Yup, I agree with this, and think the argument generalizes to most alignment work (which is why I'm relatively optimistic about our chances compared to some other people, e.g. something like 85% p(success), mostly because most things one can think of doing will probably be done).
It's possibly an argument that work is most valuable in cases of unexpectedly short timelines, although I'm not sure how much weight I actually place on that.
Yup! That sounds great :)
Thanks Ruby! Now that the other posts are out, would it be easy to forward-link them (by adding links to the italicized titles in the list at the end)?
Finding the min-max solution might be easier, but what we actually care about is an acceptable solution. My point is that the min-max solution, in most cases, will be unacceptably bad.
And in fact, since min_x f(theta,x) <= E_x[f(theta,x)], any solution that is acceptable in the worst case is also acceptable in the average case.
Thanks! I appreciated these distinctions. The worst-case argument for modularity came up in a past argument I had with Eliezer, where I argued that this was a reason for randomization (even though Bayesian decision theory implies you should never randomize). See section 2 here: The Power of Noise.
Re: 50% vs. 10% vs. 90%. I liked this illustration, although I don't think your argument actually implies 50% specifically. For instance if it turns out that everyone else is working on the 50% worlds and no one is working on the 90% worlds, you should probably work on the 90% worlds. In addition:
* It seems pretty plausible that the problem is overall more tractable in 10% worlds than 50% worlds, so given equal neglectedness you would prefer the 10% world.
* Many ideas will generalize across worlds, and recruitment / skill-building / organization-building also generalizes across worlds. This is an argument towards working on problems that seem tractable and relevant to any world, as long as they are neglected enough that you are building out distinct ideas and organizational capacity (vs. just picking from the same tree as ML generally). I don't think that this argument dominates considerations, but it likely explains some of our differences in approach.
In the terms laid out in your post, I think my biggest functional disagreement (in terms of how it affects what problems we work on) is that I expect most worst-case assumptions make the problem entirely impossible, and I am more optimistic that many empirically-grounded assumptions will generalize quite far, all the way to AGI. To be clear, I am not against all worst-case assumptions (for instance my entire PhD thesis is about this) but I do think they are usually a source of significant added difficulty and one has to be fairly careful where they are making them.
For instance, as regards Redwood's project, I expect making language models fully adversarially robust is impossible with currently accessible techniques, and that even a fairly restricted adversary will be impossible to defend against while maintaining good test accuracy. On the other hand I am still pretty excited about Redwood's project because I think you will learn interesting things by trying. (I spent some time trying to solve the unrestricted adversarial example competition, totally failed, but still felt it was a good use of time for similar reasons, and the difficulties for language models seem interestingly distinct in a way that should generate additional insight.) I'm actually not sure if this differs that much from your beliefs, though.
I think this probably depends on the field. In machine learning, solving problems under worst-case assumptions is usually impossible because of the no free lunch theorem. You might assume that a particular facet of the environment is worst-case, which is a totally fine thing to do, but I don't think it's correct to call it the "second-simplest solution", since there are many choices of what facet of the environment is worst-case.
One keyword for this is "partial specification", e.g. here is a paper I wrote that makes a minimal set of statistical assumptions and worst-case assumptions everywhere else: https://arxiv.org/abs/1606.05313. (Unfortunately the statistical assumptions are not really reasonable so the method was way too brittle in practice.) This kind of idea is also common in robust statistics. But my take would not be that it is simpler--in general it is way harder than just working with the empirical distribution in front of you.
Cool paper! One brief comment is this seems closely related to performative prediction and it seems worth discussing the relationship.
Edit: just realized this is a review, not a new paper, so my comment is a bit less relevant. Although it does still seem like a useful connection to make.
My basic take is that there will be lots of empirical examples where increasing model size by a factor of 100 leads to nonlinear increases in capabilities (and perhaps to qualitative changes in behavior). On median, I'd guess we'll see at least 2 such examples in 2022 and at least 100 by 2030.
At the point where there's a "FOOM", such examples will be commonplace and happening all the time. Foom will look like one particularly large phase transition (maybe 99th percentile among examples so far) that chains into more and more. It seems possible (though not certain--maybe 33%?) that once you have the right phase transition to kick off the rest, everything else happens pretty quickly (within a few days).
