RE: DMS vital as full-auto. 'a long road'13 Oct 2019 09:43
https://www.economist.com/leaders/2019/10/10/driverless-cars-are-stuck-in-a-jam
excerpts -
'Jim Hackett, the boss of Ford, acknowledges that the industry “overestimated the arrival of autonomous vehicles”. Chris Urmson, a linchpin in Alphabet’s self-driving efforts (he left in 2016), used to hope his young son would never need a driving licence. Mr Urmson now talks of self-driving cars appearing gradually over the next 30 to 50 years. Firms are increasingly switching to a more incremental approach, building on technologies such as lane-keeping or automatic parking. A string of fatalities involving self-driving cars have scotched the idea that a zero-crash world is anywhere close. Markets are starting to catch on. In September Morgan Stanley, a bank, cut its valuation of Waymo by 40%, to $105bn, citing delays in its technology.'
Hit the brakes
One is that, for all the advances in machine learning, machines are still not very good at learning. Most humans need a few dozen hours to master driving. Waymo’s cars have had over 10m miles of practice, and still fall short. And once humans have learned to drive, even on the easy streets of Phoenix, they can, with a little effort, apply that knowledge anywhere, rapidly learning to adapt their skills to rush-hour Bangkok or a gravel-track in rural Greece. Computers are less flexible. AI researchers have expended much brow-sweat searching for techniques to help them match the quick-fire learning displayed by humans. So far, they have not succeeded.
Another lesson is that machine-learning systems are brittle. Learning solely from existing data means they struggle with situations that they have never seen before. Humans can use general knowledge and on-the-fly reasoning to react to things that are new to them—a light aircraft landing on a busy road, for instance, as happened in Washington state in August (thanks to humans’ cognitive flexibility, no one was hurt). Autonomous-car researchers call these unusual situations “edge cases”. Driving is full of them, though most are less dramatic. Mishandled edge cases seem to have been a factor in at least some of the deaths caused by autonomous cars to date. The problem is so hard that some firms, particularly in China, think it may be easier to re-engineer entire cities to support limited self-driving than to build fully autonomous cars (see article).
The most general point is that, like most technologies, what is currently called “AI” is both powerful and limited. Recent progress in machine learning has been transformative. At the same time, the eventual goal—the creation in a machine of a fluid, general, human-like intelligence—remains distant. People need to separate the justified excitement from the opportunistic hyperbole. Few doubt that a completely autonomous car is possible in principle. But the consensus is, increasingly, that it is not imminent. Anyone counting on AI for business or pleasure could do worse than remember that cautionary tale
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