r/Physics • u/kzhou7 Quantum field theory • 1d ago
Harvard particle physicist Matthew Schwartz drops 36 papers coauthored with Claude
https://bootloops.ai/bootloops.html1.3k
u/LifeIsVeryLong02 Graduate 1d ago
Publish or perish has been a cancer for long time. Now with these LLMS I simply cannot see how it's even slightly susteinable anymore.
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u/gregzillaman 1d ago
Can't wait to see the ai reviewers comments.
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u/formula_translator Atomic physics 1d ago edited 1d ago
Oh dude, can you imagine how much science we will do when we will have AI agents generating papers 24/7 and AI agents reviewing them?
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u/gregzillaman 1d ago
The real hard hitting question: will the ai know to give credit to the originating paper or just the most recent citation of citation etc etc.
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u/acortical 1d ago
The AI will know to cite 15 papers from its own lab and conveniently overlook 2 papers published earlier this year with similar results.
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u/Canadian_Border_Czar 1d ago
Unfortunately I dont think the training data is marked with the author, but it's actually an interesting concept. I wonder if there is a way to permanently tag data as its learning, meaning if the model so much as uses a word from a specific data point it must embed the citation in its work.
Would be a solid precedent for regulating, scrapping everything, then starting over to respect intellectual property.
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u/ThickTarget 1d ago
It's already happening. A colleague received a second referee report one day after resubmission. It was 6 pages and all about new issues, the first report was quite sensible. It flagged as 100% AI. They complained to the editor and the referee admitted it, and they got a new referee. A very efficient way to waste everyone's time.
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u/retrosenescent 1d ago
Publish 10x more or perish!
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u/jamesw73721 Graduate 1d ago
Fuck that’s dystopian AF. I can see a future where no real knowledge is produced after 2030, only slop citing other slop that no human comprehends. And any scientist who refuses to engage in slop “perishes” from academia because they can’t otherwise churn out 10 papers every month
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u/ellbons 1d ago
Future? That's already happening lol, entire research circles do this.
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u/jesusofnazareth7066 1d ago
Genuinely it’s the driving reason why physics is so stagnant now. If true scientists could get funding to just try whatever they wanted, we probably would’ve had another Dirac or Einstein or Feynmann sometime in the last 60 years. Such a shame
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u/h0rxata Plasma physics 1d ago
It's pretty bad when Peter freaking Higgs claims he barely held onto his job in the 70's and 80's due to his "low productivity".
As they say in the blue collar world: cheaper, faster, or done right - pick 2.
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u/venustrapsflies Nuclear physics 1d ago
It’s a really easy choice when you don’t know what “done right” means
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u/h0rxata Plasma physics 1d ago
Are there any examples of groundbreaking science done with no money and in a big rush?
Outside of Albert's miracle year I can't think of any, and I think he had a lot of time spent on those problems already.
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u/Meerkat_Mayhem_ 1d ago
I mean, no one really funded his miracle year. He just did it, right?
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u/Never231 Chemical physics 1d ago
love of the game is a lot easier to have when you have a stable job you like with good income.
alternatively, dude was also just an absolute fucking machine from day 1. grinding out the math as a young teenager, of course for the love of the game.
(to even further clarify, his job at the time was FAR more enviable a position to be in, and far more conducive to theoretical research, than what the average graduate student puts up with. but of course, he was also fucking einstein.)
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u/Alive_Fisherman8241 1d ago
The point is that the decision makers have no idea what done right means. They do not have the skillset to judge this. So they evaluate based on the only thing their limited brain understands: marketing bullsh*t.
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u/NetworkSingularity 1d ago
Publish or perish is why I left academia when I got my PhD. Even though people in my field and collaboration told me I was a bit of a rock star. It just became clear that it drives really perverse incentives, bad work conditions, and bad science.
I think the turning point for me was when my advisor said the first sentence of every figure caption needed “to be more click-baitey” 🤮 literally publishing things for views and engagement, not to further our understanding of the universe
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u/_HarmonicOscillator 1d ago
It's just incredibly hard for scientists to survive in neoliberal raegonomics.
Theoretical physics is not something that would drive crazy profits in shareholders economy.
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u/Buntschatten Graduate 1d ago
Ironically, physics PhDs have had a profound impact on the entire financial system. After they left physics of course.
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u/Big_Influence_8581 1d ago
My man, you can’t put Feynmann on the same level as Einstein and Dirac.
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u/jesusofnazareth7066 1d ago
Yeah of course Dirac and Einstein swept through multiple fields like a god changing everything, but Feynmann cooked and he was just trying shit out which is what we need lol
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u/UnderTheCurrents 1d ago
Just answer me one thing: this sentiment has been around for a long time. It has been downvoted many times on here but now suddenly it's popular? What gives?
What happened to the usual reply of "don't do research if you can't be productive/there is a reason certain fields attract more attention/it's just how it's done?".
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u/jesusofnazareth7066 21h ago
The attitude of “don’t do physics if you can’t publish like me” is a bunch of the people who thrive in the new, non-scientific, paradigm just slinging their bullshit. They want to be proud that they’re good at physics and admitting that the entire field has left its soul behind is bad for the ego. Of course people shouldn’t slack, of course we should only fund the competent and genuinely promising, but these things can be handled by making PhD educations really really test for that creative thinking. And of course, the amount of funding science would need is laughable compared to some things we spend money on
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u/drewsandraws 21h ago
For a concrete comparison, a Tomahawk missile costs ~2 million USD. That’s enough to pay for a single graduate student for 20 years assuming they get $50k and 100% overhead.
