This really affects the nature of the situation. 20 years ago, cars contained microcontrollers with a tiny bit of code which was thoroughly reviewed and tested by skilled professionals. Today, all cars run so much code, even outside of the entertainment system, that the review and testing just can't be the same. (And there's way more programmers, so the range of skill and care is also much wider.)
When the Toyota electronic throttle "unintended acceleration" accidents were in the news, the software was described as a "big bowl of spaghetti" but the NTSB ultimately determined that it was not the cause of the problems. It was drivers using the wrong pedal.
I've long been curious about the "big bowl of spaghetti" comment (and all the other criticisms made by the experts who inspected Toyota's code). There were some extremely serious accusations which don't seem consistent with the fact that the vast majority of Toyotas on the road aren't showing problems caused by their MCU's spaghetti code.
AI takes it to a whole new level. Neural networks are all black box, they can't be reviewed. You feed in your training data, you test it against your test data, and just have faith that it will respond appropriately to a pattern that's slightly different than anything it's seen before. Sometimes the results are surprising.
That's my biggest problem with AI and neural networks. You can't really measure progress here. If you wanted the same safety standards as for every other automotive software you'd have to test drive for hundreds of thousands of kilometres after every change of parameters, because there's no way to know what has changed about the AI's behavior except for testing it thoroughly.
Compare this to classic engineering where you know the changes you've made, so you can rerun your unit tests, rerun your integration tests, check your change in the vehicle and be reasonably sure that what you changed is actually what you wanted.
The other approach to autonomous driving is to slowly and progressively engineer more and more autonomous systems where you can be reasonably sure to not have regressions. Or at least to contain your neural networks to very very specific tasks (object recognition, which they're good at), where you can always add more to your test data to be reasonably sure you don't have a regression.
I don't think we'll see too many cars being controlled by neural networks entirely, unless there's some huge advancement here. Most of the reason we see more neural networks now is that our computing power has reached the ability to train sufficiently complex NNs for useful tasks. Not because the math behind it advanced that much since the 60s.
https://skeptics.stackexchange.com/questions/39559/does-the-...
This really affects the nature of the situation. 20 years ago, cars contained microcontrollers with a tiny bit of code which was thoroughly reviewed and tested by skilled professionals. Today, all cars run so much code, even outside of the entertainment system, that the review and testing just can't be the same. (And there's way more programmers, so the range of skill and care is also much wider.)
We're in a new mountain-of-flaky-software world.