I’m re-watching a lot of these research meetings again, and I noticed something interesting in this one that I had glossed over the first time through. Marcus talks about it a bit starting around 7:02. Hinton’s 2017 paper discusses it in section 5.
A capsule is able to learn on its own different ways of spanning the space of variations in the way a given digit is drawn (in the case of MNIST). By perturbing these different dimensions, you can see that the capsules learn interesting things like scale, thickness, scew, etc. as well as more abstract distortions.
Personally, I have been focused on the idea of pooling and making associations, but this extracting of properties/dimensions seems intuitively to me that it must also be a core part of what the cortical circuitry is doing as well.
I know @Bitking has mentioned something related to this as well on a few occasions. For example:
Has anyone given some thought to an HTM-compatible algorithm for this type of extraction process?