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Just like people tend to overestimate the scope & intelligence of contemporary AI, they tend to underestimate how much you can achieve with these simple & narrow systems, when sufficiently scaled and widely deployed.https://twitter.com/erikbryn/status/1025940657167388673 …
Sure, deep learning looks dumb from every angle, and it does not constitute a meaningful step towards AI. Simultaneously, deep learning is profoundly important and has the potential to have a considerable impact on most industries. It doesn't have to be clever to be useful.
so because DL scores better on benchmarks, it would offer something ML generally can't? The way I see it, DL only REVIVED dreams of ML having a much larger impact, when this potential has ALWAYS been there, even before DL.
Rightly said. Was looking for an answer to this for a long time. Thanks!
I’ve been thinking about this a lot lately. Do you have any reading suggestions on modular neural networks? I can’t find any specific literature, but I wanted to see how it performs on complex tasks
This is precisely the conclusion from the NTAF report on AI and ML : http://informationsystemsbiology.blogspot.com/2018/04/artificial-intelligence-applications.html?m=1 …
I see DL as a tool that scales on the engineering side: the general DL framework where you get optimization ‘for free’ allows many people to use ML to unstructured data, outside of ML experts
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