alt Hacker NewsI'm not sure if people here even read the entirety of the article. From the article:
> We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments.
> Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subsequent experiments validated these proposals, confirming that the suggested drugs inhibit tumor viability at clinically relevant concentrations in multiple AML cell lines.
and,
> For this test, expert researchers instructed the AI co-scientist to explore a topic that had already been subject to novel discovery in their group, but had not yet been revealed in the public domain, namely, to explain how capsid-forming phage-inducible chromosomal islands (cf-PICIs) exist across multiple bacterial species. The AI co-scientist system independently proposed that cf-PICIs interact with diverse phage tails to expand their host range. This in silico discovery, which had been experimentally validated in the original novel laboratory experiments performed prior to use of the AI co-scientist system, are described in co-timed manuscripts (1, 2) with our collaborators at the Fleming Initiative and Imperial College London. This illustrates the value of the AI co-scientist system as an assistive technology, as it was able to leverage decades of research comprising all prior open access literature on this topic.
The model was able to come up with new scientific hypotheses that were tested to be correct in the lab, which is quite significant.
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That a UPR inhibitor would inhibit viability of AML cell lines is not exactly a novel scientific hypothesis. They took a previously published inhibitor known to be active in other cell lines and tried it in a new one. It's a cool, undergrad-level experiment. I would be impressed if a sophomore in high school proposed it, but not a sophomore in college.
I read the cf-PICI paper (abstract) and the hypothesis from the AI co-scientist. While the mechanism from the actual paper is pretty cool (if I'm understanding it correctly), I'm not particularly impressed with the hypothesis from the co-scientist.
It's quite a natural next step to take to consider the tails and binding partners to them, so much so that it's probably what I would have done and I have a background of about 20 minutes in this particular area. If the co-scientist had hypothesised the novel mechanism to start with, then I would be impressed at the intelligence of it. I would bet that there were enough hints towards these next steps in the discussion sections of the referenced papers anyway.
What's a bit suspicious is in the Supplementary Information, around where the hypothesis is laid out, it says "In addition, our own preliminary data indicate that cf-PICI capsids can indeed interact with tails from multiple phage types, providing further impetus for this research direction." (Page 35). A bit weird that it uses "our own preliminary data".
This is one thing I've been wondering about AI: will its broad training enable it to uncover previously covered connections between areas the way multi-disciplinary people tend to, or will it still miss them because it's still limited to its training corpus and can't really infer.
If it ends up being more the case that AI can help us discover new stuff, that's very optimistic.
Similar stuff is being done for material sciences where AI suggest different combinations to find different properties. So when people say AI(machine learning, LLM) are just for show I am a bit shocked as AI's today have accelerated discoveries in many different fields of science and this is just the start. Anna archive probably will play a huge role in this as no human or even a group of humans will have all the knowledge of so many fields that an Ai will have.
https://www.independent.co.uk/news/science/super-diamond-b26...
It's cool, no doubt. But keep in mind this is 20 years late:
As a prototype for a "robot scientist", Adam is able to perform independent
experiments to test hypotheses and interpret findings without human guidance,
removing some of the drudgery of laboratory experimentation.[11][12] Adam is
capable of:
* hypothesizing to explain observations
* devising experiments to test these hypotheses
* physically running the experiments using laboratory robotics
* interpreting the results from the experiments
* repeating the cycle as required[10][13][14][15][16]
While researching yeast-based functional genomics, Adam became the first
machine in history to have discovered new scientific knowledge independently of
its human creators.[5][17][18]
I also think people underestimate how much benefit a current LLM already has to researchers.
A lot of them have to do things on computers which has nothing to do with their expertise. Like coding a small tool for working their data, small tools crunching results, formatting text data, searching and finding the right materials.
A LLM which helps a scientist to code something in an hour instead of a week, makes this research A LOT faster.
And we know from another paper, that we have now so much data, you need to use systems to find the right information for you. The study estimated how much additionanl critical information a research paper missed.
Does this qualify as an answer to Dwarkesh's question?[1][2]
[1]https://marginalrevolution.com/marginalrevolution/2025/02/dw... [2]https://x.com/dwarkesh_sp/status/1888164523984470055
I don't know his @ but I'm sure he is on here somewhere
> in silico discovery
Oh I don’t like that. I don’t like that at all.
I expect it's going to be reasonably useful with the "stamp collecting" part of science and not so much with the rest.
Not that I don't think there's a lot of potential in this approach, but the leukemia example seemed at least poorly-worded, "the suggested drugs inhibit tumor viability" reads oddly given that blood cancers don't form tumors?
So, I've been reading Google research papers for decades now and also worked there for a decade and wrote a few papers of my own.
When google publishes papers, they tend to juice the results significance (google is not the only group that does this, but they are pretty egregious). You need to be skilled in the field of the paper to be able to pare away the exceptional claims. A really good example is https://spectrum.ieee.org/chip-design-controversy while I think Google did some interesting work there and it's true they included some of the results in their chip designs, their comparison claims are definitely over-hyped and they did not react well when they got called out on it.