Sure, I'll just write some short points and you can ask questions about it. I'll stick to AlphaGo because less is known about what industry experts are doing with self driving cars, so for all I know they may possesses some magic I'm not aware of. The laziest thing I can do with SDC's is make an argument from authority, since a lot of academic experts have condemned the idea that we are anywhere near fully autonomous SDC's.
Regarding AlphaGo, the issues are:
DNN's are a very sloppy model, in the technical sense coined by Sethna (I can provide citations for the interested). In particular, it was found by Zhang et al (https://arxiv.org/pdf/1611.03530.pdf?from=timeline&isappinstalled=0) that DNN's, among other things, can achieve zero training error on randomly labeled or randomly generated data, pushing their generalization error arbitrarily high. To me this implies that DNN's have such enormous expressiveness that they can effectively memorize the dataset. With enough throughput and GPU toasters, you can span such an enormous portion of the Go gamespace that you can out muscle a human. Essentially it doesn't win via intelligence but via brutish Input/Output superiority that a human brain does not have access to. Consider the learning efficiency as a better measure (how many games must I win per game rank?). DeepMind is now moving on to the real time strategy computer game Starcraft which I think will illustrate this point very poignantly, since the data is much harder to handle. Moreover, they are much more carefully forcing I/O limitations on their "AI" algorithms so that I/O is properly normalized out.
All this said, clearly DNN's will have niche applications, it's just that they have been portrayed (largely by the media) in a highly misleading manner.
As far as i know Go gamespace is practically infinite, so sheer brute force and memory isn't enough.