As we know, Deep Learning faces certain issues (e.g., generalizability, data hunger, etc.). If we want to speculate, which controversial theories do you have in your sights you think that it is worth to look nowadays?

So far, I’ve come across 3 interesting ones:

1. Cognitive science approach by Tenenbaum: Building machines that learn and think like people. It portrays the problem as an architecture problem.
2. Capsule Networks by Hinton: Transforming Autoencoders. More generalizable DL.
3. Neuroscience approach by Hawkins: The Thousand Brains Theory. Inspired by the neocortex.

What are your thoughts about those 3 theories or do you have other theories that catch your attention?