There’s a lot of similarity. People (including myself in the past) have criticized Russell on the basis that no formal model can prove properties of real-world effects, because the map is not the territory, but I now agree with Russell that it’s plausible to get good enough maps. However:
I think it’s quite likely that this is only possible with an infra-Bayesian (or credal-set) approach to explicitly account for Knightian uncertainty, which seems to be a difference from Russell’s published proposals (although he has investigated Halpern-style probability logics, which have some similarities to credal sets, he mostly gravitates toward frameworks with ordinary Bayesian semantics).
Instead of an IRL or CIRL approach to value learning, I propose to rely primarily on linguistic dialogues that are grounded in a fully interpretable representation of preferences. A crux for this is that I believe success in the current stage of humanity’s game does not require loading very much of human values.
There’s a lot of similarity. People (including myself in the past) have criticized Russell on the basis that no formal model can prove properties of real-world effects, because the map is not the territory, but I now agree with Russell that it’s plausible to get good enough maps. However:
I think it’s quite likely that this is only possible with an infra-Bayesian (or credal-set) approach to explicitly account for Knightian uncertainty, which seems to be a difference from Russell’s published proposals (although he has investigated Halpern-style probability logics, which have some similarities to credal sets, he mostly gravitates toward frameworks with ordinary Bayesian semantics).
Instead of an IRL or CIRL approach to value learning, I propose to rely primarily on linguistic dialogues that are grounded in a fully interpretable representation of preferences. A crux for this is that I believe success in the current stage of humanity’s game does not require loading very much of human values.