However, as someone who builds them for vision applications I'm increasingly convinced that some form of ANN will underlie AGI - what he calls a universal algorithm.
If we assume that general intelligence comes from highly trained, highly connected single processors (neurons) with a massive and complex sensor system, then replicating that neuron is step one - which arguably is what we are building, albeit comparatively crudely, with ANN's.
If you compare at a high level how infants learn and how we train RNN/CNNs they are remarkably similar.
I think where the author, and in general the ML crowd focuses too much is on unsupervised learning as being pivotal for AGI.
In fact if you look again at biological models the bulk of animal learning is supervised training in the strict technical sense. Just look at feral children studies as proof of this.
Where the author detours too much is assuming the academic world would prove a broader scope for ANN if it were there. In fact however research priorities are across the board not focused on general intelligence and most machine learning programs explicitly forbid this research for graduate students as it's not productive over the timeline of a program.
Bengio and others I think are on the right track, focusing on the question of ANN towards AGI and I think it will start producing results as our training methods.