I don't know what people mean when they say 'the near-term possibility of autonomous driving'. Some people mean a car that can go everywhere and do everything a

I don't know what people mean when they say 'the near-term possibility of autonomous driving'. Some people mean a car that can go everywhere and do everything a human can. I'm thinking about robotaxis in the Bay area that drive along established circuits and do pick-ups and drop offs in designated zones like bus stops. That's what I'm expecting to see in the 5 year time frame, and being able to apply autonomous driving technology to that limited extent opens a very lucrative new market. Any way you look at the autonomous driving problem it's a hard problem, but every few days it seems there's some paper coming out that demos a new optimization to one aspect or another of the autonomous vehicle development pipeline. The world of modular deep learning systems, where you have various parent and child subsystems that can be quickly trained and swapped out is very much an open and field of research and my bet is on agile teams that can build and test models faster, and can take things from research to implementation in less time than the competition. Drive.ai's best trade secret is the system they have for vastly reducing the amount of training data they need to get a result, and vastly reducing the amount of human labour needed to annotate or clean up said training data. I don't know what the hype is. People are expecting terminator robots or something, that's not going to happen anytime soon. But progress in Machine learning has been steady and driving for a decade and shows know signs of letting up in the forseeable future, because we just keep opening up new frontiers to explore. There's so much to do.