Not to be a luddite, but large language models are fundamentally not meant for tasks of this nature. And listen to this:
> Most notably, it provides confidence levels in its findings, which Cheeseman emphasizes is crucial.
These 'confidence levels' are suspect. You can ask Claude today, "What is your confidence in __" and it will, unsurprisingly, give a 'confidence interval'. I'd like to better understand the system implemented by Cheeseman. Otherwise I find the whole thing, heh, cheesy!
I've spent the last ~9 months building a system that, amongst other things, uses a vLLM to classify and describe >40 million house images of number signs in all of Italy. I wish I was joking, but that aside.
When asked about their confidence, these things are almost entirely useless. If the Magic Disruption Box is incapabele of knowing whether or not it read "42/A" correctly, I'm not convinced it's gonna revolutionize science by doing autonomous research.
Finding patterns in large datasets is one of the things LLMs are really good at. Genetics is an area where scientists have already done impressive things with LLMs.
However you feel about LLMs, and I say this because you don't have to use them for very long before you witness how useful they can be for large datasets so I'm guessing you're not a fan, they are undeniably incredible tools in some areas of science.
As a scientist, the two links you provided are severely lacking in utility.
The first developed a model to calculate protein function based on DNA sequence - yet provides no results of testing of the model. Until it does, it’s no better than the hundreds of predictive models thrown on the trash heap of science.
The second tested a models “ability to predict neuroscience results” (which reads really oddly). How did they test it? Pitted humans against LLMs in determining which published abstracts were correct.
Well yeah? That’s exactly what LLMs are good at - predicting language. But science is not advanced by predicting which abstracts of known science are correct.
It reminds me of my days in working with computational chemists - we had an x-ray structure of the molecule bound to the target. You can’t get much better than that at hard, objective data.
“Oh yeah, if you just add a methyl group here you’ll improve binding by an order of magnitude”.
So we went back to the lab, spent a week synthesizing the molecule, sent it to the biologists for a binding study. And the new molecule was 50% worse at binding.
And that’s not to blame the computation chemist. Biology is really damn hard. Scientists are constantly being surprised at results that are contradictory to current knowledge.
Could LLMs be used in the future to help come up with broad hypotheses in new areas? Sure! Are the hypotheses going to prove fruitless most of the time? Yes! But that’s science.
But any claim of a massive leap in scientific productivity (whether LLMs or something else) should be taken with a grain of salt.
I don't follow the logic that "it hallucinates so it's useless". In the context of codebases I know for sure that they can be useful. Large datasets too. Are they also really bad at some aspects of dealing with both? Absolutely. Dangerously, humorously bad sometimes.
Old "agged Technological Frontier" but explains a bit the challenge https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 namely... it's hard and the lack of reproducibility (models getting inaccessible to researcher quickly) makes this kind of studies very challenging.
Can't LLMs be fed the entire corpus of literature to synthesise (if not "insight") useful intersections? Not to mention much better search than what was available when I was a lowly grad...
I'm just a noob but lecun seems obsessed with the idea of world models, which I assume means a more rigorous physical approach, and I don't understand (again, confused noob here) how are t would help precise abstract thinking.
Call me when a disinterested third-party says so. PR announcements by the very people who have a large stake in our belief in their product are unreliable.
This company predicts software development is a dead occupation yet ships a mobile chat UI that appears to be perpetually full of bugs, and has had a number of high profile incidents.
"This company predicts software development is a dead occupation"
Citation needed?
Closest I've seen to that was Dario saying AI would write 90% of the code, but that's very different from declaring the death of software development as an occupation.
What quotes? This is an AI summary that may or may not have summarized actual quotes from the researchers, but I don't see a single quote in this article, or a source.
Why are you commenting if you can't even take a few minutes to read this ? It's quite bizarre. There's a quote and repo for Cheeseman, and a paper for Biomni.
There is only one quote in the entire article, though:
> Cheeseman finds Claude consistently catches things he missed. “Every time I go through I’m like, I didn’t notice that one! And in each case, these are discoveries that we can understand and verify,” he says.
Pretty vague and not really quantifiable. You would think an article making a bold claim would contain more than a single, hand-wavy quote from an actual scientist.
>Pretty vague and not really quantifiable. You would think an article making a bold claim would contain more than a single, hand-wavy quote from an actual scientist.
Why? What purpose would quotes serve better than a paper with numbers and code? Just seems like nitpicking here. The article could have gone without a single quote (or had several more) and it wouldn't really change anything. And that quote is not really vague in the context of the article.
Conflict of interest is a thing. The researchers could be AI hallucinations. The quotes could be too. Or the researchers could be real and intentionally saying things that are untrue. Who knows.
What is interesting is that HN seems to have reached a crescendo of AI fanboi posts. Yet if you step outside the bubble the Microsoft and Nvidia CEOs are begging people to actually like AI, Dell's come out and said that people don't want AI, and forums are littered with people complaining about negative consequences of AI. Go figure.
Anthropic puts out plenty of AI slop. I'll wait for a human who doesn't have a financial interest in propping up Anthropic to review the slop before passing judgement.
> scholarly dark matter that exists to pad CVs and satisfy bureaucratic metrics, but which no one actually reads or relies upon.
Is it cynical to believe this is already true and has been forever?
