Accelerating scientific breakthroughs with an AI co-scientist

I'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.

It seems in general we’re heading toward’s Minsky’s society of minds concept. I know OpenAI is wanting to collapse all their models into a single omni model that can do it all, but I wonder if under the hood it’d just be about routing. It’d make sense to me for agents to specialize in certain tool calls, ways of thinking, etc that as a conceptual framework/scaffolding provides a useful direction.

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.

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?