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points by ChemSpider 3 days ago | 0 comments

How long until we see similar announcements for (wet lab) chemistry, medicine or material science? Months? Years? Never?

(Never would mean AI will be used in future labs like a fancy calculator, but not much more changes)

[–] Routine8502 3 days ago

I'll give you an example from my field (nuclear fusion). I have a small team working on an idea which I think would really advance the field. Our initial concept paper was published in 2019. Progress on it has been slow because we don't have enough manufacturing capabilities, budget, or time on the facility. Will AI solve that? Don't get me wrong, I'm pretty bullish on AI, and it's been phenomenally useful so far (I definitely feel like a 10x physicist in the last 6 months), but I don't see a swarm of agents putting fusion energy on the grid tomorrow.

Also, fwiw I have a personal test problem I give to every frontier model on release, which is to ask it to final a realatively obvious solution (in hindsight) to a minorly important open problem in my field. I know the answer, but havent gotten around to writing it up yet (it's really not that important). Every frontier model (so far) just regurgitates existing solutions.

[–] szvsw 3 days ago

How much effort do you put into prompting it? Are you asking it one shot with a simple prompt? Are you giving it to multiple agents and giving them a workspace to collaborate in? You might give it to a trusted colleague who does not know the solution along with an agreement to jointly publish, ask them to go through the process of solving it with agentic support, and then compare your solutions.

Edit: to be clear, I completely believe your experience - just pointing out that it might be interesting for you to actually “try” to solve it thoroughly with the tools, though it is obviously hard to do that in a fair way when you know the solution.

[–] Routine8502 3 days ago

Enough! I think it's a reasonably fair test of the models. Let's put it this way, I use the same process to resolve problems outside my peak expertise, and I find it works well.

I think the solution is easy enough that any competent colleague would come up with it. Some variant on it has probably even been published (hence why I'm not chomping at the bit to publish). I think it comes down to the models being extremely spiky, and the problems they're trying to solve being easily verifiable in code.

[–] Buttons840 3 days ago

Have you ever told the AI your answer? I bet if you did it would train on it and know it going forward.

[–] Routine8502 3 days ago

Nah. And I have an enterprise plan, so I'm /relatively/ confident that they aren't going to do that.

[–] eru 3 days ago

With my personal little test problem, that never made a difference.

However current frontier models got good enough at websearch and thinking through the problem, that they can finally piece together the answer from my own public writing (eg on stackoverflow). And recently someone wrote up an answer to the problem and put it on Arxiv.

[–] Buttons840 2 days ago

So, there's a very niche topic that I was generating some fiction about using AI, and as part of that I gave one of the characters an unusual name. A few months later I again experimented with using AI for this fictional niche, and the AI just randomly chooses to use the same unusual name.

"Forebears" is a site that ranks the popularity of names. They rank the name I used at 324,668.

This is why I believe the AI can be significantly influenced by a single person.

[–] eru 2 days ago

Did you use the same AI provider both times? They might have just looked it up in the memory.

[–] Routine8502 2 days ago

What did you select for data controls?

[–] therealdrag0 3 days ago

Iirc some of the prior math discoveries involved very very very heavy handed guidance to force the AI to discard prior assumptions, and to try many avenues with MAX effort. So ya models don’t behave like ground breaking theoreticians by default.

[–] pfdietz 3 days ago

Do a breakthrough!

[–] eru 3 days ago

Ha, I had a similar little test problem, but the frontier models are now smart enough to read enough online of what I posted here and there to piece together the answer. And someone else has written the paper in the meantime and got it accepted for publishing.

[–] marshray 3 days ago

Definitely. It will let some smooth-talker with a vibe-coded website raise billions for "Nuclear Fusion AI". Then they can use AI to squeeze your team with lawsuits until you agree to sell cheap, and maybe keep you on as an employee to build it.

