First jump that computers gave us : speed. With excess of speed came the ability to brute force many problems.
Next jump given by AI (not LLMs specifically, I mean “machine learned systems” in general) is navigation. Even with large amounts of speed some problems are still impractically large, we are using AI to better explore that space, by navigating it smarter, rather than just speeding through it combinatorially.
I am reposting something along the lines of a flagged and dead comment: This would be lend more credibility to the premise AI is revolutionizing scientific discovery if it came from someone who's Nobel (or work in general) were in a non-AI-centered domain. This is not a critique of his speech or points, but I think the lead implied by the (especially Youtube) title would hit harder if it came from someone whose work wasn't AI-centered.
Jumper's work is the poster child of AI success in science; this isn't about a new domain being revolutionized by it.
I will throw out an idea I've been thinking about recently about a far less ambitious idea, but related: Amber (MD package) provides Force Field names and partial charges for a number of small organic molecules in their GeoStd set. I believe these come from its Antechamber program. Would it be possible to infer useful FF name and Partial charge for arbitrary organic molecules using AI instead, trained on the GeoStd set data?
I'll share something as a former solar researcher.
Scientific progress is heavily influenced by how many bodies you can throw at a problem.
The more experiments you can run, with more variety and angles the more data you can get, the higher the likelihood of a breakthrough.
Several huge scientist are famous not because they are geniuses, but because they are great fundraisers and can have 20/30/50 bodies to throw at problems every year.
This is true in virtually any experimental field.
If LLMs can be de facto another body then scientific progress is going to sky rocket.
Robots also tend to be more precise than humans and could possibly lead to better replication.
But given that LLMs cannot interact with the real world I don't see that happening anytime soon.
Seems you're burying the Lede in your post - yes, AIs aren't scientists.
What can be said about scientists and bodies is interesting but ultimately irrelevant.
Edit: I'd add that various LLMs/neural-nets have turned out to be great tools for research. I simply find the scientist-equivalent position problematic.
Yeah the title was the worst and the most disgusting part, and I'm saying this in totally positive sense. As soon as you catch that "DeepMind" name there, you know it's really about science and a crafted model, not a wannabe general intelligence.
The cynists will comment that I've just been sucked in by the PR. However, I know this team and have been using these techniques for other problems. I know they are so close to a computationally-assisted proof of counterexample that it is virtually inevitable at this point. If they don't do it, I'm pretty sure I could take a handful of people and a few years and do it myself. Mostly a lot of interval arithmetic with a final application of Schauder that remains; tedious and time-consuming, but not overly challenging compared to the parts already done.
I think the PR is making it seem that Deepmind is not standing on the shoulder of giants, when in fact it very much is. The paper itself makes this clear. I wish them luck!
They are building a formally-defined counter example to this? Am I understanding correctly?
> In three space dimensions and time, given an initial velocity field, there exists a vector velocity and a scalar pressure field, which are both smooth and globally defined, that solve the Navier–Stokes equations.
I have a mildly psychotic friend who think that he uncovered the secrets to everything with AI. Quantum theory and Jungian archetypes, together with 4 dimensions - great mix
Let's say the "Secrets of the Universe" broadly consists of Graph of 100 "abstract" interconnected concepts. The concepts have to be abstract because it is describing everything. It has to be limited in number because we cannot be endlessly chasing the definitions till we reach the levels of atoms. Is it possible to get glimpse of that Graph just toying with abstract ideas. The exact nodes / concepts used in the graph maybe different (depending on field) but the structure will be isomorphic. It has to be discoverable in any field since we started with the assumption that the Graph is "Secret of the Universe" so it should apply to any subset as well and should be discoverable from that subset. This is like analytic functions where knowing its derivatives in a small enough interval can lead us to the exact function.
>...noticed an email from one of my PhD students sent more than eight years ago, outlining a highly complex immune cell experiment that would run for several weeks and asking me to make corrections
>...Incredibly, GPT-5 Pro would have been as good as, if not better than, me at making these corrections, interpretations, analyses, and follow-up experiment suggestions! The experiment would also have yielded better results thanks to more precise planning...
Maybe the era of AI speeding things is upon us. Maybe not so long till AIs are helping make better AIs?
24 comments
[ 2.7 ms ] story [ 53.1 ms ] threadNext jump given by AI (not LLMs specifically, I mean “machine learned systems” in general) is navigation. Even with large amounts of speed some problems are still impractically large, we are using AI to better explore that space, by navigating it smarter, rather than just speeding through it combinatorially.
Why lying when one can easily find examples of exactly that?
1) https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs...
2) https://pmc.ncbi.nlm.nih.gov/articles/PMC11510778/
But I’m sure you could find the connections to the AI industry complex somehow.
https://research.nvidia.com/labs/dbr/blog/illustrated-evo2/
It's nice to see more and more labs using ai for drug discovery, something truly net positive for society.
Jumper's work is the poster child of AI success in science; this isn't about a new domain being revolutionized by it.
I will throw out an idea I've been thinking about recently about a far less ambitious idea, but related: Amber (MD package) provides Force Field names and partial charges for a number of small organic molecules in their GeoStd set. I believe these come from its Antechamber program. Would it be possible to infer useful FF name and Partial charge for arbitrary organic molecules using AI instead, trained on the GeoStd set data?
https://en.wikipedia.org/wiki/Combinatorial_chemistry
Scientific progress is heavily influenced by how many bodies you can throw at a problem.
The more experiments you can run, with more variety and angles the more data you can get, the higher the likelihood of a breakthrough.
Several huge scientist are famous not because they are geniuses, but because they are great fundraisers and can have 20/30/50 bodies to throw at problems every year.
This is true in virtually any experimental field.
If LLMs can be de facto another body then scientific progress is going to sky rocket.
Robots also tend to be more precise than humans and could possibly lead to better replication.
But given that LLMs cannot interact with the real world I don't see that happening anytime soon.
Pair LLMs with machines and robotics and you are getting closer
What can be said about scientists and bodies is interesting but ultimately irrelevant.
Edit: I'd add that various LLMs/neural-nets have turned out to be great tools for research. I simply find the scientist-equivalent position problematic.
Yes they can...VLAs exist.
Three points jumped out
1) "really when you look at these machine learning breakthroughs they're probably fewer people than you imagine"
In a world of idiots, few people can do great things.
2) External benchmarks forced people upstream to improve
We need more of these.
3) "the third of these ingredients research was worth a hundredfold of the first of these ingredients data."
Available data is 0 for most things.
The cynists will comment that I've just been sucked in by the PR. However, I know this team and have been using these techniques for other problems. I know they are so close to a computationally-assisted proof of counterexample that it is virtually inevitable at this point. If they don't do it, I'm pretty sure I could take a handful of people and a few years and do it myself. Mostly a lot of interval arithmetic with a final application of Schauder that remains; tedious and time-consuming, but not overly challenging compared to the parts already done.
> In three space dimensions and time, given an initial velocity field, there exists a vector velocity and a scalar pressure field, which are both smooth and globally defined, that solve the Navier–Stokes equations.
https://x.com/DeryaTR_/status/1972115494787338484
>...noticed an email from one of my PhD students sent more than eight years ago, outlining a highly complex immune cell experiment that would run for several weeks and asking me to make corrections
>...Incredibly, GPT-5 Pro would have been as good as, if not better than, me at making these corrections, interpretations, analyses, and follow-up experiment suggestions! The experiment would also have yielded better results thanks to more precise planning...
Maybe the era of AI speeding things is upon us. Maybe not so long till AIs are helping make better AIs?