[vladoh]

While driving, do you understand how the engine ECU of your car computes how much fuel to inject into the cylinder, how the ECUs distribute the power, and the braking force on individual wheels, or how your rear wheel steering calculates the turning angle of the rear wheels based on your seed and steering input?

You don't need to know most of the details how your car works in order to drive. You need much more knowledge to build one, yes, but not to drive.

There are different levels of abstractions and depending on your problem you need to understand them only up to a certain level. And different people have different problems to solve.

In most real-world problems today, the difficult part is indeed the data, not the underlying math of the activation function, loss function, or optimizer. Just Google "data-centric AI Andrew Ng" to read more on the topic from one of the most well-known people in ML.

[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?