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  anchorContext: '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\\.',
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  text: '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?\n\nI 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](https://medium.com/ai-control/mimicry-maximization-and-meeting-halfway-c149dd23fc17)).\n\nOf 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.',
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