alignment problem involves “aligning” the representation spaces between machines of the world and that of the human. alternative perspective: teach humans new concepts to understand/communicate better
feature attribution doesn’t work
We take that perspective because many of the intersectional intepretability doesn’t work well (feature permutation, etc.)—feature attribution type analyses (“Impossibility Theorems Been Kim”) actually has no correlation with predictive results.
feature information store in models is unrelated to model edit success
i.e.: knowledge storing location located using ROME technique, though it gives you a sense of the location to store information, doens’t correlate to success of model editing.
can we use ML to teach people?
for instance, we can teach grandmasters to play chess using AlphaGo, and see if we can make a quantitative impact.
A concept is a unit of knowledge that’s useful for a task. Two properties:
- minimality: irrelavent information has been removed
- transferable: it can be taught atomically
filtering for good concepts
Representing a concept as a sparse vector as the latent space. We can check if a concept is transferable by teaching a student agent by doing KL divergence.
instead of doing demonstration learning on machines, do it on HUMANS. Filter for the concepts that are well operationalized.
recap: using a dense network to embed the network, and then MCTS.