From within "the field", there isn't a single "deep learning community" counterposed to an "other stuff community". There's a broad machine learning community, in which deep learning is currently the most fashionable approach, and there are small factions of pro- and anti-"deep" partisans, who consider their approach the One True Approach.
Science is usually unkind to partisans, and so most people I've met don't really bother with that kind of partisanship. Asking, "Can deep learning get us all the way to AGI, yes or no?" is the wrong question. The proper questions are:
* What is deep learning good or bad at?
* What do we mean by AGI? (Similarly: data analytics, machine perception, statistical AI, etc?)
* Why should we target one goal (high-dimensional statistics, missing-data reconstruction, reinforcement learning, AGI) over another?
* What methods currently afford us the best contributions towards the scientific and engineering goals we're targeting?
However, that is exactly not the paper's own stance, which is explicitly constructive to the field. And it is reciprocated! Check out all the NeurIPS papers citing and relating their work to the suggestions here: https://scholar.google.com/scholar?cites=8504723689348856287...
While I misspoke, my point remains that both this contribution and it's reception are part of a healthy community, not a case of "moving past" or exposing shortcomings of a field..