The number one item on the list is:
“Better reinforcement learning / integration of deep learning and reinforcement learning. Reinforcement learning algorithms that can reliably learn how to control robots, etc.”
To a large extent, this depends on how well we can map features and performance from a simulator (where we would perform reinforcement learning) to the real world. So far, this has been a challenge, but I’ve seen several companies recently working on this problem.
The other seven items on the list are all worth a read, too.
And if you’d like to learn more from Ian, he has a book, and also he’s an instructor in Udacity’s Deep Learning Foundations Nanodegree Program.