How much would you say (3) supports (1) on your model? I’m still pretty new to AIS and am updating from your model.
I agree that marginal improvements are good for fields like medicine, and perhaps so too AIS. E.g. I can imagine self-other overlap scaling to near-ASI, though I’m doubtful about stability under reflection. I’ll put 35% we find a semi-robust solution sufficient to not kill everyone.
Given my model, I think 20% generalizability is worth a person’s time. Given yours, I’d say 1% is enough.
I think that the distribution of success probability of typical optimal-from-our-perspective solutions is very wide for both of the ways we describe generalizability; within that, we should weight generalizability heavier than my understanding of your model does.
Earlier:
Designing only best-worst-case subproblem solutions while waiting for Alice would be like restricting strategies in game to ones agnostic to the opponent’s moves
Is this saying people should coordinate in case valuable solutions aren’t in the apriori generalizable space?
How much would you say (3) supports (1) on your model? I’m still pretty new to AIS and am updating from your model.
I agree that marginal improvements are good for fields like medicine, and perhaps so too AIS. E.g. I can imagine self-other overlap scaling to near-ASI, though I’m doubtful about stability under reflection. I’ll put 35% we find a semi-robust solution sufficient to not kill everyone.
I think that the distribution of success probability of typical optimal-from-our-perspective solutions is very wide for both of the ways we describe generalizability; within that, we should weight generalizability heavier than my understanding of your model does.
Earlier:
Is this saying people should coordinate in case valuable solutions aren’t in the apriori generalizable space?