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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

#131

Post by Ameo »

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…

> The model source code and weights will also be provided upon final publication.

Page 59 from the preprint[1]

Seems like they do intend to publish the weights actually

[1]: https://storage.googleapis.com/deepmind-media/papers/alphage...

Re: AlphaGenome: AI for better understanding the genome

#132

Post by LarsDu88 »

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…

The predecessor to this model Enformer, which was developed in collaboration with Calico had a weight release and a source release.

The precedent I'm going with is specifically in the gene regulatory realm.

Furthermore, a weight release would allow others to finetune the model on different datasets and/or organisms.

Re: AlphaGenome: AI for better understanding the genome

#133

Post by forgotpwagain »

jebarker wrote: post #50

I don't think DM is the only lab doing high-impact AI applications research, but they really seem to punch above their weight in it. Why is that or is it just that they have better technical marketing for their work?

DeepMind/Google does a lot more than the other places that most HN readers would think about first (Amazon, Meta, etc). But there is a lot of excellent work with equal ambition and scale happening in pharma and biotech, that is less visible to the average HN reader. There is also excellent work happening in academic science as well (frequently as a collaboration with industry for compute). NVIDIA partners with whoever they can to get you committed to their tech stack.

For instance, Evo2 by the Arc Institute is a DNA Foundation Model that can do some really remarkable things to understand/interpret/design DNA sequences, and there are now multiple open weight models for working with biomolecules at a structural level that are equivalent to AlphaFold 3.

Re: AlphaGenome: AI for better understanding the genome

#134

Post by [deleted] »

gordonhart wrote: post #68
oceanplexian wrote: post #44

Earlier quoted context omitted.

> The transition to AI started late but gemini is super competitive overall. If by competitive you mean "We spent $75 Billion dollars and now have a middle of the pack model somewhere between Anthropic and Chinese startup", that's a generous way to put it.

Gemini 2.5 Pro is excellent. Top model in public benchmarks and soundly beat the alternatives (including all Claudes and that Chinese startup’s flagship) in my company’s internal benchmarks. I’m no Google lover — in fact I’m usually a detractor due to the overall enshittification of their products — but denying that Gemini tops the pile right now is pure ignorance.

[deleted]

Re: AlphaGenome: AI for better understanding the genome

#135

Post by oceanplexian »

deepdarkforest wrote: post #48
oceanplexian wrote: post #44

Earlier quoted context omitted.

> The transition to AI started late but gemini is super competitive overall. If by competitive you mean "We spent $75 Billion dollars and now have a middle of the pack model somewhere between Anthropic and Chinese startup", that's a generous way to put it.

By competitive, i mean no.1 in LM arena overall, in webdev, in image gen, in grounding etc. Plus, leading the chatbot arena ELO. Flash is the most used model in openrouter this month as well. Gemma models are leading on device stats as well. So yes, competitive

Except coding, where it’s essentially middle of the pack. Which is the only thing that you can build objective benchmarks around. The fact that people on LM arena prefer the output has no relationship to how intelligent the model actually is.

Re: AlphaGenome: AI for better understanding the genome

#136

Post by coderatlarge »

plemer wrote: post #113
shadowgovt wrote: post #47

Earlier quoted context omitted.

It is a lot to expect of readers... It's also explicitly asked of us in this forum. https://news.ycombinator.com/newsguidelines.html . "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith."

fair point

it’s fine for a forum to try to have different expectations than the local cafe - that’s kind of like a host asking their guests to remove shoes before walking into their home. but it doesn’t really change a priori basic facts about good writing.

perhaps this is the appropriate forum to reference pg

https://paulgraham.com/writing44.html

https://paulgraham.com/essay.htm

Re: AlphaGenome: AI for better understanding the genome

#138

Post by roughly »

wrsh07 wrote: post #130
Onawa wrote: post #128

Earlier quoted context omitted.

I think that from a research/academic view of the landscape, building off a mutable API is much less preferred than building of a set of open weights. It would be even better if we had the training data, along with all code and open weights. However, I would take open weights over almost anything else in the current landscape.

If it came to light that somebody found a way to use this API in a way that is harmful to society would you be happy that Google could revoke access? Or unhappy? This is a real tradeoff of freedom vs _. I agree that I'm not always a fan of Google being the one in control, but I'm much happier that they are even releasing an API. That's not something they did for go! (Of course there was a book written so someone got…

If it came to light that somebody found a way to use this API in a way that is beneficial to society, would you be happy that Google could revoke access? Or unhappy?

Re: AlphaGenome: AI for better understanding the genome

#139

Post by liamwire »

CGMthrowaway wrote: post #12
bitpush wrote: post #9

Earlier quoted context omitted.

[flagged]

Yeah it comes off as braggy, but it’s only natural to be proud of your foresight

Natural? Sure. Deserved? Not really, not unless we’re also forthcoming in our lack of foresight and the times we plainly got it wrong.

Re: AlphaGenome: AI for better understanding the genome

#140

Post by liamwire »

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.

Really? It’s always felt to me like it was app availability — for all the efforts, the app marketplace was a fraction of a fraction of the competitions’, and much like the network effects in social media, if you can’t catch up quickly, it can be almost impossible to ever do so. Haemorrhaging billions per quarter takes a strong stomach and a long vision, one that’s likely to put any executive’s tenure at risk. Nevertheless, it interesting to think what things might’ve looked like had Microsoft persisted another decade.
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