The proposal here is to include a term in the loss function that incentivizes the AI to have a human-compatible ontology. For a cartoonish example, imagine that the term works this way: “The AI model gets a higher score to the degree that people doing ‘digital neuroscience’ would have an easier time, and find more interesting things, probing its ‘digital brain.’” So an AI with neurons corresponding to diamonds, robbers, sensors, etc. would outscore an AI whose neurons can’t easily be seen to correspond to any human-familiar concepts.
I think that a lot depends on what kind of term you include.
If you just say “find more interesting things” then the model will just have a bunch of neurons designed to look interesting. Presumably you want them to be connected in some way to the computation, but we don’t really have any candidates for defining that in a way that does what you want.
In some sense I think if the digital neuroscientists are good enough at their job / have a good enough set of definitions, then this proposal might work. But I think that the magic is mostly being done in the step where we make a lot of interpretability progress, and so if we define a concrete version of interpretability right now it will be easy to construct counterexamples (even if we define it in terms of human judgments). If we are just relying on the digital neuroscientists to think of something clever, the counterexample will involve something like “they don’t think of anything clever.” In general I’d be happy to talk about concrete proposals along these lines.
(I agree with Ajeya and Mark that the hard case for this kind of method is when the most efficient way of thinking is totally alien to the human. I think that can happen, and in that case in order to be competitive you basically just need to learn an “interpreted” version of the alien model. That is, you need to basically show that if there exists an alien model with performance X, there is a human-comprehensible model with performance X, and the only way you’ll be able to argue that for any model we can define a human-comprehensible model with similar complexity and the same behavior.)
I think that a lot depends on what kind of term you include.
If you just say “find more interesting things” then the model will just have a bunch of neurons designed to look interesting. Presumably you want them to be connected in some way to the computation, but we don’t really have any candidates for defining that in a way that does what you want.
In some sense I think if the digital neuroscientists are good enough at their job / have a good enough set of definitions, then this proposal might work. But I think that the magic is mostly being done in the step where we make a lot of interpretability progress, and so if we define a concrete version of interpretability right now it will be easy to construct counterexamples (even if we define it in terms of human judgments). If we are just relying on the digital neuroscientists to think of something clever, the counterexample will involve something like “they don’t think of anything clever.” In general I’d be happy to talk about concrete proposals along these lines.
(I agree with Ajeya and Mark that the hard case for this kind of method is when the most efficient way of thinking is totally alien to the human. I think that can happen, and in that case in order to be competitive you basically just need to learn an “interpreted” version of the alien model. That is, you need to basically show that if there exists an alien model with performance X, there is a human-comprehensible model with performance X, and the only way you’ll be able to argue that for any model we can define a human-comprehensible model with similar complexity and the same behavior.)