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It works only if it fly above your territory - and similar systems are used for drones detection now. Actually, they use people eyes and smartphones and instant messaging. But during recent attack on Iran a single F35 flied over Iraq and fired a missile from like 200 km distance on a target in Iran.
yair-halberstadt on Can stealth aircraft be detected optically?You're intuition is correct when the jet has already passed ahead - those are very hard to catch and shoot down. But usually you detect an aircraft when it's heading towards you, and all the missile has to do is intercept. It doesn't even have to be faster than the jet (unless the jet detected it in time and does a 180).
tailcalled on Ironing Out the SquigglesThe way I'd phrase the theoretical problem when you fit a model to a distribution (e.g. minimizing KL-divergence on a set of samples), you can often prove theorems of the form "the fitted-distribution has such-and-such relationship to the true distribution", e.g. you can compute confidence intervals for parameters and predictions in linear regression.
Often, all that is sufficient for those theorems to hold is:
... because then if you have some point X you want to make predictions for, the sample size being big enough means you have a whole bunch of points in the empirical distribution that are similar to X. These points affect the loss landscape, and because you've got a flexible optimal model, that forces the model to approximate them well enough.
But this "you've got a bunch of empirical points dragging the loss around in relevant ways" part only works on-distribution, because you don't have a bunch of empirical points from off-distribution data. Even if technically they form an exponentially small slice of the true distribution, this means they only have an exponentially small effect on the loss function, and therefore being at an optimal loss is exponentially weakly informative about these points.
(Obviously this is somewhat complicated by overfitting, double descent, etc., but I think the gist of the argument goes through.)
I guess it depends on whether one makes the cut between theory and practice with or without assuming that one has learned the distribution? I.e. I'm saying if you have a distribution D, take some samples E, and approximate E with Q, then you might be able to prove that samples from Q are similar to samples from D, but you can't prove that conditioning on something exponentially unlikely in D gives you something reasonable in Q. Meanwhile you're saying that conditioning on something exponentially unlikely in D is tantamount to optimization.
hmys on ACX Covid Origins Post convinced readershttps://www.richardhanania.com/p/if-scott-alexander-told-me-to-jump
viliam on How would you navigate a severe financial emergency with no help or resources?Just some random thoughts:
Let D be the distribution of (reward, trajectory) pairs for every possible trajectory.
Split D into two subsets: D1 where reward > 7 and D2 where reward ≤ 7.
Suppose that, in D, 1-in-a-googol samples is in the subset D1, and all the rest are in D2.
(For example, if a videogame involves pressing buttons for 20 minutes, you can easily have less than 1-in-a-googol chance of beating even the first mini-boss if you press the buttons randomly.)
Now we randomly pick a million samples from D in an attempt to learn the distribution D. But (as expected with overwhelming probability), it turns out that every single one of those million samples are more specifically in the subset D2.
Now consider a point X in D1 (its reward is 30). Question: Is X “out of distribution”?
Arguably no, because we set up a procedure to learn the distribution D, and D contains X.
Arguably yes, because when we ran the procedure, all the points actually used to learn the distribution were in D2, so we were kinda really only learning D2, and D2 doesn’t contain X.
(In the videogame example, if in training you never see a run that gets past the first mini-boss, then certainly intuitively we'd say that runs that do get past it, and that thus get to the next stage of the game, are OOD.)
Anyway, I was gonna say the opposite of what you said—sufficiently hard optimization via conditional sampling works in theory (i.e., if you could somehow learn D and conditionally sample on reward>7, it would work), but not in practice (because reward>7 is so hard to come by that you will never actually learn that part of D by random sampling).
oliver-sourbut on Transformers Represent Belief State Geometry in their Residual StreamI guess my question would be 'how else did you think a well-generalising sequence model would achieve this?' Like, what is a sufficient world model but a posterior over HMM states in this case? This is what GR theorem asks. (Of course, a poorly-fit model might track extraneous detail or have a bad posterior.)
From your preamble and your experiment design, it looks like you correctly anticipated the result, so this should not have been a surprise (to you). In general I object to being sold something as surprising which isn't (it strikes me as a lesser-noticed and perhaps oft-inadvertent rhetorical dark art and I see it on the rise on LW, which is sad).
That said, since I'm the only one objecting here, you appear to be more right about the surprisingness of this!
The linear probe is new news (but not surprising?) on top of GR, I agree. But the OP presents the other aspects as the surprises, and not this.
mesaoptimizer on Please stop publishing ideas/insights/research about AINote that I agree with your sentiment here, although my concrete argument is basically what LawrenceC wrote as a reply to this post [LW(p) · GW(p)].
mesaoptimizer on Please stop publishing ideas/insights/research about AIRyan, this is kind of a side-note but I notice that you have a very Paul-like approach to arguments and replies on LW.
Two things that come to notice:
I just found this interesting and plausible enough to highlight to you. Its a moderate investment of my time to find out examples from your comment history to highlight all these instances, but writing this comment still seemed valuable.
odd-anon on List your AI X-Risk cruxes!Relevant: My Taxonomy of AI-risk counterarguments [LW · GW], inspired by Zvi Mowshowitz's The Crux List [LW · GW].