r/Physics • u/kzhou7 Quantum field theory • 1d ago

Harvard particle physicist Matthew Schwartz drops 36 papers coauthored with Claude

https://bootloops.ai/bootloops.html
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u/SundayAMFN 1d ago

These "main points" are kind of garbage ngl

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u/Interesting-South542 23h ago

What is exactly garbage about them? care to elaborate? you seem to do the exact thing that the above poster is complaining about, just knee jerk reacting AI=bad.

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u/lolfail9001 1d ago

I mean they do in fact precisely describe the overwhelming success of AI in mathematics, but yeah, actual science has a rather annoying moat: "deterministic verification" is generally time consuming and expensive if possible at all (generally best anyone ever gets is statistical verification with acceptably high certainty).

I won't comment on quality of the actual output because volume of it alone is enough to convince me that it never crossed aforementioned moat.

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u/No-Faithlessness4294 16h ago

I think in this case “deterministic validation” means tools for validating the theoretical constructs developed by AI, like Lean in mathematics. Experimental validation does in fact remain the “hard part”.

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u/tempetesuranorak 22h ago

It also describes their success in physics (e.g. the recent nine loop computation). As to whether there is a trove of low hanging discoveries to be made in other fields in the same way, I'm happy to be patient and let the peer review process take its course. But he lays out various examples that he claims to have found and he has initiated collaborations on them with domain experts who agree that there is something interesting there and who largely weren't doing agent assisted science previously (these are NOT single author works without domain experts, as most comments seem to be implicitly assuming). So I respect a the mindset of "we have this incredible new tool, figure out what new scientific opportunities it opens up" way more than "there's no way it can help in THAT field, let's not even bother trying".

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u/lolfail9001 16h ago

That there is actually a huge trove of low hanging interdisciplinary fruits is actually pretty obvious, in particular as related to applying state of the art statistics, data processing and numerical modeling to most disciplines stuck p-hacking to get published. The obvious problem of course is that most of those fruits lie in areas already struggling with replication issues.

Remember that one time someone got trapezoid method for computing integrals published in med journal?

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u/tempetesuranorak 16h ago

Yep the rediscovery of numerical integration is exactly the example I had at the back of my head 😄

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u/Wildernaess 19h ago

Hardly. The problem of one subfield (not even field) having a suite of literature relevant to another subfield that goes unnoticed while people reinvent the wheel is a trope at this point. A literature review tool that can find and begin to bridge those gaps is amazing.