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AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

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Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#311

Post by curiousllama »

schemescape wrote: post #300

Did AlphaFold2 also have the biggest budget? :) Edit: from the other HN article on this topic: > We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks https://deepmind.com/blog/…

Actually, no! Or at least, the budget they used (In other words, it's less like GPT3 and more like ImageNet.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#312

Post by whimsicalism »

curiousllama wrote: post #311
schemescape wrote: post #300

Did AlphaFold2 also have the biggest budget? :) Edit: from the other HN article on this topic: > We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks https://deepmind.com/blog/…

Actually, no! Or at least, the budget they used ( In other words, it's less like GPT3 and more like ImageNet.

I don't know - tens of thousands per train is not accessible for most academic institutions when you consider the necessity of ablation studies, experimentation, etc.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#313

Post by Supermancho »

elcritch wrote: post #270
beowulfey wrote: post #249

Earlier quoted context omitted.

I mean, credit where credit is due. Google employs some of the greatest names in artificial intelligence and the DeepMind team had a huge chunk of them working on this problem. While the resources may have been available, I don’t think any other single institution had the level of brain power.

It also makes one reconsider the notion that monopolies are entirely bad. This essentially appears to be a vanity project for Google. Though of course they'll benefit from it in many ways, but it's not like they're doing this as the core product of their service. It's a pretty awesome achievement.

> It also makes one reconsider the notion that monopolies are entirely bad.

Much like political dictators, they can be exceedingly efficient and have resources (and authority) to do things in spite of opposing interests.

People who faced with the narrative that countries have a monopoly on a number of aspects of life find monopolies are not a BAD THING(tm), but that they are bad for a consumer market - as a monopoly eventually blockades aspects of the market.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#314

Post by breatheoften »

martinpw wrote: post #305
mabbo wrote: post #30

Sometimes announcements like this are a bit over-the-top. But what really, to me, cements the 'big-deal' of this is the "Median Free-Modelling Accuracy" graph half way down the page. Scores of 30-45 for 15 years. Now scores of 87-92. This isn't a minor improvement, it's a leap forward.

Why is the graph not monotonically increasing? Does the complexity of the problem to be solved increase each time? If so, does that make the relative improvement from the previous result even more impressive?

That's quite interesting ... I believe the test set size is not constant year to year but rather a function of how many new structures have been experimentally discovered since the last contest?

Does seem like the contest structure could include quite a bit of risk for hiding the effect of overfitting ... I wonder if there is anything inherent about the problem that reduces that risk ...?

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#315

Post by lacksconfidence »

curiousllama wrote: post #311
schemescape wrote: post #300

Did AlphaFold2 also have the biggest budget? :) Edit: from the other HN article on this topic: > We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks https://deepmind.com/blog/…

Actually, no! Or at least, the budget they used ( In other words, it's less like GPT3 and more like ImageNet.

Is the cost to train really the relevant metric for developing this? It seems like the salary's involved are probably at least 10x whatever they spent on hardware.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#316

Post by uoaei »

visarga wrote: post #291
uoaei wrote: post #256

No, they didn't. They approximated a solution to protein folding. The two are different concepts -- this isn't the typical HN pedantry. "Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded. Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usua…

> "Solving" the problem would entail developing an interpretable algorithm It looks like you'd like a grokable solution, but the problem might be just too complex to grasp for the human brain. "Solved" means they solved the protein puzzles on the official benchmark. > but you would expect this with any suitable universal function approximator Yeah, it's just that easy. Function approximator, engage! It took a team of…

> the problem might be just too complex to grasp for the human brain

Maybe all at once, but having a self-consistent, unified theory is very important.

We can't understand the full brain, but we can understand the essential components and how they work together. This still constitutes "interpretable".

> The universal function approximation theorem doesn't also say how to find that network.

Correct, and irrelevant.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#318

Post by FrojoS »

beowulfey wrote: post #254
treis wrote: post #143

Earlier quoted context omitted.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

I don’t think you can say DeepMind could ever be more accurate to the true physical structure since it was built on the same experimental structures that it is being compared to. The limit of accuracy is the experimental data. However, I think we can say that a DeepMind prediction could at least be as good as a new experimental structure.

Is that true? I thought fundamentally, the simulation tries to find the state of lowest energy, which is defined by physics. So, your result can be better than the data set used for training.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#319

Post by zaroth »

zaroth wrote: post #224
throwawayiionqz wrote: post #204

Earlier quoted context omitted.

This is the cost of training the final architecture with all the refinements enabled by years of research. These years of research involved trying many different architectures, many of which received as much or more compute time than the final system. The price of training the final architecture is meaningless. Researching and training AlphaGo was expensive but it enabled the ideas and development of AlphaZero which…

> The price of training the final architecture is meaningless. The research is the giant shoulders you stand on, the compute cost is the price of the tool you need to do the present-day work. Both are relevant but the shoulder’s of giants are generally more accessible, particularly if we’re talking about published research and not proprietary tech. A competing team is not starting from the same place the DeepMind tea…

To expand on this, after fully reading AlQuraishi's "What Just Happened" post from a couple years ago, was this point that he made;

> I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. There are dozens of academic groups, with researchers likely numbering in the (low) hundreds, working on protein structure prediction. We have been working on this problem for decades, with vast expertise built up on both sides of the Atlantic and Pacific, and not insignificant computational resources when measured collectively. For DeepMind’s group of ~10 researchers, with primarily (but certainly not exclusively) ML expertise, to so thoroughly route everyone surely demonstrates the structural inefficiency of academic science. This is not Go, which had a handful of researchers working on the problem, and which had no direct applications beyond the core problem itself. Protein folding is a central problem of biochemistry, with profound implications for the biological and chemical sciences. How can a problem of such vital importance be so badly neglected?

In short, academia got utterly schooled by a small group at Google spending a relatively small dollar amount on compute, using techniques that in hindsight are fairly described as "simplistic". There's no way around it.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#320

Post by [deleted] »

uoaei wrote: post #316
visarga wrote: post #291

Earlier quoted context omitted.

> "Solving" the problem would entail developing an interpretable algorithm It looks like you'd like a grokable solution, but the problem might be just too complex to grasp for the human brain. "Solved" means they solved the protein puzzles on the official benchmark. > but you would expect this with any suitable universal function approximator Yeah, it's just that easy. Function approximator, engage! It took a team of…

> the problem might be just too complex to grasp for the human brain Maybe all at once, but having a self-consistent, unified theory is very important. We can't understand the full brain, but we can understand the essential components and how they work together. This still constitutes "interpretable". > The universal function approximation theorem doesn't also say how to find that network. Correct, and irrelevant.

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