Dan Balsam: belief

Transcript context

…So let's talk black sparse featurizers. Yeah. This kind of looks to me like if an SAE and an MOE had a baby where Yeah. We have kind of the sparseness of the SAEs, but instead of just it being a single scaler at each point on this kind of super long sparse vector. Now each of those little positions in this, like, very long concept vector is itself a little network. And because of that, now we have room for kind of a richer representation of concepts. But the kind of same SAE trick of, like, localizing concepts to individual spots on the on the super big sparse thing is the same with just this kind of additional enhancement that now allows you to have, again, richer representations, and you can look inside for, like, geometries even within these little blocks. I think the easiest way to think about it is yeah. Like, it's a generalization of an SAE where an SAE assumes that features are one dimensional. And instead, you just don't have to do that. So instead of a scalar for every feature, you can have a vector for every feature…

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