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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

#71

Post by dekhn »

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.

(to be Shpongled is to be kippered, mashed, smashed, destroyed...completely geschtonkenflopped)

Re: Accelerating scientific breakthroughs with an AI co-scientist

#72

Post by JW_00000 »

eamag wrote: post #51
coliveira wrote: post #46

Earlier quoted context omitted.

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.

The difference is the complexity of ideas. There are straightforward ideas anyone can test and improve, and there are ideas where only PhDs in CERN can test

I don't think that's really right. E.g. what makes finding the Higgs boson difficult is that you need to build a really large collider, not coming up with the idea, which could be done 50 years earlier. Admittedly the Higgs boson is still a "complex idea", but the bottleneck still was the actual testing.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#73

Post by pradn »

yodon wrote: post #30
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…

As a dev, I have the same experience. AI chat is a massive productivity enhancer, but, when coding via prompts, I'm not able to hit the super satisfying developer flow state that I get into via normal coding. Copilot is less of a productivity boost, but also less of a flow state blocker.

There is some queasy feeling of fake-ness when auto-completing so much code. It feels like you're doing something wrong. But these are all based on my experience coding for half my life. AI-native devs will probably feel differently.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#74

Post by dekhn »

Teever wrote: post #62
shpongled wrote: post #56

Earlier quoted context omitted.

They already exist, and we use them. They are not cheap though!

Any idea why they're they so expensive?

I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built (https://www.transcriptic.com/). So my scope could be automated by the workcell automation components (robot arms, motors, conveyors, etc).

When I demo'd my scope (which is similar to a 3d printer, using low-cost steppers and other hobbyist-grade components) the CEO gave me feedback which was very educational. They couldn't build a system that used my style of components because a failure due to a component would bring the whole system down and require an expensive service call (along with expensive downtime for the user). Instead, their mech engineer would select extremely high quality components that had a very low probability of failure to minimize service calls and other expensive outages.

Unfortunately, the cost curve for reliability not pretty, to reduce mechanical failures to close to zero costs close to infinity dollars.

One of the reasons Google's book scanning was so scalable was their choice to build fairly simple, cheap, easy to maintain machines, and then build a lot of them, and train the scanning individuals to work with those machines quirks. Just like their clusters, they tolerate a much higher failure rate and build all sorts of engineering solutions where other groups would just buy 1 expensive device with a service contract.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#75

Post by howenterprisey »

Nevermark wrote: post #55
klipt wrote: post #37

Earlier quoted context omitted.

Only two years since chatGPT was released and AI at the level of "impressive high school sophomore" is already blasé.

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.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#76

Post by petra »

shpongled wrote: post #42
CaptainOfCoit wrote: post #40

Earlier quoted context omitted.

> I would be impressed if a sophomore in high school proposed it That sounds good enough for a start, considering you can massively parallelize the AI co-scientist workflow, compared to the timescale and physical scale it would take to do the same thing with human high school sophomores. And every now and then, you get something exciting and really beneficial coming from even inexperienced people, so if you can incre…

We don't need an army of high school sophomores, unless they are in the lab pipetting. The expensive part of drug discovery is not the ideation phase, it is the time and labor spent running experiments and synthesizing analogues.

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

Re: Accelerating scientific breakthroughs with an AI co-scientist

#77

Post by hinkley »

I am generally down on AI these days but I still remember using Eliza for the first time.

I think I could accept an AI prompting me instead of the other way around. Something to ask you a checklist of problems and how you will address them.

I’d also love to have someone apply AI techniques to property based testing. The process of narrowing down from 2^32 inputs to six interesting ones works better if it’s faster.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#78

Post by hinkley »

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…

> in silico discovery

Oh I don’t like that. I don’t like that at all.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#79

Post by hinkley »

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 have a less generous recollection of the wisdom of sophomores.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#80

Post by dekhn »

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.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.

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