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.
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.
https://arxiv.org/abs/2509.06503
They set up scoreable computational science problems and do search over solutions.
What type of interaction do you envision? Could a non-domain-expert, but somewhat trained person provide a bridge? If the LLM comes up with the big ideas and tells a human technical assistant to execute (put the vial here, run the 3D printer with this file, put the object there, drive in a screw), would that help? But dexterous robots are getting more and more advanced, see CoRL demos right now.
No, because the bottleneck isn't the thinking but running experiments.
I worked in solar research, assembling a cell to test implied 40 different steps and from beginning to testing it was around 4 to 5 days.
This means that in one year working full time I will realistically run 40ish different experiments. Many of those will need to be done multiple times, and when you have 40 different steps that can go wrong and kill your efficiency this further compounds.
Thus realistically are running 5 to 10 different experiments (or better, a handful plus their variations).
At no point in this process you're like "yeah, if only LLMs could provide ideas", it's just not true, you get millions of ideas, time and bodies are the limit.
Another delay point is getting collaborators schedules to align for meetings on progress or potential directions.
Placing the results in context takes some time but not so much as you might guess if you are constantly reading and writing sourced paragraphs and skeleton papers needing only results plopped in when they are ready and some exposition in the discussion section.
Writing the code might be the fastest step in the process already.
Pair LLMs with machines and robotics and you are getting closer
Yes they can...VLAs exist.
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?
No it wouldn't. I've seen anti-AI people try to make this sort of argument repeatedly and it doesn't make any sense.
It's an attempt to smuggle in an ad-hominem. It's relying on the fact that people who hate AI also hate people who work in AI.
Carter sold his peanut farm to avoid conflicts of interest, Trump launched a coin pump & dump on his first day.
If a Nobel Prize winner works for a corporation, that should be disclosed (the original title contained "Nobel Prize Laureate" instead of "DeepMind Director").
But I suppose that in the current age where everyone just wants to get rich these courtesies no longer matter.
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.
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.
- Build a complex intractable mathematical model (here, Navier-Stokes)
- Approximate it with a function approximator (here, a Physics Informed Neural Network)
- Use the some property of function approximator to search for more solutions to the original model (here, using Gauss-Newton)
In a sense, this is actually just the process of model-based science anyway: use a model for the physical world and exploit the mathematics of the model for real-world effects.
This is very very good work, but this heritage goes back to polynomial approximation even from Taylor series, and has been the foundation of engineering for literal centuries. Throughout history, the approximator keeps getting better and better and hungrier and hungrier for data (Taylor series, Chebyshev + other orthogonal bases for polynomials, neural networks, RNNs, LSTMs, PINNs, <the future>).
You didn't say anything to the contrary, and neither did the original video, but it's very different than what some other people are talking about in this thread ("run an LLM in a loop to do science the way a person does it"). Maybe I'm just ranting at the overloading of the term AI to mean "anything on a GPU".
I also wouldn't say this is entirely "classical". Old, yes, but still unfamiliar and controversial to a surprising number of people. But I get your point :-).
That's a strong claim. Is it based on more than the linked work on some model problems from fluid mechanics?
I will say that I dread the discourse if it works out, since I don't believe enough people will understand that using a PINN to get new solutions of differential equations has substantially no similarity to asking ChatGPT (or AlphaProof etc) for a proof of a conjecture. And there'll be a lot of people trying to hide the difference.
PINNs are different in concept, yes, but clearly no less important, so the additional attention will be appreciated. Asking LLMs for proofs is a different vein of research, often involving Lean. It is much further behind, but still making ground.
> 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.
When working toward a problem of this magnitude, it is natural to release papers stepwise to report progress toward the solution. Perelman did the same for the Poincare conjecture. Folks knew the problem was near a solution once the monotonicity proof of the W functional came out.
You can just ignore them. I see a lot of science-literate folks try to meet the anti-science folks as if they're on equal footing and it's almost always a waste of time. Imagine if every time you talked about biology you had to try to address the young earth creationists in the room and try to pre-rebut their concerns.
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?
Maybe its utopia, maybe its akin to an Eliezer Yudkowsky prediction, who knows. Regardless of the specific outcome, its a huge gamble with effectively unlimited risk.
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.
Still at the top of the benchmarks of integer optimization by huge margin are the traditional usual suspects. Same in constraint programming and SAT.
Not published just yet are experiments for finding solutions to mathematical problems traditionally found with SAT solvers, at much larger scale than was previously possible.
Uh, ok, I didn't claim that. At All.
Deep Learning machine learned features have definitely helped us (meaning my company) over hand engineered features, allowing us to navigate our problem space significantly faster
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.
> Available data is 0 for most things.
I would argue that we need an effective alternative to benchmarks entirely given how hard they are to obtain in scientific disciplines. Classical statistics has gone very far by getting a lot out of limited datasets, and train-test splits are absolutely unnecessary there.
Even if this is a super high bar, I think more papers in ML for science should strive to be truly interdisciplinary and include an actual science advancement... Not just "we modify X and get some improvement on a benchmark dataset that may or may not be representative of the problems scientists could actually encounter." The ultimate goal of "ml for science" is science, not really to improve ML methods imo
