[fxtentacle]

Except that we can build cars that work. Whereas for DL AI, in most practical applications, there is like 10% edge cases where things just randomly explode. But don't take it from me, just read "Distributional Reinforcement Learning with Quantile Regression" by Google Brain and Deepmind and they'll tell you

"Even at 200 million frames, there are 10% of games where all algorithms reach less than 10% of human. This final point in particular shows us that all of our recent advances continue to be severely limited on a small subset of the Atari 2600 games."

In short, current AI approaches cannot even reliably win video games from 40 years ago, no matter how much $$$ you burn on GPU power.

How do you expect a non-expert to know if their problem is in the 10% that works well, the 80% that works tolerably, but worse than traditional algorithms, or the 10% where all bets are off?