Ahh for MD I mostly used DFT with VASP or CP2K, but then I was not working on the same problems. For thorny issues (biggish and plain DFT fails, but no MD) I had good results using hybrid functionals and tuning the parameters to match some result of higher level methods. Did you try meta-GGAs like SCAN? Sometimes they are suprisingly decent where PBE fails catastrophically...
jacopo
My job was doing quantum chemistry simulations for a few years, so I think I can comprehend the scale actually. I had access to one of the top-50 supercomputers and codes just do not scale to that number of processors for one simulation independently of system size (even if they had let me launch a job that big, which was not possible)
Isn’t this a trivial consequence of LLMs operating on tokens as opposed to letters?
True, but this doesn’t apply to the original reasoning in the post—he assumes constant probability while you need increasing probability (as with the balls) to make the math work.
Or decreasing benefits, which probably is the case in the real world.
Edit: misred the previous comment, see below
It seems very weird and unlikely to me that the system would go to the higher energy state 100% of the time
I think vibrational energy is neglected in the first paper, it would be implicitly be accounted for in AIMD. Also, the higer energy state could be the lower free energy state—if the difference is big enough it could go there nearly 100% of the time.
Although they never take the whole supercomputer, so if you have the whole supercomputer for yourself and the calculations do not depend on each other you can run many in parallel
That’s one simulation though. If you have to screen hundreds of candidate structures, and simulate every step of the process because you cannot run experiments, it becomes years of supercomputer time.
There are plenty of people on LessWrong who are overconfident in all their opionions (or maybe write as if they are, as a misguided rhetorical choice?). It is probably a selection effect of people who appreciate the sequences—whatever you think of his accuracy record, EY definitely writes as if he’s always very confident in his conclusions.
Whatever the reason, (rhetorical) overconfidence is most often seen here as a venial sin, as long as you bring decently-reasoned arguments and are willing to change your mind in response to other’s. Maybe it’s not your case, but I’m sure many would have been lighter with their downvotes had the topic been another one—just a few people strong downvoting instead of simple downvoting can change the karma balance quite a bit
(Phd in condensed matter simulation) I agree with everything you wrote where I know enough (for readers, I don’t know anything about lead contacts and several other experimental tricky points, so my agreement should not be counted too much).
I just add on the simulation side (Q3): this is what you would expect to see in a room-T superconductor unless it relies on a completely new mechanism. But, this is something you see also in a lot of materials that superconduct at 20K or so. Even in some where the superconducting phase is completely suppressed by magetism or structural distortions or any other phase transition. In addition, DFT+U is a quick-and-dirty approach for this kind of problem, as fits the speed at which the preprint was put out. So from the simulation bayesian evidence in favor but very weak
Is there something that would regularise the vectors towards constant norm? An helix would make a lot of sense in this case. Especially one with varying radius, like in some (not all) the images
I don’t think it would change your conclusion but your kettle was not very scaly. My gets much worse than that, with the resistence entirely covered by a thick layer, despite descaling 3-4 times per year. It depends on the calc content of your tap water. I still don’t think it affects energy use (maybe?), but the taste can be noticeable and I feel tea is actually harder to digest if I put off the descaling.
Also, you can use citric acid instead of vinegar. Better for the environment, less damaging to the kettle and it doesn’t smell :)
Well stated. I would go even further: the only short timeline scenario I can immagine involves some unholy combination of recursive LLM calls, hardcoded functions or non-LLM ML stuff, and API calls. There would probably be space to align such a thing. (sort of. If we start thinking about it in advance.)
Isn’t that the point of the original transformer paper? I have not actually read it, just going by summaries read here and there.
If I don’t misremember RNN should be expecially difficult to train in parallel
That seem reasonable, but it will probably change a number of correct answers (to tricky questions) as well if asked whether it’s certain. One should verify that the number of incorrect answers fixed is significantly larger than the number of errors introduced.
But it might be difficult to devise a set of equally difficult questions for which the first result is different. Maybe choose questions where different instances give different answers, and see if asking a double check changes the wrong answers but not the correct ones?
Good post, thank you for it. Linking this will save me a lot of time when commenting...
However I think that the banking case is not a good application. When one bank fails, it makes much more likely that other banks will fail immediately after. So it is perfectly plausible that two banks are weak for unrelated reasons, and that when one fails this pushes the other under as well.
The second one does not even have to be that weak. The twentieth could be perfectly healthy and still fail in the panic (it’s a full blown financial crisis at this point!)
It’s not clear here, but if you read the linked post it’s spelled out (the two are complementary really). The thesis is that it’s easy to do a narrow AI that knows only about chess, but very hard to make an AGI that knows the world, can operate in a variety of situations, but only cares about chess in a consistent way.
I think this is correct at least with current AI paradigms, and it has both some reassuring and some depressing implications.
I always thought Hall’s point about nanotech was trivially false. Nanotech research like he wanted it died out in the whole world, but he explains it by US-specific factors. Why didn’t research continue elsewhere? Plus, other fields that got large funding in Europe or Japan are alive and thriving. How comes?
That doesn’t mean that a government program which sets up bad incentives cannot be worse than useless. It can be quite damaging, but not kill a technologically promising research field worldwide for twenty years.
The point about incouraging safe over innovative research is on spot though. Although the main culprits are not granting agencies but tying researcher careers to the number of peer reviewed papers imo. The main problem with the granting system is the amount of time wasted in writing grant applications.
That was quite different though (spoiler alert)
A benevolent conspiracy to hide a dangerous scientific discovery by lying about the state of the art and denying resources to anyone whose research might uncover the lie. Ultimately failing because apparently unrelated advances made rediscovering the true result too easy.
I always saw it as a reply to the idea that physicists could have hidden the possibility of an atomic bomb for more than a few years.
You could also try to fit an ML potential to some expensive method, but it’s very easy to produce very wrong things if you don’t know what you’re doing (I wouldn’t be able for one)