Let's imagine that as a researcher I make a breaktrhough NN model, but that I need a lot of TPUs/GPUs in order to test it, is there a service for temporarily lending such hardware to me for free/not much ? (e.g google colab ?)
Otherwise researchers will plateau with their hardware budget.
AlphaFold: a solution to a 50-year-old grand challenge in biology
431–440 of 683 posts
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#432Like this is awesome and a huge advancement but one thing that worries me with an AI solution is that it doesn't really draw us any closer to the why. Why do proteins fold the way they do? We can predict the resulting structure which is extremely significant, we have no clue why. While we get the insight of being able to predict some structures we don't get the insight of why things are happening the way they are. In…
These are predictions. Presumably the proteins will be inspected and the model refined and updated before we start using DNA without first checking the output.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#433Very interesting, however now the problem becomes to characterize such machine learning approaches. With traditional simulation methods the authors can usually explain easily in which situation a specific approach is good or bad, with neural networks we don't really have a good approach how to analyze the quality of the prediction.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#434Earlier quoted context omitted.
Determining what a protein structure does might be even harder than folding. Right now we can't really do that ab initio, you have determine the activity in the lab and then look at the structure. And that allows you to potentially identify this motif in other proteins. If someone produces an AI that you give a sequence and it tells you what the protein does exactly, I'd be extremely impressed. I don't see that happe…
Five years ago, I would have said the following: "If someone produces an AI that you give a sequence and it tells you the protein conformation, I'd be extremely impressed". Sure there are many more things to solve in this space; but that doesn't take away that this is an impressive achievement and does unlock quite a few things (including making more tractable the problem you just brought up). I'm excited to see what…
I think I have to clarify that my response was to a large part to the "this will change all our lives" part, and might look too negative on its own. I'm very, very impressed by these results, but that still doesn't mean that we just solved biology. If this works that well on folding, this could mean that a lot of other stuff that simply didn't work well in silico might come into reach.
I'm maybe overcompensating for the tech-centric population here, with some comments speculating for very near and drastic impacts from discoveries like this. Biology and life sciences are much slower, and there's always more complexity below every breakthrough. That does tend to push me towards commenting with the more skeptical and sober view here.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#435Earlier quoted context omitted.
I agree. "AlphaFold achieves a median score of 87.0 GDT". While this is a major advance, to me 100 GDT would be 'solved', not 87.
> To me Are you a domain expert? Because: > According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods.
but experimental methods have not solved protein folding either. AlphaFold has'nt solved protein folding but I can't wait to see their progress for ALphaFold 3.
What would be informatively useful would be to know how much accuracy is needed on average for drug engineers, I'd say that 99% is more likely to be the minimum to make solid inferences
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#436Earlier quoted context omitted.
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 bee…
I don't think AlQuraishi really hits the mark in his critique. The mere fact that hundreds or thousands of people working on a problem for decades doesn't account for the fact that the field of machine learning has been growing extremely rapidly over the last decade, the compute power available has grown exponentially, and the people working on the problem simply weren't looking at the problem in the way that the dee…
> and the people working on the problem simply weren't looking at the problem in the way that the deepmind people were looking at it.
>The approaches are that different.
I'm not sure if that analogy applies here. DeepMind wasn't the first group tackling structure prediction with machine learning. Their success lies in the innovations that they implemented (predicting interresidue distances as opposed to contacts, for example).
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#437At Sun back in the day our workstations tended to have fairly promiscuous login settings, so one of my coworkers took the liberty to launch folding@home on every machine in the org. Listing running processes one day, I saw this thing pegging my CPU; asked around and others had it too. A virus!?! Then he fessed up. Kinda miffed at first but ultimately really cool, so we let the thing keep running. That was my introduc…
I ran Folding@Home at Google on hundreds of thousands of fast Xeon cores for over a year. I concluded at the end that unbiased MD simulations are not an effective use of computer time.
Out of curiosity, why not?
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#438Earlier quoted context omitted.
It's because for many researchers ML is just to take a standard keras or scikitlearn model shove their data in and get some table or number out, and see if that solves their problem. If that's your only ML experience then I suppose this is how sceptical you'd be of ML in general. It looks like DeepMind invented a completely new method for this round that's not just an extension of their previous work, showing how muc…
"It looks like DeepMind invented a completely new method for this round that's not just an extension of their previous work, showing how much you can gain if you don't shoebox yourself into just trying to improve existing methods. That all the scientists were highly skeptical about the scope of ML (and these are computer scientists to begin with mind you) just shows how little they knew of what they did know of what…
We don't know too much about the exact model they made but it looks sufficiently generalizable to be able to give a candidate protein structure for any given sequence. It doesn't automatically cure cancer and inject the drug but that by itself is an amazing tool that if available to everyone will revolutionize biology experimentation.
I will definitely blame the protein structure field in multiple levels though. It was always frustrating to me to open up Nature or Science and see it filled with papers about structure - like they are innovating so much that half of the top science magazines every week have papers in that field, yet it's not going anywhere? Or is it simply just a bunch of professors tooting their own horns about ostensible progress in a field that's archaic by decades if not years? The overall protein structure field internalised some dogmas in self defeating ways to everyone's detriment and finally events like this (and Cryo em, maybe) will jolt them out or make them fully irrelevant so we can move on. it's only doubly ironic that this came from a team in a company with minimal academic ties showing how toxic that entire system is. I only feel pity for the graduate students still trying to crystallize proteins in this day and age.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#439Earlier quoted context omitted.
From what I can gather, Google bought Deepmind for 500 million USD in 2014, they have outstanding debt to its parent company as of 2019 of 1.3 billion USD. And they had income around 100 million in 2019 but it's all against Google, so looks like a 2 billion +/- 0.5 operation so far, and who knows if they pay for compute. Other articles place the runrate at 500 million per year in 2019. Which means 500 million * 6 yea…
That's the cost of running DeepMind as a whole, right? Which includes all the other stuff they've worked on, like games.
Yeah, as far as I can tell, that's the whole lot of it.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#440Earlier 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.
I think there's some merit to the idea that huge corporate monopolies have the resources to accomplish undertakings that smaller companies cannot. But it's often a what-if, because we don't know what the alternative might have been.
Big companies can suck up all the air in the room by monopolizing talent and making it harder for startups to pay the kinds of salaries needed for top tier AI research. Xerox PARC came up with all kinds of groundbreaking inventions that were never commercialized (by them). For every invention that comes out of a big company, it's worth thinking about whether it might have actually come out faster if it was borne of competition instead of a side project. Or in the grand scheme of things, if corporate taxes were higher and the money was given to a university research lab.
I think the best results may come from the middle ground. Smaller/medium companies are so worried about staying afloat or hitting their quarterly earnings that they have trouble making long term investments. Large companies are diverse and profitable enough that they can afford to blow money on things that might not pan out, but they don't have the same drive -- and in fact have some pressure to avoid being "too" innovative because it could cannibalize their existing products.