Oh wait, are you the first author on this paper? I didn’t make the connection until I got around to reading your recent post.
So when you talk about moving to a hierarchical human model, how practical do you think it is to also move to a higher-dimensional space of possible human-models, rather than using a few hand-crafted goals? This necessitates some loss function or prior probability over models, and I’m not sure how many orders of magnitude more computationally expensive it makes everything.
Yup! And yeah I think those are open research questions—inference over certain kinds of non-parametric Bayesian models is tractable, but not in general. What makes me optimistic is that humans in similar cultures have similar priors over vast spaces of goals, and seem to do inference over that vast space in a fairly tractable manner. I think things get harder when you can’t assume shared priors over goal structure or task structure, both for humans and machines.
Oh wait, are you the first author on this paper? I didn’t make the connection until I got around to reading your recent post.
So when you talk about moving to a hierarchical human model, how practical do you think it is to also move to a higher-dimensional space of possible human-models, rather than using a few hand-crafted goals? This necessitates some loss function or prior probability over models, and I’m not sure how many orders of magnitude more computationally expensive it makes everything.
Yup! And yeah I think those are open research questions—inference over certain kinds of non-parametric Bayesian models is tractable, but not in general. What makes me optimistic is that humans in similar cultures have similar priors over vast spaces of goals, and seem to do inference over that vast space in a fairly tractable manner. I think things get harder when you can’t assume shared priors over goal structure or task structure, both for humans and machines.