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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

#91

Post by Nevermark »

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.

I am going to argue that you do. Then I will be interested in your response, if you feel inclined.

We all have our idiosyncratically distributed areas of high intuition, expertise and fluency.

None of us need apprentice level help there, except to delegate something routine.

Lower quality ideas there would just gum things up.

And then we all have vast areas of increasingly lesser familiarity.

I find, that the more we grow our strong areas, the more those areas benefit with as efficient contact as possible with as many more other areas as possible. In both trivial and deeper ways.

The better developer I am, in terms of development skill, tool span, novel problem recognition and solution vision, the more often and valuable I find quick AI tutelage on other topics, trivial or non-trivial.

If you know a bright high school student highly familiar with a domain that you are not, but have reason to think that area might be helpful, don’t you think instant access to talk things over with that high schooler would be valuable?

Instant non-trivial answers, perspective and suggestions? With your context and motivations taken into account?

Multiplied by a million bright high school students over a million domains.

—

We can project the capability vector of these models onto one dimension, like “school level idea quality”. But lower dimension projections are literally shadows of the whole.

It if we use them in the direction of their total ability vector (and given they can iterate, it is actually a compounding eigenvector!) and their value goes way beyond “a human high schooler with ideas”.

It does take time to get the most out of a differently calibrated tool.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#92

Post by hall0ween »

dekhn wrote: post #83
tsumnia wrote: post #81

Earlier quoted context omitted.

Remember Google is a publicly traded company, so everything must be reviewed to "ensure shareholder value". Like dekhn said, its impressive, but marketing wants more than "impressive".

This is true for public universities and private universities; you see the same thing happening in academic papers (and especially the university PR around the paper)

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

Re: Accelerating scientific breakthroughs with an AI co-scientist

#93

Post by Workaccount2 »

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…

Does this qualify as an answer to Dwarkesh's question?[1][2]

[1]https://marginalrevolution.com/marginalrevolution/2025/02/dw... [2]https://x.com/dwarkesh_sp/status/1888164523984470055

I don't know his @ but I'm sure he is on here somewhere

Re: Accelerating scientific breakthroughs with an AI co-scientist

#94

Post by Workaccount2 »

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.

People are facing existential dread that the knowledge they worked years for is possibly about to become worth a $20 monthly subscription. People will downplay it for years no matter what.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#95

Post by jiggawatts »

dekhn wrote: post #74
Teever wrote: post #62

Earlier quoted context omitted.

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 educ…

This sounds like it could be centralised, a bit like the clouds in the IT world. A low failure rate of 1-3% is comparable to servers in a rack, but if you have thousands of them, then this is just a statistic and not a servicing issue. Several hyperscalers simply leave failed nodes where they are, it’s not worth the bother to service them!

Maybe the next startup idea is biochemistry as a service, centralised to a large lab facility with hundreds of each device, maintained by a dedicated team of on-site professionals.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#96

Post by dekhn »

jiggawatts wrote: post #95
dekhn wrote: post #74

Earlier quoted context omitted.

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 educ…

This sounds like it could be centralised, a bit like the clouds in the IT world. A low failure rate of 1-3% is comparable to servers in a rack, but if you have thousands of them, then this is just a statistic and not a servicing issue. Several hyperscalers simply leave failed nodes where they are, it’s not worth the bother to service them! Maybe the next startup idea is biochemistry as a service, centralised to a lar…

None of the companies that proposed this concept have managed to demonstrate strong marketplace viability. A lot of discovery science remains extremely manual, artisinal, and vehemently opposed to automation.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#97

Post by devmor »

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.

This is the general problem with nearly all of this era of generative AI and why the public dislike it so much.

It is trained on human prose; human prose is primarily a representation of ideas; it synthesizes ideas.

There are very few uses for a machine to create ideas. We have a wealth of ideas and people enjoy coming up with ideas. It’s a solution built for a problem that does not exist.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#98

Post by mnky9800n »

parineum wrote: post #6
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…

> I don’t need an ai to connect across ideas or come up with new hypothesis. This feels like hubris to me. The idea here isn't to assist you with menial tasks, the idea is to give you an AI generalist that might ne able to alert you to things outside of your field that may be related to your work. It's not going to reduce your workload, in fact, it'll probably increase it but the result should be better science. I ha…

I have a billion ideas, being able to automate the testing of those ideas in some kind of Star Trek talk to the computer and it just knows what you want way would be perfect. This is the promise of ai. This is the promise of a personal computer. It is a bicycle for your mind. It is not hubris to want to be able to iterate more quickly on your own ideas. It is a natural part of being a tool building species.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#99

Post by falcor84 »

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.

As discussed elsewhere, Deepmind are also working on extending Alphafold to simulate biochemical pathways and then looking to tackle whole-cell simulation. It's not quite pipetting, but this sort of AI scientist would likely be paired with the simulation environment (essentially as function calling), to allow for very rapid iteration of in-silico research.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#100

Post by dekhn »

ein0p wrote: post #89
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…

That applies to absolutely everyone. Convenient results are highlighted, inconvenient are either not mentioned or de-emphasized. You do have to be well read in the field to see what the authors _aren't_ saying, that's one of the purposes of being well-read in the first place. That is also why 100% of science reporting is basically disinformation - journalists are not equipped with this level of nuanced understanding.

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.
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