I think Hinton is going the right direction, but I also think he’s a long way behind HTM. Some of the things he says is incorrect (like that visual perception is a “routing problem” when an entity’s perspective changes). I think he misunderstands SDRs as some type of multidimensional array (which indeed might have some similar matching properties). But he says nothing about neuronal receptivity zones, which are core to HTM theory. And his association of “capsules” to mini-column seems errant. Take this screenshot, for example:
Each of those long ellipses are supposed to represent a capsule, and each capsule is detecting whether a feature exists (nose, mouth, face). But each capsule is also supposed to be a mini-column. The addition of a hierarchy confuses the matter more. This seems like it’s reaching in the same direction as HTM, but I think HTM explains the workings of mini-columns much more realistically.
I also think Mr. Hinton has some micro vs macro concepts convoluted a bit. In some spots he seems to be talking about mini-columns and spatial feature recognition, but then he jumps to explaining object orientation in space using the same terms, which makes this confusing.
He is framing this as a computer problem. How can we make computers intelligent? He’s using neuroscience to inform his research, so logically he’d come across the same structures that underly HTM theory (mini-columns, layers, columns).
We have always framed our research as an intelligence problem. At the core, the question is “why are we intelligent?” In order to answer this question, we’ve chosen to research the neocortex and build intelligent machines to prove out the theories.
I think this perspective difference is a big reason why there are disconnects within the ML and HTM communities.