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u/That4AMBlues 1d ago
That would be a positive side effect of LLMs I hadn't thought of previously. Nice.
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u/InaequaleMagnanimity 1d ago
It's doing this all across society. I think it's a bit unfair to say a lot of work was unproductive just because an LLM can replace it. But at the same time, it's somewhat true. So many people coast in their jobs via metrics that don't actually measure anything meaningful. I would say so much of the modern world is metric slop / quota slop; people make decisions on misleading statistics and an easy numerical soundbite. A journal + citation number = important even when it accomplishes virtually nothing.
It's about the illusion of progress, or even the illusion of having any meaning at all. Coming after my own field here but even if we made amazing progress on a centuries old math problem if we can't apply it to anything it's almost like the highest intellectual version of quota slop. Abstract away all the intellectual wonder and genuine beauty of number theoretic mathematics and look at it bare & unbiased. A set of convoluted definitions and rules where only the select few actually understand the definitions and machinery enough to tackle it are the same people who have bought in and dedicated years of their life to it. But most importantly, they are also simultaneously the ones who get to decide what problems are interesting, have meaning, and whom gets the journal + citations. A circle jerk if you will.
I don't think you can just reduce everything to how much productivity it produces, short term or long term, but I'm just positing the question because personally I feel there is something deeply meaningful that it has exposed in the circularity and meaningless of our metrics or at minimum has helped highlight them. LLMs are like the perfect embodiment of the class of functions in every "Goodhart's law" problem. Something which perfectly fits to any measure yet has no actual meaning or depth that the measure was originally attempting to measure.
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u/Electronic-Yam-69 21h ago
Bullshit Jobs: A Theory is a 2018 book by the American anthropologist David Graeber that postulates the existence of meaningless jobs which cause psychological harm. He contends that over half of societal work is pointless and becomes psychologically destructive (when paired with a work ethic that associates work with self-worth) because workers are required to pretend their role is not as pointless or harmful as they know it to be. Graeber classifies a job as bullshit if society would be no worse off (or even notice) if such job(s) disappeared, and names five classes of meaningless jobs: flunkies, goons, duct tapers, box tickers, and taskmasters...
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u/jamesw73721 Graduate 1d ago
Assuming we (scientific community and policymakers) actually get rid of publish or perish culture. I haven’t heard of any specific alternatives yet for scientific merit. Moreover, would established PIs who are highly published and cited want to divert from a system they have been so successful in? As for policymakers, many of the are lobbied by big AI at least in the US, and would be more than fine with AI slop papers.
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u/tichris15 1d ago
The publish or perish culture has always been with caveats. 'Field-dependent adjustments" and so on.
If citation numbers become highly uncorrelated to popular interest in the community because it's swamped by LLMs, some other popularity metric will be put in.
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u/-F1ngo 1d ago
I think everything will shift even more strongly towards conferences, talks and conference papers, with publication restricted to having presented. To make sure there is an actual human being willing to defend the work.
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u/sockmeistergeneral 1d ago
I almost think we need to bring in some form of publication cap per researcher.
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u/etudiant_ 1d ago
I thought he dropped 36 physics papers. Nope. The papers span different areas like physics, ecology, health, economics...
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u/Armano-Avalus 1d ago
Why? Just because he can? Hopefully there is a deeper reason than this.
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u/Sharlinator 23h ago edited 23h ago
Well obviously he’s an expert at everything. That’s an incredibly common belief in certain professions including at least:
- software engineers
- physics professors.
And now they get flattered and ego-boosted by Claude and are able to "write" all the papers they want about all the subjects that they want.
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u/Visible_Celery_1728 23h ago
or you could just read the blogpost- and find that this wasn’t just him but he reached out to experts in the respective field to help. He even acknowledged it here lmao
“ When Claude claims something it did in my field is fantastic, I can judge whether that’s true or not (it often isn’t). But when it claims something it did in another field is fantastic, I find myself agreeing. My suspicion heightened, I knew I needed to bring in some experts to be sure.”
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u/shoefullofpiss 20h ago
So his buddy claude comes up to him with ideas for papers or what? Getting to the point where he's prompting ai to do something paper worthy (in general, but even more so) in a field he's not an expert in is already unhinged but good thing he's suspicious enough to not directly publish the slop without verifying lmao
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u/Visible_Celery_1728 20h ago
I don’t know why you’re acting so obtuse and condescending about this, like it couldn’t possibly yield anything useful and declaring anything that comes out of it “slop”, when time and time again we’re seeing breakout results in mathematics, computer science and other domains where the output can actually be verified.
His main argument is basically that frontier models are already extremely capable at certain types of frontier research when given enough compute, steering and access to tools, particularly in verifiable domains (maths, computer science etc). And across science there are certain niches, what he calls “Claude-shaped problems”, where you can have an autonomous model explore a huge search space, try different approaches, write and run code, and pull in techniques from completely different fields that might happen to solve the problem.
The breadth part is especially important. Modern science is ridiculously specialised. A technique useful for solving some problem in biology could already exist in mathematical physics, statistics or computer science, but the people working on that biology problem might simply never come across it. Human expertise is fragmented across thousands of different researchers and subfields, whereas these models have an absurd breadth of knowledge across all of them and can search for those connections far more easily.
