Matthew Schwartz of Harvard has made a big recent splash, between his public Aspen talk "10000 Einsteins" a year ago about the role of AI and the future of physics, and his talk last week at the APS Global Physics summit on the same topic, and now with this essay, "Vibe Physics: The AI Grad Student", on the website of Anthropic (producers of the AI tool Claude).
The essay talks about how Prof. Schwartz used Claude to write this paper, and he states that the AI tool functions roughly like a 2nd year grad student (one who also doesn't get tired or complain, but does need close checking and supervision). The claim is that with this approach to doing calculations and writing papers, he was able to come out with a piece of work that would've taken literally ten times longer if done by working with a human student. Note that he's not exactly unbiased, and he concludes his essay (on anthropic's site) saying you should spend the $20/month Claude subscription fee and it will change your life.
There is no doubt that AI tools can speed up certain kinds of work, and there is a every hope that applying this in science will lead to increased pace of progress. That said, right now these tools are (unsurprisingly) best at working in areas that are well-known and explored - one of my colleagues has tried applying these to really underexplored higher dimensional problems, and they're much less effective there. The essay's claim that "LLMs are profoundly creative" is provocative. There is also no discussion here about the cost of these tools, in financial, energy, and environmental terms.
Still, Schwartz raises many questions about the future of the field and graduate education in general. (His paragraph about how human beings will still be needed in science for getting experimental data, at least for a while, is really something.) University research is not just about answering scholarly questions; it's about educating people. Maybe some faculty will revel in writing papers without that kind of interaction, but somehow I don't think we're quite at the stage yet where we don't need to worry anymore about training experts in technical fields. I do agree that it's good advice for everyone to pay close attention to where these capabilities are going. We certainly live in interesting times.
4 comments:
I saw Schwartz's talk. He emphasized the exponential increase in the brainpower of artificial intelligence in recent years, with an exponent something like 10,000,000 times that of biological intelligence. But it seems to me that the improvement will only continue until AI trainers can no longer outsmart the AI, at which point the AI would plateau. Maybe finding a new factorization theorem is something where AI can interpolate from its current training data and succeed through a brute force application of its knowledge, but there are plenty of other problems in physics where you need to think creatively because the most straightforward approach won't work.
1. He had to interact with the AI an absurd amount of times and correct its hallucinations and nonsense constantly. He also uncritically links to Steve Hsu's AI slop paper (which is fatally wrong, see https://www.math.columbia.edu/~woit/wordpress/?p=15362)
2. How many problems did he try it on before he found one that worked?
3. The AI that "solved" research-level math problems likely had them in its training data (https://decrypt.co/302691/did-openai-cheat-big-math-test). Perhaps something similar happened here too.
When will the AI bubble burst? The sooner the better. Maybe the oil shock will do it.
Schwartz is correct that theoretical physics has mainly advanced through solvable toy models. What he leaves out is the fact that most breakthroughs in the 20th century involved either zero- or 1-dimensional models. BCS theory is effectively zero-dimensional, as is random matrix theory. Due to holomorphic factorization, 2D CFT is effectively 1D for many purposes. Bethe ansatz-solvable integrable models can be properly 1+1-D, but computing most observables (correlation functions) is extremely painful and is largely bypassed by 1D DMRG.
For many systems in higher dimensions, though, the toy models, if they exist, likely need 2 or more effective dimensions. Basic computational complexity limits mean that LLMs or any other classical computing approach cannot straight-forwardly solve most complex problems. Whether there are nontrivial, "integrable" structures relevant to physics in more than 1+1-D that are also somewhat tractable is not at all clear, and also not amenable to brute-force search.
I have not seen or read the source materials, but I will comment on a few points mentioned here that seem to come from Matthew Schwartz.
- The "2nd year grad student" is assumed to be a fixed entity, but it is not. A second-year grad student from 100 years ago can not tackle some research problems that a second-year grad student now may find ordinary. Why? Because we have better tools now (eg, spectroscopy machines, computers, better telescopes, etc). I am surprised that a Professor from Harvard missed this obvious point.
- It seems to me that his worldview is that the primary responsibility of a Professor is to treat students like a paper machine, instead of teaching/mentoring them to be better scientists/teachers. To be honest, I am not that surprised by this, even though we all should be, in an ideal world.
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