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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

#121

Post by ClumsyPilot »

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.

> 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

Re: Accelerating scientific breakthroughs with an AI co-scientist

#122

Post by ClumsyPilot »

kridsdale1 wrote: post #86
petra wrote: post #76

Earlier quoted context omitted.

So pharmaceutical research is largely an engineering problem, of running experiments and synthesizing molecules as fast, cheap and accurate as possible ?

It also seems to be a financial problem of getting VC funds to run trials to appease regulators. Even if you’ve already seen results in a lab or other country.

We could have an alternative system where VC don’t need to appease regulators but must place X billion in escrow for compensation of any harm the medicine does to customers.

Regulator is not only there to protect the public, it also protects VC from responsibility

Re: Accelerating scientific breakthroughs with an AI co-scientist

#123

Post by ClumsyPilot »

baq wrote: post #85
rtkwe wrote: post #54

Earlier quoted context omitted.

Suggesting "maybe try this known inhibitor in other cell lines" isn't exactly novel information though. It'd be more impressive and useful if it hadn't had any published information about working as a cancer inhibitor before. People are blasé about it because it's not really beating the allegations that it's just a very fancy parrot when the highlight of it's achievements is to say try this known inhibitor with these…

A couple years ago even suggesting that a computer could propose anything at all was sci-fi. Today a computer read the whole internet, suggested a place to look at and experiments to perform and… ‘not impressive enough’. Oof.

> Today a computer read the whole internet, suggested a place to look at

Or imagine this one - computer maps the whole world, suggests a route how to get to any destination?!

You just described a basic search engine.

LLM is kind of a search engine for language

Re: Accelerating scientific breakthroughs with an AI co-scientist

#124

Post by captainclam »

quinnjh wrote: post #26

The market seems excited to charge in whatever direction the weathervane has last been pointing, regardless of the real outcomes of running in that direction. Hopefully I’m wrong, but it reminds me very much of this study (I’ll quote a paraphrase) “A groundbreaking new study of over 1,000 scientists at a major U.S. materials science firm reveals a disturbing paradox: When paired with AI systems, top researchers becom…

Definitely interesting, but I'm not so sure that such a study can yet make strong claims about AI-based work in general.

These are scientists that have cultivated a particular workflow/work habits over years, even decades. To a significant extent, I'm sure their workflow is shaped by what they find fulfilling.

That they report less fulfillment when tasked with working under a new methodology, especially one that they feel little to no mastery over, is not terribly surprising.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#125

Post by Teever »

shpongled wrote: post #67
Teever wrote: post #62

Earlier quoted context omitted.

Any idea why they're they so expensive?

There is a big range in both automation capabilities and prices. We have a couple automation systems that are semi-custom - the robot can handle operation of highly specific, non-standard instruments that 99.9% of labs aren't running. Systems have to handle very accurate pipetting of small volumes (microliters), moving plates to different stations, heating, shaking, tracking barcodes, dispensing and racking fresh pip…

What are your thoughts on cheaper hardware like the stuff from Opentrons[0]?

I've been interested in this kind of stuff watching it from afar and now I may need to buy / build a machine that does this kind of stuff for work.

[0] https://opentrons.com/

Re: Accelerating scientific breakthroughs with an AI co-scientist

#126

Post by ClumsyPilot »

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 could do _one_ thing a horse could do better: Pull stuff on a straight surface

I would say the doubters were right, and the results are terrible.

We redesigned the world to suit the car, instead of fixing its shortcomings.

Navigating a car centric neighbourhood on foot is anywhere between depressing and dangerous.

I hope the same does not happen with AI. But I expect it will. Maybe in your daily life AI will create legal contracts there are thousands of pages long And you will need AI of your own to summarise them and process them.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#127

Post by warkdarrior »

colingauvin wrote: post #49
stanford_labrat wrote: post #33

So I'm a biomedical scientist (in training I suppose...I'm in my 3rd year of a Genetics PhD) and I have seen this trend a couple of times now where AI developers tout that AI will accelerate biomedical discovery through a very specific argument that AI will be smarter and generate better hypotheses than humans. For example in this Google essay they make the claim that CRISPR was a transdisciplinary endeavor, "which c…

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.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#128

Post by nyrikki »

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.

Depends on what you call "overhype".

Wishful mnemonics in the field was called out by Drew McDermott in the mid 1970's and it is still a problem today.

https://www.inf.ed.ac.uk/teaching/courses/irm/mcdermott.pdf

And:

> As a field, I believe that we tend to suffer from what might be called serial silver bulletism, defined as follows: the tendency to believe in a silver bullet for AI, coupled with the belief that previous beliefs about silver bullets were hopelessly naive.

(H. J. Levesque. On our best behaviour. Artificial Intelligence, 212:27–35, 2014.)

Re: Accelerating scientific breakthroughs with an AI co-scientist

#129

Post by warbaker »

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…

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...

Re: Accelerating scientific breakthroughs with an AI co-scientist

#130

Post by knowaveragejoe »

not_kurt_godel wrote: post #4
mnky9800n wrote: post #3

Tbh I don’t see why I would use this. I don’t need an ai to connect across ideas or come up with new hypothesis. I need it to write lots of data pipeline code to take data that is organized by project, each in a unique way, each with its own set of multimodal data plus metadata all stored in long form documents with no regular formatting, and normalize it all into a giant database. I need it to write and test a data…

Agreed - AI that could take care of this sort of cross-system complexity and automation in a reliable way would be actually useful. Unfortunately I've yet to use an AI that can reliably handle even moderately complex text parsing in a single file more easily than if I'd just done it myself from the start.

This reminds me of a paper: "The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators"

https://arxiv.org/abs/2407.11004

In essence, LLMs are quite good at writing the code to properly parse large amounts of unstructured text, rather than what a lot of people seem to be doing which is just shoveling data into an LLM's API and asking for transformations back.

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