Yes. In that sense it is equivalent to a compression method that uses K-means clustering to do the partitioning. But because there are many more bits of knowledge out there than there are bits in the models you end up with this amorphous smear. I'm sure some of those bits carry more information about some of the concepts but in general - and that's the way it is explained in just about all the papers I've read that I can still comprehend - every weight contributes to the output in a dense model. In an MOE things get more complicated but there within every 'MOE' column the same principle applies.

I'm fine with taking this article at the high level conceptual level, that's effectively the kind of thinking that led to MOE to begin with. But I don't see that as a pathway to being able to 'explain' a model. Because if you could actually explain a model you could engineer the weights, you wouldn't need any training because it implies that you can improve on the model by simply performing surgery on the weights to get it to do what it does but better. Now, obviously, unlike the author I'm a complete nobody but for the last two years or so I've been trying very hard to understand what is going on under the hood.

It's akin to feature engineering - or rather, the lack thereof - and to see a strong suggestion that we maybe be able to reverse that is interesting, but absent hard proof at how a particular model reached a particular conclusion it is difficult to give that a lot of weight right now. It is possible that such proof is buried somewhere in TFA and I overlooked it during reading. But I think this is speculative rather than that it proves anything and before I'm going to be convinced I would like a little more evidence.