This is an excellent encapsulation of (I think) something different—the “fragility of value” issue: “formerly adequate levels of alignment can become inadequate when applied to a takeover-capable agent.” I think the “generalization gap” issue is “those perfectly-generalizing alignment techniques must generalize perfectly on the first try”.
Attempting to deconfuse myself about how that works if it’s “continuous” (someone has probably written the thing that would deconfuse me, but as an exercise): if AI power progress is “continuous” (which training is, but model-sequence isn’t), it goes from “you definitely don’t have to get it right at all to survive” to “you definitely get only one try to get it sufficiently right, if you want to survive,” but by what path? In which of the terms “definitely,” “one,” and “sufficiently” is it moving continuously, if any?
I certainly don’t think it’s via the number of tries you get to survive! I struggle to imagine an AI where we all die if we fail to align it three times in a row.
I don’t put any stock in “sufficiently,” either—I don’t believe in a takeover-capable AI that’s aligned enough to not work toward takeover, but which would work toward takeover if it were even more capable. (And even if one existed, it would have to eschew RSI and other instrumentally convergent things, else it would just count as a takeover-causing AI.)
It might be via the confidence of the statement. Now, I don’t expect AIs to launch highly-contingent outright takeover attempts; if they’re smart enough to have a reasonable chance of succeeding, I think they’ll be self-aware enough to bide their time, suppress the development of rival AIs, and do instrumentally convergent stuff while seeming friendly. But there is some level of self-knowledge at which an AI will start down the path toward takeover (e.g., extricating itself, sabotaging rivals) and succeed with a probability that’s very much neither 0 nor 1. Is this first, weakish, self-aware AI able to extricate itself? It depends! But I still expect the relevant band of AI capabilities here to be pretty narrow, and we get no guarantee it will exist at all. And we might skip over it with a fancy new model (if it was sufficiently immobilized during training or guarded its goals well).
Of course, there’s still a continuity in expectation: when training each more powerful model, it has some probability of being The Big One. But yeah, I more or less predict a Big One; I believe in an essential discontinuity arising here from a continuous process. The best analogy I can think of is how every exponential with r<1 dies out and every r>1 goes off to infinity. When you allow dynamic systems, you naturally get cuspy behavior.
I like your made up notation. I’ll try to answer, but I’m an amateur in both reasoning-about-this-stuff and representing-others’-reasoning-about-this-stuff.
I think (1) is both inner and outer misalignment. (2) is fragility of value, yes.
I think the “generalization step is hard” point is roughly “you can get δ low by trial and error. The technique you found at the end that gets δ low—it better not intrinsically depend on the trial and error process, because you don’t get to do trial and error on δ‘. Moreover, it better actually work on M’.”
Contemporary alignment techniques depend on trial and error (post-training, testing, patching). That’s one of their many problems.
My suggest term for standard MIRI thought would just be Mirism.
I kinda don’t like “generalization” as a name for this step. Maybe “extension”? There are too many steps where the central difficulty feels analogous to the general phenomenon of failure-of-generalization-OOD: the difficulty in getting δ to be small, the difficulty of going from techniques for getting δ small to techniques for getting a small δ′ (verbiage different because of the first-time constraint), the disastrousness of even smallish δ’…