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eli_sennesh comments on Debunking Fallacies in the Theory of AI Motivation - Less Wrong
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A technical point here: we don't learn a raw classifier, because that would just learn human judgments. In order to allow the system to disagree with a human, we need to use some metric other than "is simple and assigns high probability to human judgments".
I totally agree that a good understanding of multi-level models is important for understanding FAI concept spaces. I don't have a good understanding of multi-level maps; we can definitely see them as useful constructs for bounded reasoners, but it seems difficult to integrate higher levels into the goal system without deciding things about the high-level map a priori so you can define goals relative to this.
Right: and the metric I would propose is, "counterfactual-prediction power". Or in other words, the power to predict well in a causal fashion, to be able to answer counterfactual questions or predict well when we deliberately vary the experimental conditions.
To give a simple example: I train a system to recognize cats, but my training data contains only tabbies. What I want is a way of modelling that, while it may concentrate more probability on a tabby cat-like-thingy being a cat than a non-tabby cat-like-thingy, will still predict appropriately if I actually condition it on "but what if cats weren't tabby by nature?".
I think you said you're a follower of the probabilistic programming approach, and in terms of being able to condition those models on counterfactual parameterizations and make predictions, I think they're very much on the right track.