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Self-driving cars a great example, because in this case there are two competing approaches -- the symbolic one, mostly consisting of handcrafted software encoding human abstractions, and the deep learning one, learned end-to-end. One will get to L4--even L5, the other never will.https://twitter.com/GaryMarcus/status/1003314562261712896 …
It's not that deep learning is intrinsically incapable of driving -- it's that the situation space is extremely high-dimensional (due to edge cases), and that a deep learning system requires to be trained on a *dense sampling* of the same space that the system will operate in.
Because such a representative, dense sampling is impossible to obtain, even when heavily leveraging simulated environments, the symbolic approach will prevail (specifically, an approach that is mostly symbolic but blends human abstractions with learned perceptual primitives)
One day you or you may not have a choice to do otherwise.
@GaryMarcus Tho hesitating to comment w/ two eminences, isn't pragmatic answer likely to lie in combination of (i) symbolic & #ML approaches as part of single SDV system & (ii) constraining domain - in case of roads & driving, limiting to certain routes, pre-mapped, etc.?
Yeah. I was thinking in the same vayne. I guess “amalgams” will prevail for quite some time.
There already exists a neural network that has achieved L5 autonomy. The neural network found in human heads. Why is it impossible for this functionality to be replicated in silicon or any other artificial computation machine ?
Similarly to planes. They have fixed wings and engines, not birdlike flaps. Humanity found a solution more compatible with our technical and engineering capabilities 
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