I work at the Alignment Research Center (ARC). I write a blog on stuff I’m interested in (such as math, philosophy, puzzles, statistics, and elections): https://ericneyman.wordpress.com/
Eric Neyman
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See here and here for my attempts to do this a few years ago! Our project (which we called Pact) ultimately died, mostly because it was no one’s first priority to make it happen. About once a year I get contacted by some person or group who’s trying to do the same thing, asking about the lessons we learned.
I think it’s a great idea—at least in theory—and I wish them the best of luck!
(For anyone who’s inclined toward mechanism design and is interested in some of my thoughts around incentives for donors on such a platform, I wrote about that on my blog five years ago.)
Any chance we could get Ghibli Mode back? I miss my little blue monster :(
Ohh I see. Do you have a suggested rephrasing?
Empirically, the “nerd-crack explanation” seems to have been (partially) correct, see here.
Oh, I don’t think it was at all morally bad for Polymarket to make this market—just not strategic, from the standpoint of having people take them seriously.
Top Manifold user Semiotic Rivalry said on Twitter that he knows the top Yes holders, that they are very smart, and that the Time Value of Money hypothesis is part of (but not the whole) story. The other part has to do with how Polymarket structures rewards for traders who provide liquidity.
https://x.com/SemioticRivalry/status/1904261225057251727
Yeah, I think the time value of Polymarket cash doesn’t track the time value of money in the global economy especially closely:
If Polymarket cash were completely fungible with regular cash, you’d expect the Jesus market to reflect the overall interest rate of the economy. In practice, though, getting money into Polymarket is kind of annoying (you need crypto) and illegal for Americans. Plus, it takes a few days, and trade opportunities often evaporate in a matter of minutes or hours! And that’s not to mention the regulatory uncertainty: maybe the US government will freeze Polymarket’s assets and traders won’t be able to get their money out?
And so it’s not unreasonable to have opinions on the future time value of Polymarket cash that differs substantially from your opinions on the future time value of money.
Yeah, honestly I have no idea why Polymarket created this question.
Will Jesus Christ return in an election year?
Do you think that these drugs significantly help with alcoholism (as one might posit if the drugs help significantly with willpower)? If so, I’m curious what you make of this Dynomight post arguing that so far the results don’t look promising.
I think that large portions of the AI safety community act this way. This includes most people working on scalable alignment, interp, and deception.
Are you sure? For example, I work on technical AI safety because it’s my comparative advantage, but agree at a high level with your view of the AI safety problem, and almost all of my donations are directed at making AI governance go well. My (not very confident) impression is that most of the people working on technical AI safety (at least in Berkeley/SF) are in a similar place.
We are interested in natural distributions over reversible circuits (see e.g. footnote 3), where we believe that circuits that satisfy P are exceptionally rare (probably exponentially rare).
Probably don’t update on this too much, but when I hear “Berkeley Genomics Project”, it sounds to me like a project that’s affiliated with UC Berkeley (which it seems like you guys are not). Might be worth keeping in mind, in that some people might be misled by the name.
Echoing Jacob, yeah, thanks for writing this!
Since there are only exponentially many circuits, having the time-complexity of the verifier grow only linearly with would mean that you could get a verifier that never makes mistakes. So (if I’m not mistaken) if you’re right about that, then the stronger version of reduction-regularity would imply that our conjecture is equivalent to NP = coNP.
I haven’t thought enough about the reduction-regularity assumption to have a take on its plausibility, but based on my intuition about our no-coincidence principle, I think it’s pretty unlikely to be equivalent to NP = coNP in a way that’s easy-ish to show.
That’s an interesting point! I think it only applies to constructive proofs, though: you could imagine disproving the counterexample by showing that for every V, there is some circuit that satisfies P(C) but that V doesn’t flag, without exhibiting a particular such circuit.
Do you have a link/citation for this quote? I couldn’t immediately find it.
We’ve done some experiments with small reversible circuits. Empirically, a small circuit generated in the way you suggest has very obvious structure that makes it satisfy P (i.e. it is immediately evident from looking at the circuit that P holds).
This leaves open the question of whether this is true as the circuits get large. Our reasons for believing this are mostly based on the same “no-coincidence” intuition highlighted by Gowers: a naive heuristic estimate suggests that if there is no special structure in the circuit, the probability that it would satisfy P is doubly exponentially small. So probably if C does satisfy P, it’s because of some special structure.
Is this a correct rephrasing of your question?
It seems like a full explanation of a neural network’s low loss on the training set needs to rely on lots of pieces of knowledge that it learns from the training set (e.g. “Barack” is usually followed by “Obama”). How do random “empirical regularities” about the training set like this one fit into the explanation of the neural net?
Our current best guess about what an explanation looks like is something like modeling the distribution of neural activations. Such an activation model would end up having baked-in empirical regularities, like the fact that “Barack” is usually followed by “Obama”. So in other words, just as the neural net learned this empirical regularity of the training set, our explanation will also learn the empirical regularity, and that will be part of the explanation of the neural net’s low loss.
(There’s a lot more to be said here, and our picture of this isn’t fully fleshed out: there are some follow-up questions you might ask to which I would answer “I don’t know”. I’m also not sure I understood your question correctly.)
Yeah, I did a CS PhD in Columbia’s theory group and have talked about this conjecture with a few TCS professors.
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My favorite example of a president being a good Bayesian is Abraham Lincoln (h/t Julia Galef):