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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

#111

Post by dekhn »

ein0p wrote: post #106
dekhn wrote: post #100

Earlier quoted context omitted.

yes, but google has a long history of being egregious, with the additional detail that their work is often irreproducible for technical reasons (rather than being irreproducible for missing methods). For example, we published an excellent paper but nobody could reproduce it because at the time, nobody else had a million spare cores to run MD simulations of proteins.

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.

Well, a goal of most science is to be reproducible, and it couldn't be reproduced, merely for technical reasons (and so we shared as much data from the runs as possible so people could verify our results). This sort of thing comes up when CERN is the only place that can run an experiment and nobody can verify it.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#112

Post by throwawaymaths »

pertymcpert wrote: post #102
coherentpony wrote: post #88

Earlier quoted context omitted.

It bothers me that the word 'hallucinate' is used to describe when the output of a machine learning model is wrong. In other fields, when models are wrong, the discussion is around 'errors'. How large the errors are, their structural nature, possible bounds, and so forth. But when it's AI it's a 'hallucination'. Almost as if the thing is feeling a bit poorly and just needs to rest and take some fever-reducer before b…

I think hallucinate is a good term because when an AI completely makes up facts or APIs etc it doesn't do so as a minor mistake of an otherwise correct reasoning step.

its more like conspiracy theory. when you're picking a token youre kinda like putting a gun to the LLM's head and demanding, "what you got next?"

Re: Accelerating scientific breakthroughs with an AI co-scientist

#113

Post by shpongled »

dekhn wrote: post #103
kridsdale1 wrote: post #87

Earlier quoted context omitted.

That’s similar to how Google won in distributed systems. They used cheap PCs in shipping containers when everyone else was buying huge expensive SUN etc servers.

yes, and that's the reason I went to work at google: to get access to their distributed systems and use ML to scale up biology. I never was able to join Google Research and do the work I wanted (but DeepMind went ahead and solved protein structure prediction, so, the job got done anyway).

They really didn't solve it. AF works great for proteins that have a homologous protein with a crystal structure. It is absolutely useless for proteins with no published structure to use as a template - e.g. many of the undrugged cancer targets in existence.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#114

Post by dekhn »

shpongled wrote: post #113
dekhn wrote: post #103

Earlier quoted context omitted.

yes, and that's the reason I went to work at google: to get access to their distributed systems and use ML to scale up biology. I never was able to join Google Research and do the work I wanted (but DeepMind went ahead and solved protein structure prediction, so, the job got done anyway).

They really didn't solve it. AF works great for proteins that have a homologous protein with a crystal structure. It is absolutely useless for proteins with no published structure to use as a template - e.g. many of the undrugged cancer targets in existence.

that's not true (I work in the field) but it's not an interesting argument to have.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#115

Post by rlyshw »

preston4tw wrote: post #17
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…

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.

This is kinda getting at a core question of epistemology. I’ve been working on an epistemological engine by which LLMs would interact with a large knowledge graph and be able to identify “gaps” or infer new discoveries. Crucial to this workflow is a method for feedback of real world data. The engine could produce endless hypotheses but they’re just noise without some real world validation metric.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#116

Post by shpongled »

shpongled wrote: post #113
dekhn wrote: post #103

Earlier quoted context omitted.

yes, and that's the reason I went to work at google: to get access to their distributed systems and use ML to scale up biology. I never was able to join Google Research and do the work I wanted (but DeepMind went ahead and solved protein structure prediction, so, the job got done anyway).

They really didn't solve it. AF works great for proteins that have a homologous protein with a crystal structure. It is absolutely useless for proteins with no published structure to use as a template - e.g. many of the undrugged cancer targets in existence.

@dekhn it is true (I also work in the field. I'm a software engineer who got a wet-lab PhD in biochemistry and work at a biotech doing oncology drug discovery)

Re: Accelerating scientific breakthroughs with an AI co-scientist

#117

Post by hall0ween »

BeetleB wrote: post #101
hall0ween wrote: post #92

Earlier quoted context omitted.

I would say anecdotal. This hasn't been my case across four universities and ten years.

The actual papers don't overhype. But the university PR's regarding those papers? They can really overhype the results. And of course, the media then takes it up an extra order of magnitude.

Fair point!

Re: Accelerating scientific breakthroughs with an AI co-scientist

#118

Post by ein0p »

dekhn wrote: post #111
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.

Well, a goal of most science is to be reproducible, and it couldn't be reproduced, merely for technical reasons (and so we shared as much data from the runs as possible so people could verify our results). This sort of thing comes up when CERN is the only place that can run an experiment and nobody can verify it.

It is probably reproducible, if you have the requisite million cores. That isn't even difficult today - a million cores is about 100 GPUs.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#119

Post by the8472 »

howenterprisey wrote: post #75
Nevermark wrote: post #55

Earlier quoted context omitted.

Especially when you consider the artificial impressive high school sophomore is capable of having impressive high school sophomore ideas across and between an incredibly broad spectrum of domains. And that their generation of impressive high school sophomore ideas is faster, more reliable, communicated better, and can continue 24/7 (given matching collaboration), relative to their bio high school sophomore counterpar…

I don't need high school level ideas, though. If people do, that's good for them, but I haven't met any. And if the quality of the ideas is going to improve in future years, that's good too, but also not demonstrated here.

> And if the quality of the ideas is going to improve in future years, that's good too, but also not demonstrated here.

I don't quite understand the argument here. The future hasn't happened yet. What does it mean to demonstrate the future developments now?

Re: Accelerating scientific breakthroughs with an AI co-scientist

#120

Post by theptip »

semi-extrinsic wrote: post #65
preston4tw wrote: post #17

Earlier quoted context omitted.

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.

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.
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