"Presumably the advantage of..."


by Paul Christiano Mar 16 2016

The problem of conservatism is an extension of the supervised learning problem in which, given labeled examples, we try to generate further cases that are almost certainly positive examples of a concept, rather than demanding that we label all possible further examples correctly\. Another way of looking at it is that, given labeled training data, we don't just want to learn a simple concept that fits the labeled data, we want to learn a simple small concept that fits the data \- one that, subject to the constraint of labeling the training data correctly, predicts as few other positive examples as possible\.

Presumably the advantage of this approach---rather than simply learning to imitate the human burrito-making process or even human burritos, is that it might be easier to do. Is that right?

I think that's a valid goal, but I'm not sure how well "conservative generalizations" actually address the problem. Certainly it still leaves you at a significant disadvantage relative to a non-conservative agent, and it seems more natural to first consider direct approaches to making imitation effective (like bootstrapping + meeting halfway).

Of course all of these approaches still involve a lot of extra work, so maybe the difference is are expectations about how different research angles will work out.