gibberfish

Except the overwhelming majority of Europeans alive today, including those in power, have come of age at a time when their country's colonial efforts were already a thing of the past, so it's hardly fair to throw them in with their parents' or grandparents' generation.

Still doesn't mean this clump of rock has any kind of intentionality.

Not so unlikely if you consider survivorship bias.

The comment you originally replied to points out all the ways in which the deep learning "interface" is not relatively simple, at least not if your problem has any sort of deviation from the most simple use cases. For a user, making a jpeg is a one-time one-command affair. If you think training a neural net can be reduced to this level of abstraction (with the knowledge we have today), you have either never used them in practice, or you've been very lucky with the complexity of the problems you've encountered so far.

There's ongoing work in adversarial examples for neural networks. This could conceivably be used to exploit self-driving cars, making it see things that aren't there.

That argument applies to the general population maybe, but that doesn't mean it also applies to the population of Google employees. Everyone there passed their hiring standards, so everyone has proved their individual merit already.

Also, looking at accidents per distance driven hides hides the fact that more time spent on the road is a societal choice that also leads to more fatalities.

Also, you can fit just about anything to two points.