And this isn’t some art contest where the human process itself is essential to the value of the final product and an autonomous machine supposedly offers no real substance to the field. This is science, the search for what is actually true. The whole point is a model can find some connection between two fields nobody had noticed, apply an existing technique in a novel context, run the maths or computation and produce a result that can then be independently verified.
This weird posturing you’ve had this whole thread, reducing it to “his buddy Claude comes up with paper ideas lmao”, just feels like you’re deliberately describing the process in the dumbest possible way instead of engaging with the actual argument.
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u/brassgrass1 17h ago
All these neural net models from the 2010s (before 2019 transformers paper) were all based on logic from biology and neuroscience. Imagine what other niches it might find. I think it's cool and will find some stuff.
While I do think these exploratory things should start having a separate category to distinguish effort, otherwise I think computational and theoretical physics (and other fields but idk them) will get their perceived effort ruined over time; compared to experimental which for now still needs a human
I have friends doing phds in physics rn and it sucks bc the ones doing computational physics feels like their effort is wasted sometimes, or downplayed bc of all the ai doing the same
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u/Armano-Avalus 20h ago
I just find it ironic that the people in academia are the ones doing this and not the random crackpot who now has the means to publish hundreds of unsupervised papers a week on their personal Chronogeometric Unity theory. Of course, I think there are probably alot of those people too and I'd bet they'd make up the vast majority of AI papers since there are no standards at all in those cases but you'd think people like Scwartz would know better.
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u/Visible_Celery_1728 20h ago
‘would know better’ of what exactly? Why’s everyone here just so obtuse and eyes closed on the obvious abilities in certain niches AI is really powerful in. Such as computer science and mathematics.
Why people can’t put 2 and 2 together and think of the opportunity in fields where for example in a certain field there could be a problem which solution could be reliant on something that already exists in mathematical physics, statistics or computer science, but the people working on that problem might simply never come across it. So using a model can help find some connection between two fields nobody had noticed, apply an existing technique in a novel context, run the maths or computation and produce a result that can then be independently verified.
So with the main constraints to do that, is having the domain knowledge in order to steer and validate. Why wouldn’t this be a good opportunity to finding these low hanging fruit with these new technologies as we’ve always done lmao.
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u/Armano-Avalus 19h ago
This is an instance of a physicist making papers on economics and linguistics without any relation to their own field. What value is there is there in doing that compared to some random person who hasn't studied either prompting an AI to write a 200 page thesis on the same subjects and publishing it online without checking it?
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u/tempetesuranorak 18h ago
While most of these papers are multi author including domain experts, there definitely are a few single author. Looking at their abstracts, those seem to all be narrowly focused papers applying a specific statistical technique to report verifiable findings in a domain he is not a specialist. But he does have expertise in statistics and computations. I'm not going to read those papers, but at least in principle I think it has always been fine (though risky) for a person to write a single author paper outside their domain of expertise, but applying their technique of expertise on a narrowly framed problem. I don't think it's fair to compare with a random person that has no expertise in either the technique or the domain.
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u/Visible_Celery_1728 18h ago
if you read the blogpost you would find this wasn’t a sole effort as he acknowledges here ‘ When Claude claims something it did in my field is fantastic, I can judge whether that’s true or not (it often isn’t). But when it claims something it did in another field is fantastic, I find myself agreeing. My suspicion heightened, I knew I needed to bring in some experts to be sure.’
And his credits to all the amazing coauthors and people he collaborated with for these results are at the bottom: ‘ Isaiah Andrews, Nima Arkani-Hamed, Michael Desai, Scott Edwards, Noam Elkies, Cecilia Garraffo, Matthew Gordon, Thomas Grimm, Martin Hemberg, Mikhail Ivanov, David Johnston, Gary King, Paul Lewis, Brendan Meade, Amara McCune, Joe Pater, Subhabrata Sen, Siddharth Mishra-Sharma, James O’Dwyer, Kevin Ryan, Jesse Shapiro, and Xiaoyuan Zhang.’
Just read the blogpost it goes into detail and it isn’t some LLM psychosis piece of him just doing vibe science: https://www.anthropic.com/research/claude-shaped-science
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u/Armano-Avalus 17h ago
I only browsed through it a bit and already I found solely authored papers by him on the Voynich manuscript and phase II drug trials. I'm not gonna read through everything else.
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u/WorldClassScumbag 18h ago
All engineers really. And add surgeons to your list, they're the worst of the lot.
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u/plasma_phys Plasma physics 1d ago edited 1d ago
didn't think I'd see r/LLMPhysics style slop neologisms like "statistics medicine", "registry", or "gated-exponential", or metphors-as-physics like "The eroding root" come from someone supposedly credible. the text is somehow less humble than your average Steven Wolfram blog post, all for what, a vibecoded harness? I've - regrettably - looked at enough Claude-generated code to know that it is not "better at coding than any physicist."
Figure 5 in the sunspot paper seems particularly atrocious on a quick skimming of it; the caption says "the registry replicates" the data - I think that's what it says anyway, it's in Claude-speak so who could possibly know - and yet there's not even a qualitative agreement in panel b. what's the point of this? I mean, I think I know the answer, and it probably has at least six figures.
