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Your correct - that one doesn't propose an architecture but suggests that others do so. It also is arguably from "outside" the main deep learning community, an

Your correct - that one doesn't propose an architecture but suggests that others do so. It also is arguably from "outside" the main deep learning community, and in the context of the OP could be interpreted as corrective. 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&as_sdt=5,33&sciodt=0,33&hl=en 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..

eli_gottlieb 8y ago
>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.. 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?
- evrydayhustling 8y ago
  Sounds like we are on exactly the same page. Defining "the field" precisely is a rhetorical device. Defining and working on problems is what creates progress.

eli_gottlieb 8y ago

>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.. 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?

evrydayhustling 8y ago
Sounds like we are on exactly the same page. Defining "the field" precisely is a rhetorical device. Defining and working on problems is what creates progress.