advocates of #machinelearning, I am told that you all know that (current) #ML is limited. fair enough. but which limits are you willing to *publicly* acknowledge?https://twitter.com/NotSimplicio/status/1173373706674085888 …
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Replying to @GaryMarcus
Lack of generalization outside of the training set makes DL (maybe not all *possible* ML) inappropriate for many problems. Ultimately, the limitation of ML is our own: failure to architect solutions that lever it but also (will) use other tech to advantage
4 replies 2 retweets 13 likes -
Replying to @titudeadjust
that of course has been my central point since 1998; it rarely appears in discussions of deep learning results, though at this point it is widely known within the field.
4 replies 0 retweets 10 likes -
Replying to @GaryMarcus @titudeadjust
No, the reason why the "DL community" is largely focused on supervised is because it works. Self-supervised/unsupervised is harder. Today, self-sup works *really* well in NLP. Not so much in vision. Yet.
3 replies 0 retweets 11 likes
it works well for some things in nlp, poorly for others, as i explained here:https://www.wired.com/story/adaptation-if-computers-are-so-smart-how-come-they-cant-read/ …
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