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Is the next AI winter already on its way? Provocative post by @filippie509https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-on-its-way/ …
I'll note AI summer bias (the gap between production ML and research ML is staggering - it'll take years to even out) but the article didn't convince me winter is coming. A major omission from discussion is AutoML (architecture search etc) taking advantage of scaled compute.pic.twitter.com/UMgWptv1k1
Even w/o AutoML, excess neural compute (which good companies never have) has been highly effective when used in anything "brute"-ish (hyperparameter search, ensembling, ...). Even if base advances slowed I doubt we've thoroughly searched that academic space given limited compute.
Since we are being provocative, the argument would have more weight if it supplied any evidence :) Falling tweets: the number of published articles and production systems is skyrocketing. Scaling: model size = quality? No. SDCs: cherry picking bad design says nothing about DL.
Hopefully the next spring will be around sub-symbolic / symbolic transitions & integration. Not quite as catchy as "deep learning" though :)
My take is it will be about common sense, and that is actually related to sub-symbolic/symbolic transitions. But we have to address Moravec's paradox. This is the glowing obstacle to all those AI wonders: that inability for models to acquire the stuff we cannot verbalize.
Agree with most of it and it's still backprop with more layers and 1000s of PhD students' parameter tuning. However, I'd say google bought deep mind with the sole reason to create PR from the very start. They knew with catchy PR stunts they can sell GPU power on their servers.
That might be true, though even if so, it must have very quickly got out of control. People really do want to believe they have some magical bullet at hand. It is a very powerful illusion.
That apostrophe in the title is provocative as hell
Dickishness aside (sorry...), v interesting post
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