Like, I may not want to become a cyborg if I stop being me, but that's a separate concern from whether it's bad for alignment (if the resulting cyborg is still aligned).
I think that's an important objection, but I see it applying almost entirely on a personal level. On the strategic level, I actually buy that this kind of augmentation (i.e. with in some sense passive AI) is not an alignment risk (any more than any technology is). My worry is the "dual use technology" section.
This is a great article! It helps me understand shard theory better and value it more; in particular, it relates to something I've been thinking about where people seem to conflate utility-optimizing agents with policy-execuing agents, but the two have meaningfully different alignment characteristics, and shard theory seems to be deeply exploring the latter, which is 👍.
That is to say, prior to "simulators" and "shard theory", a lot of focus was on utility-maximizers--agents that do things like planning or search to maximize a utility function; but planning, although instrumentally useful, is not strictly necessary for many intelligent behaviors, so we are seeing more focus on e.g. agents that enact learned policies in RL that do not explicitly maximize reward in deployment but try to enact policies that did so in training.
The answer is not to find a clever way to get a robust grader. The answer is to not need a robust grader
💯
From my perspective, this post convincingly argues that one route to alignment involves splitting the problem into two still-difficult sub-problems (but actually easier, unlike inner- and outer-alignment, as you've said elsewhere): identifying a good shard structure and training an AI with such a shard structure. One point is that the structure is inherently somewhat robust (and that therefore each individual shard need not be), making it a much larger target.
I have two objections:
-
I don't buy the implied "naturally-robust" claim. You've solved the optimizer's curse, wireheading via self-generated adversarial inputs, etc., but the policy induced by the shard structure is still sensitive to the details; unless you're hiding specific robust structures in your back pocket, I have no way of knowing that increasing the candy-shard's value won't cause a phase shift that substantially increases the perceived value of the "kill all humans, take their candy" action plan. I ultimately care about the agent's "revealed preferences", and I am not convinced that those are smooth relative to changes in the shards.
-
I don't think that we can train a "value humans" shard that avoids problems with the edge cases of what that means. Maybe it learns that it should kill all humans and preserve their history; or maybe it learns that it should keep them alive and comatose; or maybe it has strong opinions one way or another on whether uploading is death; or maybe it respects autonomy too much to do anything (though that one would probably be decomissioned and replaced by one more dangerous). The problem is not adversarial inputs but genuine vagueness where precision matters. I think this boils down to me disagreeing with John Wentworth's "natural abstraction hypothesis" (at least in some ways that matter)
1Some potentially naive thoughts/questions:
- At a cursory level, this seems closely related to Deep Double Descent, but you don't mention it, which I find surprising; did I pattern-match in error?
- This also seems tangentially related to the single basin hypothesis
Would you say that models designed from the ground up to be collaborative and capabilitarian would be a net win for alignment, even if they're not explicitly weakened in terms of helping people develop capabilities? I'd be worried that they could multiply human efforts equally, but with humans spending more effort on capabilities, that's still a net negative.