Re:Deep learning is hitting a wall?
by idealmedtech on Wednesday March 09, 2022 @10:33PM
(#30622967)
I think he does offer some compelling arguments in this article, worth at least examining rather than dismissing based on his past skepticism. Particularly about the potential harmony between deep learning and symbolic manipulation, which is enormously difficult (a far cry from the set-and-forget style that has taken DL so far), but perhaps cracking AGI will require us to do a little more difficult work. I'd love to be proved wrong here, though! My depth of understanding on DL is pretty shallow.
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We are a very long way from even having 0.0001% of the compute required to produce a weak AGI.
like everything, it will take much more resources and time than we can predict today with our best estimates, partly because that's just how these things turn out, and mostly because a true AGI will likely require billions of neural networks all adapting and swapping neurons and communications pathways amongst themselves and training themselves at the same time they are doing useful work.
we have no idea how to do anything like this at scale, yet.
We have no idea what scale is required for AGI. Could be brain scale, could be 100x less, could be 100x more. No clue.
Re:Deep learning is hitting a wall?
by garymarcus on Wednesday March 09, 2022 @10:46PM
(#30623020)
exactly - the article is a call to arms to do that difficult work.
Re:Deep learning is hitting a wall?
by 9wzYQbTYsAIc on Wednesday March 09, 2022 @10:49PM
(#30623033)
Why not return to the roots of Parallel Distributed Processing?