I shared this blog with you veen, and I find his input interesting on recent news, whether it's this particular case http://ideas.4brad.com/comma-ai-cancels-comma-one-add-box-after-threats-nhtsa

Black box

Comma is not the only company trying to build a system with pure neural networks doing the actual steering decisions (known as “path planning”.) NVIDIA’s teams have been actively working on this, as have several others. They plan to make commentary to NHTSA about these element of the regulations, which should not be forbidding this approach until we know it to be dangerous.

It is challenging. It’s hard to do QA on neural networks. You can examine any single state of them, but not really understand them. You can fix the errors they make, but not know how you fixed it or whether your fix is going to work in other cases. On their own that sounds too scary, but the problem is they are outperforming other algorithms at many of the problems they are being applied to.

If you have two systems:

    A conventionally coded system which you fully understand, which has one safety incident per 100,000 miles
    A black box machine learning system that has one safety incident per 150,000 miles, but you have minimal understanding of

Which is the one that is better to put on the road? It’s not a no-brainer.

or about the Trolley Problems : http://ideas.4brad.com/yikes-even-barak-obama-wants-solve-robocar-trolley-problems-now-0

The cost of solving may be much higher than people estimate

People grossly underestimate how hard some of these problems will be to solve. Many of the situations I have seen proposed actually demand that cars develop entirely new capabilities that they don’t need except to solve these problems. In these cases, we are talking about serious cost, and delays to deployment if it is judged necessary to solve these problems. Since robocars are planned as a life-saving technology, each day of delay has serious consequences. Real people will be hurt because of these delays aimed at making a better decision in rare hypothetical situations.