[–] Routine8502 3 days ago

Hah. Fusion scammers are a dime a dozen. Unlike SaaS apps or finance, though, you can't sweet talk a neutron into existence, so they fizzle out pretty fast. I'm more worried about fusion dying because nobody comes up with a good enough idea than someone stealing my ideas.

[–] eru 3 days ago

Going on a tangent: I think fusion is pretty much useless this century. We already have energy generation technology that has high capital costs and low running costs: it's called nuclear fission and works fairly well.

Fission hasn't exactly taken the world by storm.

There are a few nice advantages fusion has, but they are more important once we want to reach the stars as far as I can tell; not this century on this planet.

But I'm just an armchair analyst. You probably have a much better understanding.

[–] Routine8502 3 days ago

Nah I agree with you. Not this century, not this planet. Fusion rockets are really efficient, though ;)

I just find it to be an interesting problem! My hope is it happens before I die. My rule of thumb is anyone who says energy on the grid in < 10 years is a blatant liar.

[–] eru 2 days ago

Oh, I think energy on the grid in < 10 years is probably technically feasible.

It's just probably not economically feasible, ie it would be a waste of resources to rush fusion so much.

> I just find it to be an interesting problem!

Ha, that's why I was working Zero Knowledge Proofs for quite a while. I don't even hold any cryptocurrency or other crypto assets.

[–] Routine8502 2 days ago

I agree that you can build a facility to put energy on the grid in under a decade, but the probability that it works is going to be tiny, and anyone giving you the billion or so it would take would be disappointed to say the least.

Therefore, most credible folks are talking a 2-3 facility staged approach. And probably 5 years per facility to build, and 5 years per to test.

Also, remember that even with huge cash pools, you have supply chain problems. Some of the nonlinear optics takes a fixed amount of time to fabricate.

[–] tintor 3 days ago

How about swarm of agents controling a fleet of humanoids and automated construction vehicles?

[–] Routine8502 2 days ago

I guess I'd say, let's have a swarm of agents build a metro line first and see how well that goes. Obviously, given that we have embodied intelligence (sometimes) that's capable of doing this work, it would be silly of me to say that it's impossible for it to happen in silicon. But given where the SOTA is right now I'm skeptical of this being true in the near future. But hey! If you'd asked me the same question about GPT in 2019 I'd have said I would be skeptical there too. So who knows.

[–] sheepscreek 3 days ago

I mean, you never know. If we could have simulation models with excellent fidelity, then AI could do all the research and iterations and come up with something very plausible. Cutting down physical world experimental iterations/prototyping.

So effectively, you would spend the first 1000 experiments inside a computer simulation and move to the physical world only when you had enough confidence.

[–] Routine8502 3 days ago

I think you should realize that we already do that. In fact, that's one of the reasons computers were invented in the first place! It turns out that science is about unknown unknowns, and by definition simulations can't answer that.

But agents are really useful for doing some of the really boring stuff, like convergence checks or setting up meshes or whatever.

[–] sheepscreek 3 days ago

If you're talking about things at the equivalent level of say, finding out if positrons maintain symmetry with electrons for their magnetic moment, then yeah - I hear you. Or if you wanted to research some unknown behaviour of hot plasma at 15 million C, you can't do that in a simulation.

..or can we?

Humour me for a moment. Maybe simulating to the degree that was necessary for cutting edge work was not possible earlier, when humans had to program each and every thing in the system that could affect the result. But with current gen petaflop-on-your-desk level compute + swarms of agents that can work tirelessly to account for every known thing to humanity, maybe the threshold has been pushed far enough to produce some more meaningful discovery work in simulation?

[–] Routine8502 2 days ago

Respectfully, finding out how scientific computation is used in fusion research is a Google click away. And yes, we can simulate these conditions.

Let's put it this way - a first year graduate student can ignite a fusion target in simulations. It took thousands of scientists the better part of three decades to do that in experiments. I think agents could do the former really efficiently and at scale, but I'm skeptical that agents could do the latter. Oh, and btw, fusion simulations of the kind you're talking are in exa-zettaflop territory ;)

[–] sheepscreek 2 days ago

> Oh, and btw, fusion simulations of the kind you're talking are in exa-zettaflop territory

Haha, thanks for humouring me!

