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Accelerating scientific breakthroughs with an AI co-scientist

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Re: Accelerating scientific breakthroughs with an AI co-scientist

#131

Post by staunton »

theptip wrote: post #120
semi-extrinsic wrote: post #65

Earlier quoted context omitted.

In some sense, AI should be the most capable at doing this within math. Literally the entire domain in its entirety can be tokenized. There are no experiments required to verify anything, just theorem-lemma-proof ad nauseam. Doing this like in this test, it's very tricky to rule out the hypothesis that the AI is just combining statements from the Discussion / Future Outlook sections of some previous work in the field…

Math seems to me like the hardest thing for LLMs to do. It requires going deep with high IQ symbol manipulation. The case for LLMs is currently where new discoveries can be made from interpolation or perhaps extrapolation between existing data points in a broad corpus which is challenging for humans to absorb.

Alternatively, human brains are just terrible at "high IQ symbol manipulation" and that's a much easier cognitive task to automate than, say, "surviving as a stray cat".

Re: Accelerating scientific breakthroughs with an AI co-scientist

#132

Post by Bloating »

dekhn wrote: post #80
crypto420 wrote: post #11

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). Subse…

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…

https://www.youtube.com/watch?v=shFUDPqVmTg

Re: Accelerating scientific breakthroughs with an AI co-scientist

#133

Post by colingauvin »

warkdarrior wrote: post #127
colingauvin wrote: post #49

Earlier quoted context omitted.

It's pretty painful watching CS try to turn biology into an engineering problem. It's generally very easy to marginally move the needle in drug discovery. It's very hard to move the needle enough to justify the cost. What is challenging is culling ideas, and having enough SNR in your readouts to really trust them.

> It's generally very easy to marginally move the needle in drug discovery. It's very hard to move the needle enough to justify the cost. Maybe this kind of AI-based exploration would lower the costs. The more something is automated, the cheaper it should be to test many concepts in parallel.

A med chemist can sit down with a known drug, and generate 50 analogs in LiveDesign in an afternoon. One of those analogs may have less CYP inhibition, or better blood brain barrier penetration, or slightly higher potency or something. Or maybe they use an enumeration method and generate 50k analogs in one afternoon.

But no one is going to bring it to market because it costs millions and millions to synthesize, get through PK, ADMET, mouse, rat and dog tox, clinicals, etc. And the FDA won't approve marginal drugs, they need to be significantly better than the SoC (with some exceptions).

Point is, coming up with new ideas is cheap, easy, and doesn't need help. Synthesizing and testing is expensive and difficult.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#135

Post by dr_kretyn »

Interesting set of comments. Personally - fantastic! It's a co-scientist and not a "scientist". There's enormous value in reviewing work and ranking "what" might provide some interesting output. A lot of ideas are not even considered because they aren't "common" because their components are expensive. If there's a "reasonable expectation" then there's a lower risk of failure. I'm not a scientist "anymore" but I'd love to play with this and see what odd combinations might potentially produce.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#136

Post by elicksaur »

shpongled wrote: post #34
crypto420 wrote: post #11

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). Subse…

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’m not familiar with the subject matter, but given your description, I wouldn’t really be impressed by anyone suggesting it. It just sounds like a very plausible “What if” alternative.

On the level of suggesting suitable alternative ingredients in fruit salad.

We should really stop insulting the intelligence of people to sell AI.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#137

Post by dekhn »

warbaker wrote: post #129
dekhn wrote: post #80

Earlier quoted context omitted.

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…

The article you linked is not an example of this happening. Google open-sourced the chip design method, and uses it in production for TPU and other chips. https://github.com/google-research/circuit_training https://deepmind.google/discover/blog/how-alphachip-transfor...

It's an ongoing debacle with multiple people making extremely good arguments that Google overstated the results.

Yes, I know it's in TPUs and I said exactly that.

You simply can't take Google press at face value.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#138

Post by ANewFormation »

fhd2 wrote: post #57
xbmcuser wrote: post #36

Earlier quoted context omitted.

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 kno…

It's a matter of perspective and expectations. The automobile was a useful invention. I don't know if back then there was a lot of hype around how it can do anything a horse can do, but better. People might have complained about how it can't come to you when called, can't traverse stairs, or whatever. It could do _one_ thing a horse could do better: Pull stuff on a straight surface. Doing just one thing better is evi…

It's not just about doing something better but about the balance between the pros and the cons. The problem with LLMs are hallucinations. If cars just somehow made you drive the wrong way with the frequency that LLMs send one down the wrong path with compelling sounding nonsense, then I suspect we'd still be riding horses nowadays.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#139

Post by webmaven »

ClumsyPilot wrote: post #121
ein0p wrote: post #106

Earlier quoted context omitted.

It's hardly Google's problem that nobody else has a million cores, wouldn't you agree? Should they not publish the result at all if it's using more than a handful of cores so that anyone in academia can reproduce it? That'd be rather limiting.

> Google's problem that nobody else has a million cores, wouldn't you agree On the contrary - their advantage. They know it and they can make outlandish claims that no one will disprove

For a while, anyway.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#140

Post by webmaven »

coliveira wrote: post #46
eamag wrote: post #22

Earlier quoted context omitted.

I think you're just not the target audience. If AI can come up with some good ideas and then split it into tasks some of them an undergrad can do - it can speed up the global research speed by involving more people in useful science

In science, having ideas is not the limiting factor. They're just automating the wrong thing. I want to have ideas and ask the machine to test for me, not the other way around.

If I understand what's been published about this, it isn't just ideation, but also critiquing and ranking them, to select the few most worth pursuing.

Choosing a hypothesis to test is actually a hard problem, and one that a lot of humans do poorly, with significant impact on their subsequent career. From what I have seen as an outsider to academia, many of the people who choose good hypotheses for their dissertation describe it as having been lucky.

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