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AlphaGenome: AI for better understanding the genome

deepmind.google

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Re: AlphaGenome: AI for better understanding the genome

#161

Post by diggan »

wrsh07 wrote: post #123
LarsDu88 wrote: post #110

You know the corporate screws are coming down hard, when the model (which can be run off a single A100) doesn't get a code release or a weight release, but instead sits behind an API, and the authors say fuck it and copy-paste the entirety of the model code in pseudocode on page 31 of the white paper. Please Google/Demis/Sergei, just release the darn weights. This thing ain't gonna be curing cancer sitting behind an…

This is a strange take because this is consistent with what Google has been doing for a decade with AI. AlphaGo never had the weights released. Nor has any successor (not muzero, the StarCraft one, the protein folding alphafold, nor any other that could reasonably be claimed to be in the series afaik) You can state as a philosophical ideal that you prefer open source or open weights, but that's not something deepmind…

All of what you said makes sense from the perspective of a product manager working for a for-profit company trying to maximize profit either today or eventually.

But the submission blog post writes:

> To advance scientific research, we’re making AlphaGenome available in preview via our AlphaGenome API for non-commercial research, and planning to release the model in the future. We believe AlphaGenome can be a valuable resource for the scientific community, helping scientists better understand genome function, disease biology, and ultimately, drive new biological discoveries and the development of new treatments.

And at that point, they're painting this release as something they did in order to "advance scientific research" and because they believe "AlphaGenome can be a valuable resource".

So now they're at a cross-point, is this release actually for advancing scientific research and if so, why aren't they doing it in a way so it actually maximizes advancing scientific research, which I think is the point parent's comment.

Even the most basic principle for doing research, being able to reproduce something, goes out the window when you put it behind an API, so personally I doubt their ultimate goal here is to serve the scientific community.

Edit: Reading further comments it seems like they've at least claimed they want to do a model+weights release of this though (from the paper: "The model source code and weights will also be provided upon final publication.") so remains to be seen if they'll go through with it or not.

Re: AlphaGenome: AI for better understanding the genome

#162

Post by mnw21cam »

j7ake wrote: post #125
RivieraKid wrote: post #74

I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.

Why simulate? We can already do it experimentally

In my field, we're always wanting to see what will happen when DNA is changed in a human pancreatic beta cell. We kind of have a protocol for producing things that look like human pancreatic beta cells from human stem cells, but we're not really sure that they are really going to behave like real human pancreatic beta cells for any particular DNA change, and we have examples of cases where they definitely do not behave the same.

Re: AlphaGenome: AI for better understanding the genome

#164

Post by Kalanos »

The functional predictions related to "non-coding" variants are big here. Non-coding regions, referred to as the dark genome, produce regulatory non-coding RNA's that determine the level of gene expression in a given cell type. There are more regulatory RNA's than there are genes. Something like 75% of expression by volume is ncRNA.

Re: AlphaGenome: AI for better understanding the genome

#165

Post by wrsh07 »

diggan wrote: post #161
wrsh07 wrote: post #123

Earlier quoted context omitted.

This is a strange take because this is consistent with what Google has been doing for a decade with AI. AlphaGo never had the weights released. Nor has any successor (not muzero, the StarCraft one, the protein folding alphafold, nor any other that could reasonably be claimed to be in the series afaik) You can state as a philosophical ideal that you prefer open source or open weights, but that's not something deepmind…

All of what you said makes sense from the perspective of a product manager working for a for-profit company trying to maximize profit either today or eventually. But the submission blog post writes: > To advance scientific research, we’re making AlphaGenome available in preview via our AlphaGenome API for non-commercial research, and planning to release the model in the future. We believe AlphaGenome can be a valuabl…

To be clear: I agree that opening up model + weights makes it possible for third parties to distill or fine tune

If you look at the frenzy of activity that happened after midjourney became accessible, that was awesome for everyone. Midjourney probably got help running their model efficiently and a ton of progress was quickly made.

I'm pretty sympathetic to a company doing a windowing strategy: prepare the API as a sort of beta release timed with the announcement. Spend some time cleaning up the code for public release (at Google this means ripping out internal dependencies that aren't open source), and then release a reference inference implementation along with the weights.

That's pretty reasonable. I wanted to push back on this idea that "the reason Google isn't dropping model + weights is because the corporate screws are coming down hard"

Google isn't waiting to release the weights so that they can profit from this. It's essentially the first step in the process, and serving via API gives them valuable usage data they they might not get if/when it's open sourced

Re: AlphaGenome: AI for better understanding the genome

#166

Post by wespiser_2018 »

Kalanos wrote: post #164

The functional predictions related to "non-coding" variants are big here. Non-coding regions, referred to as the dark genome, produce regulatory non-coding RNA's that determine the level of gene expression in a given cell type. There are more regulatory RNA's than there are genes. Something like 75% of expression by volume is ncRNA.