Is it naive to hope that when AI can do this work, we will all admit that much of the work was never worth doing in the first place, our academic institutions are broken, and new incentives are sorely needed?
I’m reminded of a chapter in Abundance where Ezra Klein notes how successful (NIH?) grant awardees are getting older over time, nobody will take risks on young scientists, and everyone is spending more of their time churning out bureaucratic compliance than doing science.
Have you done anything interesting with this army that you can share and you're proud of? Specifically something concrete you can link to, not just something you can imagine or describe?
I believe LLM, (specifically code gen) has produced nothing of substance. I'm looking for evidence to disprove that assumption. You're welcome to share nothing, but when you brag about how it's fantastical, it's reasonable to ask. And then no one can never prove it... I can only hear that as; I could if I want to, I just don't want to.
If you don't want field questions about it, don't brag about it?
Equally, to your condemnation: the problem with AI enjoyers is they claim it's nearly perfect, and it can do everything, and it makes them so much faster. But every example is barely more than boilerplate, or it's a sham.
> Most notably, it provides confidence levels in its findings, which Cheeseman emphasizes is crucial.
These 'confidence levels' are suspect. You can ask Claude today, "What is your confidence in __" and it will, unsurprisingly, give a 'confidence interval'. I'd like to better understand the system implemented by Cheeseman. Otherwise I find the whole thing, heh, cheesy!
When asked about their confidence, these things are almost entirely useless. If the Magic Disruption Box is incapabele of knowing whether or not it read "42/A" correctly, I'm not convinced it's gonna revolutionize science by doing autonomous research.
However you feel about LLMs, and I say this because you don't have to use them for very long before you witness how useful they can be for large datasets so I'm guessing you're not a fan, they are undeniably incredible tools in some areas of science.
https://news.stanford.edu/stories/2025/02/generative-ai-tool...
https://www.nature.com/articles/s41562-024-02046-9
The first developed a model to calculate protein function based on DNA sequence - yet provides no results of testing of the model. Until it does, it’s no better than the hundreds of predictive models thrown on the trash heap of science.
The second tested a models “ability to predict neuroscience results” (which reads really oddly). How did they test it? Pitted humans against LLMs in determining which published abstracts were correct.
Well yeah? That’s exactly what LLMs are good at - predicting language. But science is not advanced by predicting which abstracts of known science are correct.
It reminds me of my days in working with computational chemists - we had an x-ray structure of the molecule bound to the target. You can’t get much better than that at hard, objective data.
“Oh yeah, if you just add a methyl group here you’ll improve binding by an order of magnitude”.
So we went back to the lab, spent a week synthesizing the molecule, sent it to the biologists for a binding study. And the new molecule was 50% worse at binding.
And that’s not to blame the computation chemist. Biology is really damn hard. Scientists are constantly being surprised at results that are contradictory to current knowledge.
Could LLMs be used in the future to help come up with broad hypotheses in new areas? Sure! Are the hypotheses going to prove fruitless most of the time? Yes! But that’s science.
But any claim of a massive leap in scientific productivity (whether LLMs or something else) should be taken with a grain of salt.
Where by "good at" you mean "are totally shit at"?
They routinely hallucinate things even on tiny datasets like codebases.
But the latter doesn't invalidate the former.
There should be some research results showing their fundamental limitations. As opposed to empirical observations. Can you point at them?
What about VLMs, VLAs, LMMs?
Citation needed?
Closest I've seen to that was Dario saying AI would write 90% of the code, but that's very different from declaring the death of software development as an occupation.
> Cheeseman finds Claude consistently catches things he missed. “Every time I go through I’m like, I didn’t notice that one! And in each case, these are discoveries that we can understand and verify,” he says.
Pretty vague and not really quantifiable. You would think an article making a bold claim would contain more than a single, hand-wavy quote from an actual scientist.
Why? What purpose would quotes serve better than a paper with numbers and code? Just seems like nitpicking here. The article could have gone without a single quote (or had several more) and it wouldn't really change anything. And that quote is not really vague in the context of the article.
What is interesting is that HN seems to have reached a crescendo of AI fanboi posts. Yet if you step outside the bubble the Microsoft and Nvidia CEOs are begging people to actually like AI, Dell's come out and said that people don't want AI, and forums are littered with people complaining about negative consequences of AI. Go figure.
Taking CV-filler from 80% to 95% of published academic work is yet another revolutionary breakthrough on the road to superintelligence.
Is it cynical to believe this is already true and has been forever?
Is it naive to hope that when AI can do this work, we will all admit that much of the work was never worth doing in the first place, our academic institutions are broken, and new incentives are sorely needed?
I’m reminded of a chapter in Abundance where Ezra Klein notes how successful (NIH?) grant awardees are getting older over time, nobody will take risks on young scientists, and everyone is spending more of their time churning out bureaucratic compliance than doing science.
Honestly it's a pattern when arguing about the topic :
- wow AI is amazing!
- OK, why?
- because it can do so much stuff! I'm making so much projects and earning a lot!
- cool, care to share a link to anything?
- ... radio silence or yet another NES emulator or something that is clearly so curated it might have taken more work than without any "AI assistance"
If you don't want field questions about it, don't brag about it?
Equally, to your condemnation: the problem with AI enjoyers is they claim it's nearly perfect, and it can do everything, and it makes them so much faster. But every example is barely more than boilerplate, or it's a sham.
Funny you say that.