your ability to utilize it expands and you end up not going back to ever try methods that are generations behind on your current fleet because the overhead of making it work doesn't justify it.

It's like asking if using the google homepage from 10k iterations ago would perform better than the current version on the present user cohorts. There's just too much invested to justify testing things like that when you can use the time to improve what is showing promise.

I don't think computational resources will have greater effect, because you can see this by simple plot running time x axis and model performance y axis.