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(1) Given: AI risk comes primarily from AI optimizing for things besides human values.
(2) Given: humans already are optimizing for things besides human values. (or, at least besides our Coherent Extrapolated Volition)
(3) Given: Our world is okay.^[CITATION NEEDED!]
(4) Therefore, imperfect value loading can still result in an okay outcome.
This is, of course, not necessarily always the case for any given imperfect value loading. However, our world serves as a single counterexample to the rule that all imperfect optimization will be disastrous.
(5) Given: A maxipok strategy is optimal. ("Maximize the probability of an okay outcome.")
(6) Given: Partial optimization for human values is easier than total optimization. (Where "partial optimization" is at least close enough to achieve an okay outcome.)
(7) ∴ MIRI should focus on imperfect value loading.
Note that I'm not convinced of several of the givens, so I'm not certain of the conclusion. However, the argument itself looks convincing to me. I’ve also chosen to leave assumptions like “imperfect value loading results in partial optimization” unstated as part of the definitions of those 2 terms. However, I’ll try and add details to any specific areas, if questioned.
Except that the proposed rule is more like, given an imperfect objective function, the outcome is likely to turn from ok to disastrous at some point as optimization power is increased. See the Context Disaster and Edge Instantiation articles at Arbital.
The idea of context disasters applies to humans or humanity as a whole as well as AIs, since as you mentioned we are already optimizing for something that is not exactly our true values. Eve... (read more)