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Autonomous driving is the ultimate example. You could use deep learning to create an impressive self-driving car prototype in 2015 on a shoestring budget (Comma did exactly that, using Keras). Five years and billions of $ later, the best DL-centric driving systems are still L2+.
Every app demo based on GPT-3 follows this pattern. You can build the demo in a weekend, but if you invest $20M and 3 years fleshing out the app, it's unlikely it will still be using GPT-3 at all, and it may ever meet customer requirements
The reason why is that parametric models trained with gradient descent make it easy to automate something, but have little ability to deviate from the patterns they've learned. Meanwhile, the real world is full of surprises, and handling it requires the ability to adapt.
Freeman Dyson along with Claude Shannon are a powerful couple of mathematicians and together they make a strong case for using "Information Theory: dealing with human language, implying DL is redundant.
Very interesting perspective. What about BERT? It’s being used in Google search, right?
That's okay. He is in google.
Find the best and most efficient DL code for "document classification", and compare it to my Inf. Th. way of accomplishing this important feature: FBK page "Koos Van Der Wilt", Post May 11 (11 Mei). I have tons more info; please ask me.
I agree, of course
Self-driving may turn out to be an easier problem than creative writing. If you throw enough data at it (what Tesla is doing), you might reduce the error rate to an acceptable level.
I was waiting for the optimistic last tweet but your thread just ended here.... 
no hope for deep learning?
It seems to deploy well in hybrid systems where DL solves a large-scale, 'easy' task and the last-mile is solved by a human.
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