Capsules are kind of like that effort, for the current AI push. They're meant to make it possible to understand smaller entities and how they pertain to a larger more complex entity. The smaller entities can inform as to the identity of the larger entity (particularly in combination, and increase or decrease accuracy depending on the combination), without having to have a perfect or clear picture of that larger entity.

In the semantic web, a complex entity might be cross-linked to a dozen or several dozen smaller entities that help describe the larger entity. With enough of the smaller entities (attributes of the larger entity) you could map and narrow down what the larger entity possibilities are.

Another obvious attraction of capsules would be the potential to recognize, tune, and maybe even manually introduce labeled concepts in nets during training -- perhaps enabling one shot learning?

This is not obvious to me at all. Can you please explain?