The main problem is human overconfidence in the resulting functions, which are often measuring things humans didn't realize was embedded in the data (eg with tumor recognition), or are utterly hopeless at representing the infinite edge cases that the real world can present (eg self-driving cars).
It's one thing to have hoped that these methods could solve these problems when the improvements were coming rapidly, but there will always be a limit to how well these systems can perform. And the problem is that they fail in entirely non-intuitive ways, making human oversight to correct for errors very difficult or impossible as well.
It's one thing to have hoped that these methods could solve these problems when the improvements were coming rapidly, but there will always be a limit to how well these systems can perform. And the problem is that they fail in entirely non-intuitive ways, making human oversight to correct for errors very difficult or impossible as well.