This problem is not neglected, and it is very unclear how any insight into why SGD works wouldn’t be directly a capabilities contribution.
I strongly disagree! AFAICT SGD works so well for capabilities that interpretability/actually understanding models/etc. is highly neglected and there’s low-hanging fruit all over the place.
To me, the label “Science of DL” is far more broad than interpretability. However, I was claiming that the general goal of Science of DL is not neglected (see my middle paragraph).
Got it, I was mostly responding to the third paragraph (insight into why SGD works, which I think is mostly an interpretability question) and should have made that clearer.
I think the situation I’m considering in the quoted part is something like this: research is done on SGD training dynamics and researcher X finds a new way of looking at model component Y, and only certain parts of it are important for performance. So they remove that part, scale the model more, and the model is better. This to me meets the definition of “why SGD works” (the model uses the Y components to achieve low loss).
I think interpretability that finds ways models represent information (especially across models) is valuable, but this feels different from “why SGD works”.
Got it, I see. I think of the two as really intertwined (e.g. a big part of my agenda at the moment is studying how biases/path-dependence in SGD affect interpretability/polysemanticity).
I strongly disagree! AFAICT SGD works so well for capabilities that interpretability/actually understanding models/etc. is highly neglected and there’s low-hanging fruit all over the place.
To me, the label “Science of DL” is far more broad than interpretability. However, I was claiming that the general goal of Science of DL is not neglected (see my middle paragraph).
Got it, I was mostly responding to the third paragraph (insight into why SGD works, which I think is mostly an interpretability question) and should have made that clearer.
I think the situation I’m considering in the quoted part is something like this: research is done on SGD training dynamics and researcher X finds a new way of looking at model component Y, and only certain parts of it are important for performance. So they remove that part, scale the model more, and the model is better. This to me meets the definition of “why SGD works” (the model uses the Y components to achieve low loss).
I think interpretability that finds ways models represent information (especially across models) is valuable, but this feels different from “why SGD works”.
Got it, I see. I think of the two as really intertwined (e.g. a big part of my agenda at the moment is studying how biases/path-dependence in SGD affect interpretability/polysemanticity).