I agree with you that humans have mismatched goals among ourselves, so some amount of goal mismatch is just a fact we have to deal with. I think the ideal is that we get an AGI that makes its goal the overlap in human goals; see [Empowerment is (almost) All We Need](https://www.lesswrong.com/posts/JPHeENwRyXn9YFmXc/empowerment-is-almost-all-we-need) and others on preference maximization.
I also agree with your intuition that having a non-maximizer improves the odds of an AGI not seeking power or doing other dangerous things. But I think we need to go far beyond the intuition; we don’t want to play odds with the future of humanity. To that end, I have more thoughts on where this will and won’t happen.
I’m saying “the problem” with optimization is actually mismatched goals, not optimization/maximization. In more depth, and hopefully more usefully: I think unbounded goals are the problem with optimization (not the only problem, but a very big one).
If an AGI had a bounded goal like “make on billion paperclips”, it wouldn’t be nearly as dangerous; it might decide to eliminate humanity to make the odds of getting to a billion as good as possible (I can’t remember where I saw this important point; I think maybe Nate Soares made it). But it might decide that its best odds would just be making some improvements to the paperclip business, in which case it wouldn’t cause problems.
One final comment on your argument about odds: In our algorithms, specifying an allowable aspiration includes specifying a desired probability of success that is sufficiently below 100%. This is exactly to avoid the problem of fulfilling the aspiration becoming an optimization problem through the backdoor.
I agree with you that humans have mismatched goals among ourselves, so some amount of goal mismatch is just a fact we have to deal with. I think the ideal is that we get an AGI that makes its goal the overlap in human goals; see [Empowerment is (almost) All We Need](https://www.lesswrong.com/posts/JPHeENwRyXn9YFmXc/empowerment-is-almost-all-we-need) and others on preference maximization.
I also agree with your intuition that having a non-maximizer improves the odds of an AGI not seeking power or doing other dangerous things. But I think we need to go far beyond the intuition; we don’t want to play odds with the future of humanity. To that end, I have more thoughts on where this will and won’t happen.
I’m saying “the problem” with optimization is actually mismatched goals, not optimization/maximization. In more depth, and hopefully more usefully: I think unbounded goals are the problem with optimization (not the only problem, but a very big one).
If an AGI had a bounded goal like “make on billion paperclips”, it wouldn’t be nearly as dangerous; it might decide to eliminate humanity to make the odds of getting to a billion as good as possible (I can’t remember where I saw this important point; I think maybe Nate Soares made it). But it might decide that its best odds would just be making some improvements to the paperclip business, in which case it wouldn’t cause problems.
So we’re converging...
One final comment on your argument about odds: In our algorithms, specifying an allowable aspiration includes specifying a desired probability of success that is sufficiently below 100%. This is exactly to avoid the problem of fulfilling the aspiration becoming an optimization problem through the backdoor.