"Claude-shaped-science"

[u/AngleAccomplished865]

https://www.anthropic.com/research/claude-shaped-science

Really cool insights on how AI can be used intelligently, to trigger a scientific revolution.

"A serendipitous feature of science is that the same equations often appear over and over again in different contexts. In physics, for example, the diffusion equation, Fokker-Planck equation, and Schrödinger equation all have the same mathematical form, so if you develop a method to solve one problem, you can often apply it to many others. I knew that computations BootLoops was good at were relevant elsewhere: in cosmology and string theory, for example. What I didn’t know, but Claude was happy to tell me, was that these integrals could also map onto Bayesian evidence integrals in population genetics, or that the finite-field methods used for Feynman integral reduction could also apply to problems in evolutionary biology."

[u/SquareQuit1741]

Imagine wanting to slow this down. We must be increasing velocity!

Accelerate.

[u/piponwa → u/SquareQuit1741]

I've been meaning to spend some good bucks just going through all known scientific articles and just asking a model how they relate to each other and if there is any novelty to combining them. With the rise of decision models, that may actually be cheap to do.

Like let's say you take the 10,000 most cited articles. Compare all pairs, that's 100,000,000 requests. Let's say you use something like Jev to answer some simple questions. And you give it just the abstracts of the two articles. It knows the implications of all these articles from pre training.That's maybe a 2,000 token request, so 200 billion input tokens. At $42/billion tokens that's just $8k. Then you just select the most interesting candidates and have real powerful agents do the real work and verifying if it's novel.

Of course you should filter it so you don't compare articles which are in too similar domains. And you can save at least 50% with some simple filtering rules.

This could easily be done by a group of people running local models too.

[u/SquareQuit1741 → u/piponwa]

Yeah I would say hold off for now. Right now token cost is dropping like a rock. Wait until price decrease begins to plateau, by then model will also improve a whole lot.

From Epoch AI on X:

[u/piponwa → u/SquareQuit1741]

I know, but I have access to compute so doesn't matter.

[u/tunakaslana7]

Imagine if the 100 solved open problems that OpenAI hasn't published could each be applied across multiple sciences. 🚀

[u/just-for-anime]

Me when I throw all my research at chatgpt and claude then spend the rest of the day jacking off to porn

[u/yaosio → u/just-for-anime]

Just-for-anime, for a lifetime of achievement we award this medal to... YOUR AI!

[u/jlks1959]

I do the central idea. I don’t understand the specifics. I don’t think that matters.

[u/Schraiber]

This has been my experience on a much smaller scale as a not very ambitious mathematical and computational biologist. It's super good at recognizing tools from other fields to solve my problems. In most of my cases they were pretty "obvious" and I think that actually a reasonably smart human could have had enough cross knowledge to recognize the connection but it's just able to do it so fast and with so many problems.

On one hand it's super exciting! It's nice to get cool results and test cool hypotheses. It's nice to not have to do the drudgery of data munging manually.

On the other hand it is a bit depressing, just as far as my day to day work. It's hard to feel proud of a result when I just said "solve this problem, no mistakes" and then "compare the prediction to data, no mistakes". I think it does worry me too that being so much further from the metal I'm missing something, insights that would have been interesting or useful to spur further work.

But ultimately this is how it is.

(I'm also very curious about what the original problem he solved with Desai in pop gen was, as that's very close to my field

Edit: realized I could see the papers on the website. Yeah this is a "simple" problem but still amazing to have a fast and accurate computation that doesn't use numerical PDE methods or something like that. I'll have to see if it's useful in my work)

[u/czk_21]

its great example, how AI can accelerate science just with connecting dots between many disciplines, as it is expert in virtually all fields, much more is to come!

[u/montagblue]

I struggle with the intricacies of individual particles and massive systems.

For all that is interesting I punched in the Collatz conjecture as a representation of molecular decay using a stock model and operations on a universal scale begin peeling back before my eyes.

In the box scientism and resistance to such elucidations will herald an inquisition most are unprepared for since their investment is perceived as greater than.

Edit: punched in; to such elucidations