[–] acidburnNSA 3 days ago

I've got frontier models doing pretty sophisticated nuclear reactor design and analysis using open-source nuclear fission reactor tools using open-source reactor designs as baselines. It is WILD how good they do.

[–] ta93754829 3 days ago

Nothing could go wrong

[–] acidburnNSA 3 days ago

Doing AI assisted enggineering to explore a space and then using classical methods to validate a point design for licensing is a good approach. In licensing the vendor and the regulator check things and then also verify many times during startup. The overall safety remains good.

[–] jacquesm 3 days ago

Up front: I totally believe you are doing your level best so please don't take this as trying to take you down, I just want to understand. In the hypothetical case that in 10 years time it turns out that that design was flawed who will be on the hook? You, the AI vendor, the regulator, the device vendor?

[–] acidburnNSA 2 days ago

Nuclear liability is always 100% held by the owner/operator by law in most countries. All manufacturers of equipment and components demand and are given indemnity.

[–] jacquesm 1 day ago

Thank you for the answer. I can see the indemnification as being a requirement to work on anything like this, the downsides would be so large as to be uninsurable so probably without that indemnification the work simply wouldn't get done.

[–] dannykwells 3 days ago

Well you can’t verify a prediction about human biology without a phase 3 clinical trial so…

[–] criemen 3 days ago

The thing that I've not fully groked here (might be relevant) is that OAI is generating "paper" proofs first, and only then formalizes them in Lean as a separate step. They've as far as I'm aware not said anywhere how many write ups failed formalization due to a bug in the proof. If we assume that number is low, models would be quite good at one-shotting math proofs. Then it depends if you can replicate the math RL training for chemistry/biology/medicine. Is there enough data out there for that? No idea.

[–] abathologist 3 days ago

I'm seeing estimates of a %95-91 failure rate on attempts in generally (or, conversely, a max of ~10% success, but likely lower, since we can only go based on how many attempts they are willing to mention in their statements). E.g., from analysis in https://medium.com/@mehmet.ozel2701/openai-tried-about-4-000...

I wonder how many of those founder at the "paper" to formalization step, or at earlier or later steps (e.g., proof doesn't turn out to prove what the paper said, hits a bug in the kernel, etc.).

[–] eru 3 days ago

Wouldn't a bug in the (Lean) kernel just result in a PR to Lean?

[–] KerrAvon 3 days ago

based on how bad these models are at one-shotting complex real-world software engineering problems -- it always has to be a conversation -- there is absolutely no way they're one-shotting complex math proofs.

Also, goes without saying, but put Sam Altman in jail, obviously.

[–] robswc 3 days ago

I would trade (as if I have any say lol) every single breakthrough they have made in math/CS for _any_ good progress towards a universal cancer cure (very theoretical, I know).

I'm kinda hoping they are investing just as much into that type of research. Its their money but man... I'd say 999/1000 people are unaffected by all these breakthroughs. They probably have no idea tons of legendary math/CS conjectures were proven true/false. If a breakthrough in cancer research were to be made, it would very quickly start effecting everyone on earth.

[–] IAmBroom 2 days ago

> I would trade (as if I have any say lol) every single breakthrough they have made in math/CS for _any_ good progress towards a universal cancer cure (very theoretical, I know).

> I'm kinda hoping they are investing just as much into that type of research.

The money would be better spent trying to breed a flying unicorn, because that's remotely possible.

"Cancer" is not "a" disease. It's a symptomatic description: cells in your body multiplying beyond a limit that is healthy for you.

What you are asking for would be like asking for a universal cure for headaches. What if your headache is caused by a tumor? Or a nailgun injury? Or psychotic hallucination?

[–] zardo 3 days ago

The time required to verify is much higher, so some combination of vastly speeding that up or vastly increasing the quality of individual "thought steps".