Comment by QuadmasterXLII
3 days ago
this article seems to take it on faith that neurons are aeasily findable, even if their meaning is tricky to untangle: it would be sharply improved by description of how the neurons are being isolated before the meat of the article, the naming of the neurons, is performed. In particular axis aligned semantic directions are incompatible with the classic superposition description, where the meaningful directions are nearly orthogonal, allowing many more to be packed onto the sphere than it has dimensions. (i’m also aware of work showing that Adam or relu pushes semantics onto the axes, but it’s not clear that that’s what he thinks is happening)
I guess it could also be that modern neural networks are so completely not equivariant that my linear algebra intuitions are no longer useful and it’s now common knowledge and or true that neuron 413 is just x[:, :, 413]
The neurons here are MLP neurons, with activations directly after the nonlinearity, so there is definitely a distinguished basis.