>Ok. I can give you a 100 million floats, and tell you that if you multiply, add and threshold them in a certain order against a set of pixels, that tells you if the picture is of a cat or a dog.
My friend, you have not even scratched the surface. First off, an elucidation of the inputs and the procedure by which an output is generated does not an explanation of the system make.
When I look at an image classifier, I want to know what features it's using to make a determination of being cat-like. That way, I can compare that with my own experience to make sure if I cut someone loose with a cat/dog detector, someone doesn't get given the idea that a young bear is a dog. Your trivial AI cat/dog detector may identify cat/dog like features in a still, but that's not equivalent with being able to distill the essence of cat/dog from the reality and common experience of the world around us. If you're going to try to sell me on a system that purportedly knows what something is, I expect it to actually represent the level of intelligence you make it out to possess.The neural networks we manufacture are of a level of magnitude so much narrower than what ML people seem to want the lay person to give them credit for.
Think of it this way:
As a programmer, I am expected to be able to create an accurate enough representation of what is going on in a complex system that a non-programmer can connect what the system is doing to whether or not it is doing what it should be. Given enough time and patience on the non-programmer's part, I should be able to transfer and walk through enough information where the non-programmer suddenly becomes a novice programmer because they have had the same foundational skill and knowledge structures communicated to them.
No one will be satisfied with "I chucked this data in, therefore it's a cat/dog detector now. No more questions." Especially when you start applying that to decisions of life-altering importance. You must be 10% smarter than the piece of equipment for it to be lynchpin in a life-critical application. That means being able to explain what your system does, how changes to inputs will effect it, what it's error margins are, what the safe operating conditions are, when it's plain flat out wrong, and as much as possible, why.
Until such knowledge can be sufficiently communicated, I see no reason to take even the most well-known luminary trying to handwave explicability as anything but trying to avoid having to uncover enough of the mystery of what they are working on in order to meet what has been accepted as sufficient due diligence.
To do so is patently unwise, and implicitly accepts far more egg breaking to make an omelette than we (those whose lives will be in the system's hands) should be willing to entertain.