Hi! I work directly on these teams as a model builder and have talked to my colleagues are the other labs well.

All our orgs have openings and if you also could consider working for organizations such as the UK AISI team and other independent organizations that are assessing these models. It's a critical field and there is a need for motivated folks.

My worldview considers the existance of lobbies and human neglect. It seems more nuanced than "believe the management" rethoric.

Your angle is "there is no oversight, why are you asking for it?". It's the same overall angle as the guy who was spreading covid rumours here in this thread.

There are efforts at estabilishing it though. And it's hard and expensive for wet labs, but it could be much simpler for things like simulating biological pathways.

One could also see your response as "other nations are developing threats, we should race", which I personally think is misguided.

Instead of these petty armchair discussions, we should instead focus on being more serious about it.

The GPT5 system card is 59 pages. Pages 5 to 56 address safety in various forms.

https://cdn.openai.com/gpt-5-system-card.pdf

Rational discourse? Not working for a marketing team? Realism?

Not believing everything you read on the internet? Being jaded from constant fluff and lies? Not having gell-mann amnesia?

I get your sentiment of "why you gotta bring down this good thing" but the answer to your actual question is battle scars from the constant barrage of hostile lies and whitewashing we are subject to. It's kind of absurd (and mildly irresponsible) to think "THIS time will be the time things only go well and nobody uses the new thing for something I don't want".

Pessimism?

Performative cynicism?

Covid is irrelevant to the discussion I opened. You're trying to steer the discussion into a place that will lead us nowhere because there's too many artificial polemics around it.

The only thing to be said about it that resonates with what I'm concerned with is that anyone that is good in the head wants better international oversight on potential bioweapons development.

He says and quotes Wikipedia.

Still irrelevant to what I brought up.

Someone has to do it. Big pharma has a lot of money and if AI can reduce their costs in human resources, they will be willing to put some of their profits aside to further the research in AI space.

Money wells are drying up across the trch Industry and ai companies will have to look for funds from adjacent industries like biotech and medicine.

I think it's important that people know this. Despite what the other AI companies claim or put out as occasional PR, they have absolutely no real interest (through internal work or funding external researchers) in using AI to benefit science and humanity as a whole. They just want their digital god. As such, there is simply not enough funding for AI research with scientific applications. Consequently, many people in machine learning are not working in scientific applications, even though they really want to.

It is awesome.

But what I’ll say is, ideally they would demonstrate whether this model can perform any better than simple linear models for predicting gene expression interactions.

We’ve seen that some of the single cell “foundation” models aren’t actually the best at in silico perturbation modeling. Simple linear models can outperform them.

So this article makes me wonder: if we take this dataset they’ve acquired, and run very standard single cell RNA seq analyses (including pathway analyses), would this published association pop out?

My guess is that yes… it would. You’d just need the right scientist, right computational biologist, and right question.

However, I don’t say this to discredit the work in TFA. We are still in the early days of scSeq foundation models, and I am excited about their potential.

Cellular level computational simulation existed a very long time and it's more impressive by the day because of large collections of experimental datasets available.

However to infer or predict celular acitivities you need a ton of domain knowledge and experties about particular cell types, biological processes and specific environments. Typically the successful ones are human curated and validated (e.g large interaction networks based on literature).

In cancer it's even more unpredictable because of the lack of good (experimental) models, in-vivo or in-vitro, representing what actually happens the clinically and biologically underneath. Given the single cell resolution, its uncertainty will also amplify because of how heterogeneous inter- and intra- tumours are.

Having said that, a foundation model is definitely the future for futher development. But with all of these things, the bigger the model, the harder the validation process.

Hell yeah

Their research is pivoting to STDs

Both is equally important, just in another dimension.

OTOH... time for a wall-e rewatch

Well it's important, because the particular new lead for drug targeting is not super valuable, they are a dime a dozen, easier to find than a startup idea. Actually driving a successful drug development program is an entirely different matter that can only be established with $10-$100M of early exploration, with a successful drug costing much more to get to market.