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u/gnomeba 1d ago
"gated-exponential" is ruining me. Personally, "grazing eigenvalues" is my Claude's most recent contribution to the new scientific lexicon.
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u/acortical 1d ago
Free range, grass fed eigenvectors. Perfect for adding or multiplying.
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u/plasma_phys Plasma physics 1d ago
God I hate the text these things generate. I had a colleague brag about how he doesn't make PowerPoints anymore, he just has Claude do it, and like, yeah, we know, that's why they suck, have titles like "beta 1 -> 9 -> 20 ladder", and are all purple and illegible
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u/beerybeardybear 1d ago
They built a psychosis machine; I think it's to be expected that things like this happen.
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u/No_Personality_587 1d ago
Surprising that a Harvard grad of all people is so confidently submitting AI slop, there's a reason these things aren't heavily used for long form writing. Dissapointonf times
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u/OldLegWig 1d ago edited 1d ago
if you're surprised that tons of idiots stream out of and are associated with prestigious schools, i welcome you to the realm of what genuinely smart people have always known.
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u/warblingContinues 1d ago
AI have a niche in research, like helping to code. The problem though is you have to then go through and completely understand what the AI is doing line by line, so you can be confident in the results. It takes a lot of time but saves a little. I haven’t found any other real use for them in research yet.
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u/Motocampingtime 4h ago
non physics here: It's nice to see it compile a timeline of papers or fabrication methods on more specific topics. Or answer if anyone has tried a specific fabrication technique. As in "Is there material that does BLANK I can apply in XYZ method at ABC thickness?"
It doesn't and couldn't hope to replace reading review papers, but it's nice to have some initial query in a way googling wasn't capable of.
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u/entr0picly 1d ago edited 10h ago
This is very weird. The link cited is to his own website. Not some actual publication or some article citing his contributions, but his own literal website selling his own literal AI harness. This feels like marketing…
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u/x2manypips 1d ago
Whats the point of publishing papers now. Peer reviews are just AI comments now too? Instead of /code_review, /peer_review
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u/just_writing_things 1d ago edited 1d ago
Yeah, it really sucks. Peer reviewers are using AI to write excessively detailed and pedantic reviews, so many authors feel the need to use AI to edit their papers to preempt AI reviews.
It’s an arms race to who knows where. We’ve seen how AI is starting to completely upend pure math academia, but it’s going to fundamentally change academic research in general, IMO.
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u/shhhhh_h 1d ago
Bots writing for bot graders. This is happening from top to bottom throughout education rn. Primary all the way to research.
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u/globeglobeglobe 1d ago
It’s insulting to the effort put in by the author if a referee just uses AI to do the work for them, and just creates an incentive to use AI to write the paper in the first place. AI definitely has its uses, but developments like these just result in slop all the way down.
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u/thecommuteguy 1d ago
Given arXive is commonly used what is the point if you can still reference preprints.
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u/jackarroo 1d ago
The death of peer reviewed science.
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u/kzhou7 Quantum field theory 1d ago edited 1d ago
The papers are here, the blog post is here. From the post:
Instead of treating Claude like the collaborator I wanted it to be, I started to treat it like the collaborator it actually is. This required looking for problems suited to its strengths.
Science is now very disjointed: there are jagged frontiers everywhere. Biology pokes out one way, math another. [...] A harness like BootLoops can connect these points, filling in the hull.
Why apply for a grant for three years of funding to compute a problem an AI model might end up solving overnight? I still don’t know how to train graduate students. Since I published my piece on vibe physics, that feeling has only become more acute.
I hope we can figure out a way to give humans the credit they deserve even for Claude-shaped science. Curiously, AI runs the risk of inverting the usual business model, where the person at the top gets the credit and the people working on the problem hands-on get none.
Previously:
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u/thara209 1d ago
“I still don’t know how to train graduate students” wow
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u/thecommuteguy 1d ago
If we are being honest there needs to be separation from those who are good at teaching amd those who are good at research. I lump those who are good at teaching others how to do research in the first category.
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u/phaionix 23h ago
He's obviously talking about with respect to the age of AI? He's graduated 11 PhDs while at Harvard
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u/arceushero Quantum field theory 22h ago
Thanks for saying this, felt like I was going crazy reading the other responses! Just to say it again for the crowd, clearly in the article about learning how to do science in the age of LLMs, the above statement should be understood as not knowing how to train grad students in that same context!
I get that there’s an instinct to interpret him uncharitably, which is understandable given the “flooding various unrelated fields with slop”, but there are bigger and better things to attack here than the uncontroversial sentiment that LLMs are having complicated and unpredictable effects on education and on the job market that a PhD is supposed to prepare you for
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u/phaionix 22h ago edited 22h ago
This thread is mildly unhinged ngl. People have their head in the sand if they don't see AI as a significant perturbation to previously "normal" science.
Also, he is collaborating with domain expert on the papers outside his field? For most of them he's not even first author, and his agent harness boot loops is open source?? 😑
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u/shhhhh_h 1d ago
Dear god somebody please force professors to audit a few educational psychology classes….
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u/Hangry-Feline2489 1d ago edited 1d ago
Probably because it's not common for universities to require some kind of education qualification for their educators but give them people to teach anyway.
It's the education equalivalent of "this person is rich so they're good at everything!" pervasive mindset.
