Earlier quoted context omitted.
Go enough shoulders down, and someone had to have been the first giant.
Pythagoras is the turtle.
Pythagoras learned from Egyptians that have been largely erased by euro/western narratives of superiority.
GPT-5.2 derives a new result in theoretical physics
161–170 of 430 posts
Re: GPT-5.2 derives a new result in theoretical physics
#162AI can be an amazing productivity multiplier for people who know what they're doing. This result reminded me of the C compiler case that Anthropic posted recently. Sure, agents wrote the code for hours but there was a human there giving them directions, scoping the problem, finding the test suites needed for the agentic loops to actually work etc etc. In general making sure the output actually works and that it's a s…
I'm not sure you can call something an optimizing C compiler if it doesn't optimize or enforce C semantics (well, it compiles C but also a lot of things that aren't syntactically valid C). It seemed to generate a lot of code (wow!) that wasn't well-integrated and didn't do what it promised to, and the human didn't have the requisite expertise to understand that. I'm not a theoretical physicist but I will hold to my s…
sure, I won't argue on this, although it did manage to deliver the marketing value they were looking for, at the end their goal was not to replace gcc but to make people talk about AI and Anthropic.
What I said in my original comment is that AI delivers when it's used by experts, in this case there was someone who was definitely not a C compiler expert, what would happen if there was a real expert doing this?
Re: GPT-5.2 derives a new result in theoretical physics
#163The headline may make it seem like AI just discovered some new result in physics all on its own, but reading the post, humans started off trying to solve some problem, it got complex, GPT simplified it and found a solution with the simpler representation. It took 12 hours for GPT pro to do this. In my experience LLM’s can make new things when they are some linear combination of existing things but I haven’t been to g…
> but I haven’t been to get them to do something totally out of distribution yet from first principles Can humans actually do that? Sometimes it appears as if we have made a completely new discovery. However, if you look more closely, you will find that many events and developments led up to this breakthrough, and that it is actually an improvement on something that already existed. We are always building on the shou…
> Can humans actually do that?
Seriously, think about it for a second...
If that were true then science should have accelerated a lot faster. Science would have happened differently and researchers would have optimized to trying to ingest as many papers as they can.
Dig deep into things and you'll find that there are often leaps of faith that need to be made. Guesses, hunches, and outright conjectures. Remember, there are paradigm shifts that happen. There are plenty of things in physics (including classical) that cannot be determined from observation alone. Or more accurately, cannot be differentiated from alternative hypotheses through observation alone.
I think the problem is when teaching science we generally teach it very linearly. As if things easily follow. But in reality there is generally constant iterative improvements but they more look like a plateau, then there are these leaps. They happen for a variety of reasons but no paradigm shift would be contentious if it was obvious and clearly in distribution. It would always be met with the same response that typical iterative improvements are met with "well that's obvious, is this even novel enough to be published? Everybody already knew this" (hell, look at the response to the top comment and my reply... that's classic "Reviewer #2" behavior). If it was always in distribution progress would be nearly frictionless. Again, with history in how we teach science we make an error in teaching things like Galileo, as if The Church was the only opposition. There were many scientists that objected, and on reasonable grounds. It is also a problem we continually make in how we view the world. If you're sticking with "it works" you'll end up with a geocentric model rather than a heliocentric model. It is true that the geocentric model had limits but so did the original heliocentric model and that's the reason it took time to be adopted.
By viewing things at too high of a level we often fool ourselves. While I'm criticizing how we teach I'll also admit it is a tough thing to balance. It is difficult to get nuanced and in teaching we must be time effective and cover a lot of material. But I think it is important to teach the history of science so that people better understand how it actually evolves and how discoveries were actually made. Without that it is hard to learn how to actually do those things yourself, and this is a frequent problem faced by many who enter PhD programs (and beyond).
> We are always building on the shoulders of giants.
It's probably worth reading The Unreasonable Effectiveness of Mathematics in the Natural Sciences. It might seem obvious now but read carefully. If you truly think it is obvious that you can sit in a room armed with only pen and paper and make accurate predictions about the world, you have fooled yourself. You have not questioned why this is true. You have not questioned when this actually became true. You have not questioned how this could be true.
https://www.hep.upenn.edu/~johnda/Papers/wignerUnreasonableE...
You are greater than the sum of your partsRe: GPT-5.2 derives a new result in theoretical physics
#164The headline may make it seem like AI just discovered some new result in physics all on its own, but reading the post, humans started off trying to solve some problem, it got complex, GPT simplified it and found a solution with the simpler representation. It took 12 hours for GPT pro to do this. In my experience LLM’s can make new things when they are some linear combination of existing things but I haven’t been to g…
"GPT did this" . Authored by Guevara (Institute for Advanced Study), Lupsasca (Vanderbilt University), Skinner (University of Cambridge), and Strominger (Harvard University). Probably not something that the average GI Joe would be able to prompt their way to... I am skeptical until they show the chat log leading up to the conjecture and proof.
"Grad Student did this". Co-authored by , , .
Is this so different?
Re: GPT-5.2 derives a new result in theoretical physics
#165AI can be an amazing productivity multiplier for people who know what they're doing. This result reminded me of the C compiler case that Anthropic posted recently. Sure, agents wrote the code for hours but there was a human there giving them directions, scoping the problem, finding the test suites needed for the agentic loops to actually work etc etc. In general making sure the output actually works and that it's a s…
>AI can be an amazing productivity multiplier for people who know what they're doing.
