There are different ways of training things than relying other people's data. For instance alpha zero is still the most remarkable achievement in ML or 'AI' in

There are different ways of training things than relying other people's data. For instance alpha zero is still the most remarkable achievement in ML or 'AI' in my opinion, and it used 0 human games in learning to play chess. It training was entirely from self-play. I think models that rely on learning from these sort of weighted monte carlo type systems are likely to end up far more robust than ones that try to learn from humans. In the AlphaZero case this was provably true. But in the more general case, the optimal solution for humans and the optimal solution for machines is not necessarily the same. So in driving, for instance, it may be the case that operating a vehicle in one way is safest for a human - yet not necessarily so for a machine. It's not hard to come up with examples. We are awesome at intuition and awful at microsecond reactions. Computers are awful at intuition and awesome at microsecond reactions. The optimal style of 'play' is likely going to differ. Yet if the machine is trained based on human data it would end up trying to shoehorn itself into replicating human behavior which may be suboptimal for it. All that said, this sounds more like marketing talk. My guess is that Apple simply decided that self driving is extremely difficult and is going to come with some major growing pains which could negatively affected their crafted brand image, making it generally a poor investment of time and resources.