I have discussed with MIRI their decision to make their research non-disclosed-by-default and we agreed that my research agenda is a reasonable exception.
Small note: my view of MIRI's nondisclosed-by-default policy is that if all researchers involved with a research program think it should obviously be public then it should obviously be public, and that doesn't require a bunch of bureaucracy. I think this while simultaneously predicting that when researchers have a part of themselves that feels uncertain or uneasy about whether their research should be public, they will find that there are large benefits to instituting a nondisclosed-by-default policy. But the policy is there to enable researchers, not to annoy them and make them jump through hoops.(Caveat: within ML, it's still rare for risk-based nondisclosure to be treated as a real option, and many social incentives favor publishing-by-default. I want to be very clear that within the context of those incentives, I expect many people to jump to "this seems obviously safe to me" when the evidence doesn't warrant it. I think it's important to facilitate an environment where it's not just OK-on-paper but also socially-hedonic to decide against publishing, and I think that these decisions often warrant serious thought. The aim of MIRI's disclosure policy is to remove undue pressures to make publication decisions prematurely, not to override researchers' considered conclusions.)
The second statement seems pretty plausible (when we consider human-accessible AGI designs, at least), but I'm not super confident of it, and I'm not resting my argument on it.
The weaker statement you provide doesn't seem like it's addressing my concern. I expect there are ways to get highly capable reasoning (sufficient for, e.g., gaining decisive strategic advantage) without understanding low-K "good reasoning"; the concern is that said systems are much more difficult to align.
As I noted when we chatted about this in person, my intuition is less "there is some small core of good consequentialist reasoning (it has “low Kolmogorov complexity” in some sense), and this small core will be quite important for AI capabilities" and more "good consequentialist reasoning is low-K and those who understand it will be better equipped to design AGI systems where the relevant consequentialist reasoning happens in transparent boxes rather than black boxes."
Indeed, if I thought one had to understand good consequentialist reasoning in order to design a highly capable AI system, I'd be less worried by a decent margin.
Weighing in late here, I'll briefly note that my current stance on the difficulty of philosophical issues is (in colloquial terms) "for the love of all that is good, please don't attempt to implement CEV with your first transhuman intelligence". My strategy at this point is very much "build the minimum AI system that is capable of stabilizing the overall strategic situation, and then buy a whole lot of time, and then use that time to figure out what to do with the future." I might be more optimistic than you about how easy it will turn out to be to find a reasonable method for extrapolating human volition, but I suspect that that's a moot point either way, because regardless, thou shalt not attempt to implement CEV with humanity's very first transhuman intelligence.
Also, +1 to the overall point of "also pursue other approaches".
Huh, thanks for the heads up. If you use an ad-blocker, try pausing that and refreshing. Meanwhile, I'll have someone look into it.
FYI, this is not what the word "corrigibility" means in this context. (Or, at least, it's not how we at MIRI have been using it, and it's not how Stuart Russell has been using it, and it's not a usage that I, as one of the people who originally brought that word into the AI alignment space, endorse.) We use the phrase "utility indifference" to refer to what you're calling "corrigibility", and we use the word "corrigibility" for the broad vague problem that "utility indifference" was but one attempt to solve.
By analogy, imagine people groping around in the dark attempting to develop probability theory. They might call the whole topic the topic of "managing uncertainty," and they might call specific attempts things like "fuzzy logic" or "multi-valued logic" before eventually settling on something that seems to work pretty well (which happened to be an attempt called "probability theory.") We're currently reserving the "corrigibilty" word for the analog of "managing uncertainty"; that is, we use the "corrigibility" label to refer to the highly general problem of developing AI algorithms that cause a system to (in an intuitive sense) reason without incentives to deceive/manipulate, and to reason (vaguely) as if it's still under construction and potentially dangerous :-)