content: This sounds like a very rosy view of the success of machine learning with respect to auto-driving cars. Since the current batch depends on being spoon fed data over pre-scanned routes, can't deal with snow, and can't be operated in heavy rain because of safety concerns I'd say it's a little early to raise the flag and declare success. Just the basic ability to evaluate and understand what the nature of objects that may appear in its path (including the surface condition of what it is driving on) seems to be lacking. Will the car slam on the brakes or attempt avoidance for any object in it's path? If not it needs to understand what is living and what isn't and not run over the former especially if it's a small human child that just ran in front of it or for whatever reason has decided to lie down  in the middle of the road. The outputs are limited but the inputs are unbounded especially if the car gets into a 'no-win' scenario where the least awful outcome must be selected for example choose to crash into another car or leave the road surface if it is unable to make the stop for someone who just stepped into it's path or some other emergent condition that presents itself. A human brain could decide in a split second whether or not leaving the road surface presents greater danger to life but this is almost impossible for a computer to decide because the decision requires understanding the world. Choosing to leave the road in one situation may not be a problem, or might get the passenger killed, or it might kill scores of pedestrians, or anything in between. Letting a computer freely operate a two ton hunk of metal among humans is serious business and the bar really isn't that low.