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.