It sounds like your reasoning goes like this: my interactions with things which are supposedly AI are bad. Self-driving cars are also some form of AI, therefore self-driving cars must be bad and or far away too.
Did I understood you right?
The problem with that logic is that there is nothing in common in implementation/architecture/incentives between the things you mention and self-driving technology.
Nobody, well nearly nobody, tries to implement self-driving cars in a blackbox “AI” fashion. What I mean is that we don’t just throw sensor data at a neural network, then squint and say “i recon it is going to drive well now”. That would be madness.
Most approaches break down the problem into sub-problems covered by sub-systems. The sub-systems are fed information with known error properties and engineered to the specifications. The failure modes are painstakingly traced through and documented. Then in turn assemblies of these subsystems and the whole are reasoned similarly. Fault trees are drawn, the operational domain is considered. The reasoning why the engineers think the system is safe and have the right redundancies in place is more complicated than the code itself.
Some of these sub-systems are implemented using what one would call “AI”. Particularly in the object recognition domain that seems to be the state of the art. But the failure modes and shortcomings of these systems are considered and reasoned about the same way you would do the same with a good old-fashioned kalman-filter based sub-system. It is known that they are going to fail in various situations in various ways. The trick is to engineer the whole system such that it still remains safe despite these sub-systems having these characteristics.
I’m not saying that we will have safe self-driving soon. What i’m saying is that you can’t reason about self-driving cars by saying “commercial entity X is spamming me with bad marketing crap. People talk about AI behind said marketing crap. Therefore self driving cars are far away.”
