I'm reminded of a friend who worked in radio hardware design. They'd use simulation and fuzzy/genetic algorithms to create a circuit, and then verify its performance with experiments. But they couldn't always say exactly why the circuit worked, just that it met the performance criteria.
It's an interesting divergence in software, between those who manage complexity by adding more human-understandable abstraction, and those who manage it by just verifying the results, letting the complexity fly free. All the ML stuff is definitely taking big steps down the latter path.
It's an interesting divergence in software, between those who manage complexity by adding more human-understandable abstraction, and those who manage it by just verifying the results, letting the complexity fly free. All the ML stuff is definitely taking big steps down the latter path.