If the "actual machine learning practitioners" can break down behavior to the neuron level, then I'd say they understand it to some degree.
The reason that many of these problems still exist is because chasing down individual errors is a seductive waste of time. If you have to tell the network "here's how you handle this one pattern and here's how you handle this other one", then you're building an expert system. Yes it's tempting to correct errors as you see them pop up. But it's better to construct a network that can learn from data to handle any situation that you didn't think of specifically.
Building a robust network requires lots of time and data. There will always be edge cases that cannot be fixed individually. The fact that Tesla or anyone else hasn't built a system for X with no embarrassing edge cases yet does not mean that we should go back to coding individual instructions and conditionals line by line.