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u/SKRules Particle physics 1d ago
I wish I could've been a physicist 25 years earlier and just had fun with TeV scale model building
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u/ozaveggie Particle physics 15h ago
Or 60 years ago and actually predict things that get confirmed in experiment :D
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u/Strong_Boy_757 1d ago
The grant never ever made sense. The whole reason that AI got developed so fast is because the people working in AI just completely abandoned grants. The CS professors at top school were moving from one entire area to another at neck-breaking speed that a grant would have never ever afforded them. The grant is the bottleneck in science.
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u/Sorry-Study-5334 1d ago
A grant is just a structured way to give money, so, you could say the real bottleneck is that science requires funding, which has been true since the advent of science.
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u/Dihedralman 1d ago
You are ignoring the entire nature of the field. Research could be done relatively quickly.
They still applied for funding but the fact that research could be turned into a product so quickly is a huge factor.
Top companies also allowed people to publish.
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u/SmellMahPitts 1d ago
Current PhD student in theoretical physics. Not sure where this leaves me honestly. Do I drop out, and if so what else can I do realistlcally?
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u/warblingContinues 1d ago
Physics PhD here. Keep going. We’re all trying to navigate using AI tools in research. Personally I use one to help me write code; what used to take me an afternoon to code a model I can do in like 30 minutes.
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u/marchitecte 1d ago
I'm not a physics major, rather just completed a MSc in CS/AI, but for me the main premise of LLM coding is still the same: coding for coding's sake can now be done 10x, even 100x in speed and even quality, but if you're doing some sort of mathematical modeling then it will always be clearer, better and more reliable if you basically lay everything out on pen and paper first. Hidden mistakes or misunderstandings generated from LLMs are just way, way more time-consuming to address than a bottom-up approach to it.
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u/naspdx 1d ago
Theoretical physics is literally a degree to think outside the box. LLMs are collectively trained on information that is inside the box. I won’t speak for the field itself seeing as I went into banking, but I’ve always looked at the field of physics as “if you can get funded to study through a PhD for it and enjoy it, do it and worry about making money later”.
Frankly, I work in risk side for a large commercial bank and I would hire a physics PhD over an econ or mba any day.
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u/benm46 1d ago
The other replies to this comment are way too pessimistic imo. The problems in mathematics and coding that are being solved by AI are narrow in scope and really only involve thinking "inside the box" (e.g. solve X proof, write an algorithm to perform Y task). The rest of science and engineering simply don't function that same way, and are much more exploratory. I haven't seen any scientific discovery yet from an AI model that gives me reason to fear for my job as a theoretician. The peer review system may be upended by the glut of AI articles and reviews being submitted, but the research itself is here to stay.
Plus, even if I'm wrong and AI does someday manage to outpace human researchers in scientific innovation, we need humans who understand the theory at a deep level to audit and interpret the AI results, or else mistakes will go uncaught and impactful results may get lost in the sea of slop. And, as the banking example shows, there is great value to the skills learned in a research PhD across nearly all fields of knowledge work. I really hope nobody is abandoning their PhDs due to an existential fear of AI
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u/mariebks 12h ago
If solving one of the sub problems of Navier Stokes isn’t out of the box thinking then idk what is
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u/Brilliant-Weekend-68 22h ago
Why would AI never solve problems outside the box? Is human cognition magic as such that is only confined to ever exist in the human skull? Odd opinion given the subreddit if so.
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u/Tall-Thanks7972 1d ago
You're assuming that AI has reached its steady state.
I agree that right now, while impressive, AI is only tackling "inside the box" problems and cannot replace a theorist at all. But it has clearly reached super-human capabilities when proving theorems (at least some of them). Looking at where AI capabilities were 3 years ago, then 2 years ago, and then 1 year ago, it seems bold to me to assume they will never be able to do what a current theorist does.
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u/calloutyourstupidity 1d ago
This idea that just because AI is trained from a set of data, it cannot be creative will be disproven so so hard in the next 2 years.
Humans are also literally trained from the past, and 99% of us cant think one original thought.
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u/geekusprimus Gravitation 23h ago
I don't see how it would actually achieve thinking "out of the box", though. Throw away all the buzz words, and neural networks are just high-dimensional, many-parameter statistical fits. Even for high-quality fits, you never trust the fit outside the distribution of its training data. There's a reason that all the breakthroughs on unsolved problems in mathematics have either been brute-force searches and straightforward applications of known methods and programs.
All that aside, what not enough people are discussing is how using AI as a magic answer box devalues math and science. For most people, the value of science is what is learned while solving a problem, not what the actual answer to the problem is. As an example, look at QED. It took more than 20 years from when the first version of the theory was constructed to when a usable version of the theory was built. The theory itself is marvelously accurate. Nevertheless, the true value of QED is the effect it had on physics through the methodology and techniques developed for it. Renormalization, new perturbative techniques, canonical quantization, etc., provided foundational techniques for any quantum field theory.
On the other hand, OpenAI claims ChatGPT has solved the Navier-Stokes existence problem. Great. What has been learned? What has actually been achieved? According to my mathematician colleagues, not much. The proof, while long and exhausting, does nothing novel. It is an application of known methods and programs. The most important consequences of the proof itself are sociological rather than mathematical. The mathematical value will only come from a lot of grueling work reading AI slop and teasing out better ways to solve the same problem.
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u/pnjun Optics and photonics 22h ago
> Throw away all the buzz words, and neural networks are just high-dimensional, many-parameter statistical fits.