It could also be that particular prioritization method that uses Gemma is useful in its own, but we won't know that unless it is somehow benchmarked against the many alternatives that have been used up until now. And in other benchmark settings, these cell sentence methods have not been that impressive.

Let's go !!!

ah ok, my bad. My worst post of the week :-)

Look through any of the single cell atlas papers that are published all the time and you'l see slightly different methods, called machine learning in the past, AI now, used to achieve exactly the same thing. Every form of AI has been continuously produced new results like this throughout the history of genomics.

The reason you are reading about this is because 1) Gemma has a massive massive PR budget, whereas scientists have zero PR budget, 2) it's coming from Google so it's not the traditional scientists and you know Google and when they publish something new, it's makes it to HN.

I don't see any reason to be excited by the result here. It is a workaday result, using a new tool. I'm usually excited by new tools, but given the source, it's going to take a lot of digging to get past the PR spin, so that extra needless work seems exhausting.

Facebooks's proteins language modelling, followed by Google's AlphaFold, did have really new and interesting methods! But single cell RNA models have been underwhelming because there's no easy "here is the ground truth" out there like there is for proteins. We won't know if this is a significant advancement until years of study show which of the many scRNA foundation models make better predictions. And there was a paper about a year ago that poured a ton of cold water on the whole field: replacing models with random weights barely changed the results on the very limited evaluation sets that we have.

Every AI post is either filled with negative comments stating that AI can just regurgitate stuff. This states otherwise and the comment I replied to tries to downplay that.

This just in "excel helped discover a potential cancer cure using pitvottables"

Other potential cancer treatment methods that 2.5pro - a different model than is referenced in the article - has confirmed as potentially viable when prompted by an amateur cancer researcher:

- EPS3.9: Polysaccharide (deep sea bacterium sugar, fermentable, induces IFN-1) causes Pyroptosis causes IFN-1 causes Epitope Spreading (which is an amplifying effect) causes anti-cancer response.

- CPMV; Cow-Pea Mosaic Virus (is a plant virus that doesn't infect humans but causes an (IFN-1 (IFN-alpha and a lot of IFN-beta)) anti-cancer response in humans. Cow Pea consumption probably used to be even more prevalent in humans before modern agriculture; cow peas may have been treating cancer in humans for thousands of years at least.)

I emailed these potential new treatments to various researchers with a fair disclaimer; but IDK whether anything has been invested in developing a treatment derived from or informed by knowledge of the relevant pathways affected by EPS3.9 or CPMV.

There are RNA and mRNA cancer vaccines in development.

Without a capsid, RNA is destroyed before arrival. So RNA vaccines are usually administered intramuscularly.

AFAIU, as a general bioengineering platform, CPMV Cow-Pea Mosaic Virus could also be used like a capsid to package for example an RNA cancer vaccine.

AFAIU, CSC3.9 (which produces the "potent anti-cancer" EPS3.9 marine spongiibacter polysaccharide) requires deep sea pressure; but it's probably possible to bioengineer an alternative to CSC3.9 which produces EPS3.9 in conditions closer to ambient temp and pressure?

Would there be advantages to (CPMV + EPS3.9) + (CPMVprime + mRNA)? (for cancer treatment)

This has been said by enough people in the know to be considered true by now. Not just from oAI, but also Anthropic and Meta have said this before. You train the best of the best, and then use it to distill/curate/inform the next training run, on something that makes sense to serve at scale. That's how you get from GPT4 / o3 prices (80$/60$ /Mtok) to gpt5 prices (10$ /Mtok) to gpt5-mini (2$ /Mtok).

Then you use a combination of the best models to amplify your training set, and enhance it for the next iteration. And then repeat the process at gen n+1.

When they do, someone like OpenAI comes along and runs the VC enshittification playbook on it.

It doesn't compensate for all the other bad stuff google's doing. Long gone are the times were I looked up to google as a champion of good technologies. When I read this I'm just sad such en important step of humankind's technological future is in the hand of evil(-ishbat least) companies