>[...]
>The "AI replaces humans in X" narrative is primarily a tool for driving attention and funding.
You're sort of acting like it's all or nothing. What about the the humans that used to be that "force multiplier" on a team with the person guiding the research?
If a piece of software required a team of ten to people, and instead it's built with one engineer overseeing an AI, that's still 90% job loss.
For a more current example: do you think all the displaced Uber/Lyft drivers aren't going to think "AI took my job" just because there's a team of people in a building somewhere handling the occasional Waymo low confidence intervention, as opposed to being 100% autonomous?
Re: GPT-5.2 derives a new result in theoretical physics
#166It's interesting to me that whenever a new breakthrough in AI use comes up, there's always a flood of people who come in to handwave away why this isn't actually a win for LLMs. Like with the novel solutions GPT 5.2 has been able to find for erdos problems - many users here (even in this very thread!) think they know more about this than Fields medalist Terence Tao, who maintains this list showing that, yes, LLMs hav…
It's an obvious tension created by the title. The reality is: "GPT 5.2 found a more general and scalable form of an equation, after crunching for 12 hours supervised by 4 experts in the field". Which is equivalent to taking some of the countless niche algorithms out there and have few experts in that algo have LLMs crunch tirelessly till they find a better formula. After same experts prompted it in the right directio…
> GPT 5.2 after crunching 12 hours mathematical formulas supervised and prompted by 4 experts in the field
Yet, if some student or child achieved the same – under equal supervision – we would call him the next Einstein.
Re: GPT-5.2 derives a new result in theoretical physics
#167Earlier quoted context omitted.
I'm a big LLM sceptic but that's… moving the goalposts a little too far. How could an average Joe even understand the conjecture enough to write the initial prompt? Or do you mean that experts would give him the prompt to copy-paste, and hope that the proverbial monkey can come up with a Henry V? At the very least posit someone like a grad student in particle physics as the human user.
I would interpret it as implying that the result was due to a lot more hand-holding that what is let on. Was the initial conjecture based on leading info from the other authors or was it simply the authors presenting all information and asking for a conjecture? Did the authors know that there was a simpler means of expressing the conjecture and lead GPT to its conclusion, or did it spontaneously do so on its own afte…
Hi I am an author of the paper. We believed that a simple formula should exist but had not been able to find it despite significant effort. It was a collaborative effort but GPT definitely solved the problem for us.
Re: GPT-5.2 derives a new result in theoretical physics
#168Earlier quoted context omitted.
"For me, it is far better to grasp the universe as it really is than to persist in delusion, however satisfying and reassuring." — Carl Sagan
I read the narnia series many times as a kid and this one stuck with me, I didn't prompt for it. I have no real way to demonstrate that I'm telling the truth, but I am ¯\_(ツ)_/¯
Sorry for the assumption. For what it's worth, I read one of Sagan's books last year, but pulled the quote from Goodreads :P
Re: GPT-5.2 derives a new result in theoretical physics
#169The headline may make it seem like AI just discovered some new result in physics all on its own, but reading the post, humans started off trying to solve some problem, it got complex, GPT simplified it and found a solution with the simpler representation. It took 12 hours for GPT pro to do this. In my experience LLM’s can make new things when they are some linear combination of existing things but I haven’t been to g…
This is the critical bit (paraphrasing): Humans have worked out the amplitudes for integer n up to n = 6 by hand, obtaining very complicated expressions, which correspond to a “Feynman diagram expansion” whose complexity grows superexponentially in n. But no one has been able to greatly reduce the complexity of these expressions, providing much simpler forms. And from these base cases, no one was then able to spot a…
That paper from the 80s (which is cited in the new one) is about "MHV amplitudes" with two negative-helicity gluons, so "double-minus amplitudes". The main significance of this new paper is to point out that "single-minus amplitudes" which had previously been thought to vanish are actually nontrivial. Moreover, GPT-5.2 Pro computed a simple formula for the single-minus amplitudes that is the analogue of the Parke-Taylor formula for the double-minus "MHV" amplitudes.
Re: GPT-5.2 derives a new result in theoretical physics
#170Earlier quoted context omitted.
It didn't solve it, it simply found that it had been solved in a publication and that the list of open problems wasn't updated.
My understanding is there's been around 10 erdos problems solved by GPT by now. Most of them have been found to be either in literature or a very similar problem was solved in literature. But one or two solutions are quite novel. https://github.com/teorth/erdosproblems/wiki/AI-contribution... may be useful
I am not aware of any unsolved Erdos problem that was solved via an LLM. I am aware of LLMs contributing to variations on known proofs of previously solved Erdos problems. But the issue with having an LLM combine existing solutions or modify existing published solutions is that the previous solutions are in the training data of the LLM, and in general there are many options to make variations on known proofs. Most proofs go through many iterations and simplifications over time, most of which are not sufficiently novel to even warrant publication. The proof you read in a textbook is likely a highly revised and simplified proof of what was first published.
If I'm wrong, please let me know which previously unsolved problem was solved, I would be genuinely curious to see an example of that.