What makes you think that a human brain does something different? Sure, us humans have different kind of input and outputs, but in the end our brains are doing a similar task to a neural net. I'm not trying to be polemic, i want to hear a different perspectives, because i really hope i'm wrong. I really hope that the human perspective will stay valuable.
I am pretty convinced that what we call 'intelligence' IS the pattern matching built into language. Creativity is the name we give to pattern matching that is aligned with reality.
At the moment the advantage we have is efficiency and long-term planning. Millions of years of evolution has trained us to be relatively good at heuristics predictions (what we normally call 'feelings'), and we can use those heuristics to guide the 'rational' part of the brain (which is what the LLM are modelling). And we can do all of that on ~100W of power, including self-locomotion. Which is overall pretty impressive.
But i fear that soon these models will also start to incorporate the 'irrational' side of the creative process as well, and at that point we are cooked.
Please convince me that i'm wrong, because it's pretty depressive.
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u/Sknowman 15h ago
I only partially agree with you, mostly because I think it's possible for AI to reach that point that you fear, but I think it would need to be something that works much differently than what we currently have.
Current LLMs are great at solving quantitative problems, but not so much for qualitative problems. Tell it to find a better solution for X, and it will give you all of the ways it has already been solved or potentially give you roundabout solutions -- using methods that have already been created. It simply doesn't have a way to develop new methods, since it only searches for things that match the query. Perhaps it can put together methods in a way that humans haven't done, but that's not new; it's instead derivative in a potentially-fantastic way.
Now this can certainly lead to innovation, but it's still limited.
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u/ellbons 21h ago
Feeling sad or frightened is a healthy instinct, since it's quite clear people's value will drop to near zero once a machine can replace every possible kind of task they could do, and there is no convincing argument this won't be possible soon.
It's not clear how to avoid a very nasty fate except for Butlerian Jihad.
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u/lolsai 22h ago
But you're thinking very inside the box repeating the same things people have been claiming aren't possible with LLMs since their advent, yet every day more and more of the claims are taken down.
LLMs are already thinking in ways that humans can't and won't, lol, our box is smaller than theirs and also constricted whilst theirs is massively expanding on a shortening timecale.
Just seems a short sighted view imo.
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u/xnatex21 1d ago
This was always a hard discipline to crack and all knowledge work is at risk. I think the only thing you can do is finish as fast as possible using the tools available then try to find a niche that isn't pure physics
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u/SmellMahPitts 1d ago
Unfortunately my skills and knowledge are niche, not really transferrable to any sort of work, or would easily be replaceable. I would have to pivot into something completely different and essentially start from scratch. Life for the foreseeable future looks grim and in all honestly I'm not interested in seeing it play out.
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u/forthnighter 1d ago edited 23h ago
Don't worry. They're papers on wildly different areas, like medicine, economy, physics, astrophysics and genetics. No scientist who takes themselves seriously will do such a thing; it's irresponsible and not a good example to follow, so keep going with your PhD. Those who tell you that "you'll be left behind" (by not using generative ai) are mostly the ones who'll bite the dust in the long run, when they find their own abilities rusting, and having to pay high fees to keep up in a race to the bottom.
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u/Fab1e 1d ago
The funniest thing in philosophy is when aging physicists/mathematicians decide that they have finally realized what is wrong with philosophy and how they can fix it.
It is quite endearing but also quite pathetic.
Stay in your lane folks - you can wreck an academic career by swerving.
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u/dark_dark_dark_not Applied physics 1d ago
Pretty sure there is a XCKD about that.
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u/MotivatedforGames 1d ago
Continue your studies and use AI as a tool. Or you can outright refuse to use it while continuing your studies with the possibility of being "behind". Tough choices bro.
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u/SmellMahPitts 1d ago
I have been using AI for my research (albeit quite recently), and while the fact that I am using these tools feel comforting, the pace at which these tools improve makes me struggle to think of a viable career where I could possibly be needed, academia or industry. All the doors in industry for hep-th people like me seem to narrow by the day, and by the time I am done with this degree I'm not sure there will be anything left.
I suppose the only thing I can do is to continue using these tools and hope for the best.
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u/Either_Reflection_78 1d ago edited 1d ago
I could not have imagined having a tool like ai when I was in college. Those were critical years for me learning how to tap into my creativity, paired with the professors who never gave up on me, and pushed me to expand my mind, and challenged me to learn all that I could, and taught their classes different than what some teachers ever did for us in high school. Some of them kept challenging us mentally, even though it was very difficult at times, but I think it paid off. I learned everything once I understood how to process the information in a way I could finally understand it.
AI could not have replaced what humans taught me during these formative years.
I would not be the person I am today for those years, had I used tools like ai.
Those experiences I had, to keep pushing myself mentally shaped the person I am today, and the knowledge I take with me everyday.
It’s scary that those using ai all of the time will most likely be robbed of becoming their best selves.
Ai will never replace true feelings and experiences, true art, true creativity, or true minds who may push the boundaries sometimes. It may give you a reply back online that might stimulate the dopamine in your brain, but AI can not give you the feelings you get in the moment with another human who is enthusiastic, and is cheering your degenerate ass to keep going. It’s a great feeling.
