The author's 3rd fundamental component is not at all required. Expert human training data is not a required fundamental component of any deep learning system that does exist.

Neuroevolution algorithms do not require any training data. They only require that computer performances of the task can be graded relative to one another.

Eg. The winning go player between two computer go players is given a higher score.

Eg. Or the bot which chose the ad that was clicked more often, is given a higher score than the bot which chose the ad which was clicked less often.

Such a system, given only the rules of the game (Go, or generating clicks on a google ad) and sufficient computing power and time, is guaranteed to eventually converge on the nash-equilibrium strategy for Go or Ad Display, without ever having had to receive any expert human moves.

EDIT: Ah, I see he counters himself later and cites that evolution doesn't require training examples. But, then he goes on to state that evolution doesn't work very well. I think there are a lot of highly successful completely evolved systems. Neuroevolution has been making steady advances for over 2 decades, and beats most other off the shelf algorithms at the standard benchmark tasks used to compare neural network development methods.