It's possible that the "functional" aspect of non-coding RNA exists on a time scale much larger that what we can assay in a lab. The sort of "junk DNA/RNA" hypothesis: the ncRNA part of the genome is material that increases fitness during relative rare events where it's repurposed into something else.

On a millions or billions of year time frame, the organisms with the flexibility of ncRNA would have an advantage, but this is extremely hard to figure out with a "single point in time" view point.

Anyway, that was the basic lesson I took from studying non-coding RNA 10 years ago. Projects like ENCODE definitely helped, but they really just exposed transcription of elements that are noisy, without providing the evidence that any of it is actually "functional". Therefore, I'm skeptical that more of the same approach will be helpful, but I'd be pleasantly surprised if wrong.

Re: AlphaGenome: AI for better understanding the genome

#167

Post by MattRix »

diggan wrote: post #161
wrsh07 wrote: post #123

Earlier quoted context omitted.

This is a strange take because this is consistent with what Google has been doing for a decade with AI. AlphaGo never had the weights released. Nor has any successor (not muzero, the StarCraft one, the protein folding alphafold, nor any other that could reasonably be claimed to be in the series afaik) You can state as a philosophical ideal that you prefer open source or open weights, but that's not something deepmind…

All of what you said makes sense from the perspective of a product manager working for a for-profit company trying to maximize profit either today or eventually. But the submission blog post writes: > To advance scientific research, we’re making AlphaGenome available in preview via our AlphaGenome API for non-commercial research, and planning to release the model in the future. We believe AlphaGenome can be a valuabl…

I feel like this take is missing a sense of balance. You can have a goal of advancing scientific research while also still making money. You don’t have to choose one extreme end of the scale.

I’d argue that the product providing some monetary value for Google will help ensure that this team doesn’t get moved some more profitable project instead. That way they can continue improving this tool and make more tools like it in the future.

Re: AlphaGenome: AI for better understanding the genome

#168

Post by RivieraKid »

tim333 wrote: post #160
RivieraKid wrote: post #74

I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.

It's a main aim at DeepMind. I hope they succeed as it could be very useful.

Do they specifically state that it's their main aim anywhere?

Edit: Never mind, I've googled the answer.

Re: AlphaGenome: AI for better understanding the genome

#169

Post by jryb »

wrsh07 wrote: post #165
diggan wrote: post #161

Earlier quoted context omitted.

All of what you said makes sense from the perspective of a product manager working for a for-profit company trying to maximize profit either today or eventually. But the submission blog post writes: > To advance scientific research, we’re making AlphaGenome available in preview via our AlphaGenome API for non-commercial research, and planning to release the model in the future. We believe AlphaGenome can be a valuabl…

To be clear: I agree that opening up model + weights makes it possible for third parties to distill or fine tune If you look at the frenzy of activity that happened after midjourney became accessible, that was awesome for everyone. Midjourney probably got help running their model efficiently and a ton of progress was quickly made. I'm pretty sympathetic to a company doing a windowing strategy: prepare the API as a so…

I take most of your points except the last one. The feedback would come in the form of publications, definitely from academia and to a lesser degree industry (admittedly a slow iteration time). Also just public discourse - there was no dearth of very specific, highly technical feedback for any of the releases of alphafold on twitter, for example.

But I can’t use this at all at work (a pharma company) because it would leak confidential information. So anything they learn from usage data is systematically excluding (the vast majority of?) people working on therapeutics.

Re: AlphaGenome: AI for better understanding the genome

#170

Post by modeless »

com2kid wrote: post #118
modeless wrote: post #88

Earlier quoted context omitted.

> the Windows CE kernel team was less than a dozen people! It showed CE was a dog and probably a big part of the reason Windows Phone failed. Migrating off of it was a huge distraction and prevented the app platform from being good for a long time. I was at Microsoft and worked on Silverlight for a bit back then.

Windows phone 7's kernel was amazing. It was a complete rewrite from the old kernel and had incredible performance, minimal resource usage, and an amazing power profile. IMHO the reason for Microsoft's failed phone venture was moving onto the windows kernel and 2xing system requirements.

Strongly disagree. Optimizing for resource usage above all else was the wrong thing. The developer experience was beyond awful. Terrible tooling. And poor resource isolation meant that it didn't recover from errors during development, needing constant reboots, like the bad old days of Windows 3 or classic Mac OS. There was no chance of building a decent app platform for third party native code on top of it because of that.

Phone hardware was exploding in capability at the time and the right thing was to lean into that and offer the same developer experience as on desktops with the same OS kernel, like Apple did from the beginning with Darwin and Android with Linux. Microsoft only realized too late.

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