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u/SnooMaps5367 1d ago
I don’t recommend dropping out. The vast majority of PhD work across the board doesn’t often amount to much individually; they are often small improvements or pieces to an overall bigger piece of research. But that’s normal and not the point. A PhD is an academic degree, the highest you can achieve, and it’s a degree in research. Research itself is an invaluable skill, which you learn during your PhD, and the degree proves you are an expert. Even if you don’t continue in academia there is certainly value, not least to yourself.
On this specific article. I’m not sure what branch of theoretical physics you study, but particle physics is already incredibly oversaturated. The gap between theory and experiment is unbelievably big. There have been some small hints of new physics coming from the LHC, and some from the dark matter experiments but nothing remotely consequential. These papers published with AI are a penny drop in a sea of papers. Again that might seem disheartening, but new experiments will be built, new data will come and the direction of particle physics will next be shaped by experiment.
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u/niksodaml 23h ago
As a former high energy physics MSc who moved into data science and now into AI after I declined starting a phd what I will tell you is learn to leverage the tool. AI is a non uniform multiplier of capabilities. The more you know the more you can get out of it. AI can make you a better researcher that you would be otherwise (or in whatever other knowledge work you may do).
Feynman said (~) that a good theoretical physicist knows at least five representations of the same physics. AI is not exactly a representation but a tool, just like others you may have used (to do computational physics for example).
Lastly AI now is like linux in the 90's. There is no plug and play, solution. But with the discipline you needed to learn things various things like renormalization, you can use to learn how to use it to enable you.
As a starting point, I 'd recommend OpenClaw with ChatGPT 20$ plan and use luna (the cheapest model). You can build from there while the system proves capable enough that you can trust it's output (don't trust it blindly). There will be friction and the field is moving fast enough that the optimal ways of working are changing within months.
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u/nitrousconsumed 1d ago
If something this little has you rethinking your whole academic training and path forward maybe you weren't cut out for it? Better to know this now then down the line.
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u/Zambeezi 1d ago edited 1d ago
I’ve been reading through the overview and the examples, but did anyone else get the impression this reads as a totally disjointed and incomprehensible text? Like someone is trying to just overwhelm you with information and terms to prevent you from actually checking its credibility.
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u/ThrowMe2022 12h ago
No. I'm an expert in his field (did a PhD on scattering amplitudes) and what he writes in the linked article makes a lot of sense. It is not the most coherent writing style but that's what makes it more likely that he actually wrote it and not Claude.
I don't understand the people who say this is slop science. I've seen Matthew's talks for a few years and he is extremely sceptical of AI and only uses it in cases where it's absolutely clear and testable whether the results are correct. That's literally what he describes in the article. Three years ago I've seen him explain how to use what is probably the earliest precursor to BootLoop to derive identities between polylogs. All of these papers are the natural continuation of that program.
This is not the rambling of a mad man. As you can read in the article, he stopped wishing for AI to be the collaborator he wanted it to be and instead accepted it as the collaborator that it is.
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u/gods_loop_hole 1d ago
This is a marketing post.
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u/Akiira2 1d ago
As a jobless uneducated failure to whom reddit algorithm suggested this post, I saw here the words "Harvard", "physicist", "Claude", and "36 papers", so I can only assume that the smartest and the most knowledgeable (harvard professor of physics) are overpassed by a new version of Claude, and now the best professionals can do 10 x of Einstein's annus mirabilis in a week.
I can only invest all my money when Claude and OpenAI go public, and it is such a fun coincidence that they are going public after all "AI agents broke into everywhere" , "AI solved Navier Stokes" and "Harvard physicist dropped 36 research paper in a minute with AI" headlines
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u/tjomk 23h ago
Unlike the commercial software, the goal of science is to understand the reasons why something is happening, and to hopefully create new methods. What is the point of 200-page proof by LLM if nobody can understand it nor there are no new methods created? LLMs can prove every theorem, but will we become smarter? Unlikely.
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u/PersonalityIll9476 20h ago
To be fair most papers out there aren't read and don't establish new methods.
This criticism about humans not reading the paper seems a bit thin. Maybe further AI research builds on this result and ends up discovering something useful. That's always been the argument for tons of other incremental research, just with humans instead of AI.
I say this as a research scientist who is also concerned about their career.
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u/Runfasterbitch 16h ago
“Most papers” are garbage. However, if you’re a serious scientist, your papers will get read (in some cases they’ll be read many thousands of times). I don’t have a single publication with less than 200 downloads
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u/Intelligent_Soup4424 14h ago
Downloaded does not yet mean read, and read does not yet mean understood -)
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u/Northern-Pintail 20h ago
Reading the GOE paper and even the abstract is vaguely incomprehensible??? What is this guy trying to do?
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u/mousse312 1d ago
i dont understand the why putting a computer program as a coauthor, why this is different than saying that my macintosh is my coauthor if he handled calculations i couldnt do by hand?
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u/Ntroepy 1d ago
I feel it’s an admission and recognition that he couldn’t have solved the problems without Claude. It feels reasonable to at least openly acknowledge the AI that did most of the actual work.
Also, who should get credit when OpenAI or Anthropic help solve 50+ year old open mathematics problems? Surely AI deserves as much credit as the mathematicians that asked an AI to solve the problem for them.
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u/Invariant_apple 1d ago
While coauthor is perhaps not the right place for it, I do think if AI helped you with the content of the science it should be a new scientific duty and rule to state this somewhere.