Is this take more consistent with Paul's or Eliezer's? I'm not totally sure. I'd guess closer to Paul's, but maybe the "1 day" world is consistent with Eliezer's?
(One candidate for the "big" phase transition would be if the model figures out how to go off and learn on its own, so that number of SGD updates is no longer the primary bottleneck on model capabilities. But I could also imagine us getting that even when models are still fairly "dumb".)
Thanks. For time/brevity, I'll just say which things I agree / disagree with:> sufficiently capable and general AI is likely to have property X as a strong default [...]
I generally agree with this, although for certain important values of X (such as "fooling humans for instrumental reasons") I'm probably more optimistic than you that there will be a robust effort to get not-X, including by many traditional ML people. I'm also probably more optimistic (but not certain) that those efforts will succeed.
[inside view, modest epistemology]: I don't have a strong take on either of these. My main take on inside views is that they are great for generating interesting and valuable hypotheses, but usually wrong on the particulars.
> less weight on reasoning like 'X was true about AI in 1990, in 2000, in 2010, and in 2020; therefore X is likely to be true about AGI when it's developedI agree, see my post On the Risks of Emergent Behavior in Foundation Models. In the past I think I put too much weight on this type of reasoning, and also think most people in ML put too much weight on it.> MIRI thinks AGI is better thought of as 'a weird specific sort of AI', rather than as 'like existing AI but more so'.Probably disagree but hard to tell. I think there will both be a lot of similarities and a lot of differences.> AGI is mostly insight-bottlenecked (we don't know how to build it), rather than hardware-bottleneckedSeems pretty wrong to me. We probably need both insight and hardware, but the insights themselves are hardware-bottlenecked: once you can easily try lots of stuff and see what happens, insights are much easier, see Crick on x-ray crystallography for historical support (ctrl+f for Crick).> I'd want to look at more conceptual work too, where I'd guess MIRI is also more pessimistic than youI'm more pessimistic than MIRI about HRAD, though that has selection effects. I've found conceptual work to be pretty helpful for pointing to where problems might exist, but usually relatively confused about how to address them or how specifically they're likely to manifest. (Which is to say, overall highly valuable, but consistent with my take above on inside views.)[experiments are either predictable or uninformative]: Seems wrong to me. As a concrete example: Do larger models have better or worse OOD generalization? I'm not sure if you'd pick "predictable" or "uninformative", but my take is: * The outcome wasn't predictable: within ML there are many people who would have taken each side. (I personally was on the wrong side, i.e. predicting "worse".) * It's informative, for two reasons: (1) It shows that NNs "automatically" generalize more than I might have thought, and (2) Asymptotically, we expect the curve to eventually reverse, so when does that happen and how can we study it?
See also my take on Measuring and Forecasting Risks from AI, especially the section on far-off risks.> Most ML experiments either aren't about interpretability and 'cracking open the hood', or they're not approaching the problem in a way that MIRI's excited by.Would agree with "most", but I think you probably meant something like "almost all", which seems wrong. There's lots of people working on interpretability, and some of the work seems quite good to me (aside from Chris, I think Noah Goodman, Julius Adebayo, and some others are doing pretty good work).
Not sure if this helps, and haven't read the thread carefully, but my sense is your framing might be eliding distinctions that are actually there, in a way that makes it harder to get to the bottom of your disagreement with Adam. Some predictions I'd have are that:
* For almost any experimental result, a typical MIRI person (and you, and Eliezer) would think it was less informative about AI alignment than I would. * For almost all experimental results you would think they were so much less informative as to not be worthwhile. * There's a small subset of experimental results that we would think are comparably informative, and also a some that you would find much more informative than I would.
(I'd be willing to take bets on these or pick candidate experiments to clarify this.)
In addition, a consequence of these beliefs is that compared to me you think we should be spending way more time sitting around thinking about stuff, and way less time doing experiments, than I do.
I would agree with you that "MIRI hates all experimental work" / etc. is not a faithful representation of this state of affairs, but I think there is nevertheless an important disagreement MIRI has with typical ML people, and that the disagreement is primarily about what we can learn from experiments.