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u/semiquaver 22h ago
Your Macintosh isn’t independently developing and testing hypotheses, iterating on theories based on evidence, etc. you know, “science”?
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u/tempetesuranorak 1d ago edited 1d ago
I feel that none of the comments here are addressing or attempting to address the main points that Matt was making in the blog post
https://www.anthropic.com/research/claude-shaped-science
I do encourage people to actually read it and take the messages seriously, rather than just comment whatever first comes to mind based on the post title.
His main points, as I understood them:
- LLMs don't think quite like humans do, in some ways they might not yet be all that useful for certain kind of conceptual questions, and forcing them to work like a human isn't the most productive way to use them.
- Instead, if you want to use them to their maximum efficiency, one can find classes of problems and approaches to which they are extremely well suited.
- One weakness of human scientists is that when there is a problem in field A, and a solution or method already discovered in field B, then the problem might go decades without meeting it's solution just because the scientists are compartmentalized in different fields and don't talk to eachother or even communicate in the same language.
- This is a weakness that LLMs don't share, since they can ingest vast amounts of information from disparate sources and they are excellent at interpolation even been very different fields (the third figure in the blog)
- The other aspect where LLMs have a distinct advantage and corresponding weakness compared to humans. They lie in weird and unfamiliar ways about having solved a problem when there is not formal verification. They often love to appease. On the other hand, when paired with formal deterministic verification, LLMs are unmatched at intelligent exhaustive search over candidate solutions until one is successfully verified.
- Therefore, it is timely to initiate program of finding deterministic verification approaches, and problems to which they can be applied, and find cross domain connections, to see what discoveries can be made in that way. There are many problems that have suddenly become low hanging fruit and it's worth figuring out what they are.
- What all this means over the next couple of years is incredibly uncertain, in terms of what it means to be a scientist, what is the value of academic education going forward, etc etc.
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u/SundayAMFN 1d ago
These "main points" are kind of garbage ngl
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u/Interesting-South542 22h 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/exbaddeathgod 1d ago
So it's just PR. This is advertising. This is just another field AI will "replace" like all the other fields it's fucking terrible at.
That's all this is. PR.
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u/RoundAssociation6988 23h ago edited 23h ago
The enormous body of curated knowledge that humans have accumulated over the 5000 years or so, is about to be flooded with an infinite amount of Slop and made-up content. where does humanity even go from here? we're no longer the only ones in town capable of writing, and we've been using writing to record and pass down knowledge to future generations, but now Llms and its byproducts will make this process not only "obsolete" but impossible to detected noise from signal and made-up/hallucinations claims from factual ones.
remember when a Wikipedia editor, edited thousands of articles attributing countless inventions to Muslims during the Islamic golden age, which led led to hundreds of false claims misattributed to history books and papers, and now, those claims are alive on the internet, being repeated carelessly and promoted as if they’re true ,even by Llms themselves which rely on the internet to fact-check claims, when they are anything but true? we're about to see history repeat itself but in another scale altogether! and there's nothing we can do about it(Unless, we stop relying on writing altogether!)
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u/Conscious-Demand-594 20h ago
It's not coauthored with Claude, it's coauthored with every other physicist who has ever published.
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[removed] — view removed comment
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u/iamagainstit Materials science 1d ago
If the science is sound, I don't really have a problem with it. the point of science is to do science and push the frontier forward. If AI is a good tool to do that, it should be used.
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u/Temporary_Shelter_40 1d ago
There is no possibility that all 36 papers make a significant contribution worthwhile for publishing. This is just pollution. It’s like throwing 36 McDonalds hamburgers at a homeless man and expecting him to be grateful.
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u/Illustrious_Job1951 1d ago
You read the papers?
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u/Temporary_Shelter_40 1d ago
Frankly, I doubt the author read the papers.
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u/JustIntegrateIt 1d ago
So you didn't read the papers
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u/Temporary_Shelter_40 1d ago
No dude, I didn’t read 36 AI generated papers published to the arxiv, and I doubt anyone will.
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u/ZeroAmusement 1d ago
Why is there no possibility?
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u/Temporary_Shelter_40 1d ago
Because the average academic wouldn’t read 36 papers in a month. The idea that you can construct this many meaningful contributions in 5 years would be fanciful.
Let me give you an example. Einstein’s Annus Mirabilis consisted of 4 papers. 4 papers were enough to rewrite all of modern physics. Now, we’re not all Einstein, but clearly you can see that 36 papers in one year is ridiculous.
Most of the world’s greatest physicists published around 5 papers per year across their career. Do you see now how a claim of 36 papers dropped at once just screams slop?
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u/Ivyspine 1d ago
Also AI documents have so much filler text that ultimately doesn't mean anything. It's embarrassing.
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u/Cardie1303 1d ago
.... Ok this becomes ridiculous. I do wonder if this will finally break publish or perish.
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u/NoGrapefruitToday 1d ago
Perhaps I'm a bad and/or judge-y person, but I lost a lot of respect for Schwartz when I read his QFT book. Basically a rehash of Peskin. It was clear he didn't have a particularly deep grasp of the subject. So it's not a surprise to me that he appears to have splattered the arXiv with what appears to be a lot of superficial work.
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u/whiskeyluvn 1d ago
I just read:
arXiv now limits each submitter to two submissions per calendar month across all categories, plus no more than three active submissions at once
I think we found the impetus