[ipiz0618]

"AI" is a very vague term. What you described aren't entirely "machine learning", but a combination of existing linguistic techniques and machine (deep) learning.

People confuse what AI can do, and what is AI all the time. It also doesn't help when there are so many inexperienced data scientist making promises that they can't achieve.

In your example, I'd argue that a human is not necessarily a better driver than a machine. An attentive and careful driver is certainly better than a machine right now, but there are many who drive carelessly. While a person is unlikely to mistake a square stop sign as something else, there are so many drivers that would simply ignore the sign, and traffic lights in general. They'd also drive dangerously because of road rage, and inattentiveness. And the majority of traffic accidents are caused by these drivers. A machine is unlikely to do these.

That said, until we figure out how to run all these deep learning models without a crazily expensive and power-consuming GPU, it is unlikely AI would be used as general purpose programs.

[dreamcompiler]

Whether humans or AI are "better" drivers is completely beside the point. The point is that we can characterize human drivers. We know where they succeed and where they fail, both in a statistical sense and in an individual sense based on their age, attention, vision, chemical impairment, etc. But we cannot characterize ML networks. We take it on faith that they work and then we find (because somebody dies) that they run right into an overturned truck or a pedestrian or under a truck crossing the road.

Until we can characterize the behavior of these systems, they must not be put in control of life-critical processes like driving.

[the_omegist]

Just playing the devil's advocate but : when you take a taxi, what do you know about the driver? You can vaguely see if he's sober and that's all. You EXPECT him to have a driver's license, to have a good eyesight, etc but you KNOW nothing about it. If he has an heart attack while he's driving on the highway, could it have been predicted (by you or the company)? No.

So i don't see why this distinction between AI and humans is made : both are black boxes. Perhaps humans have less "edge cases" but as long as the error level of AI is the same or lower than the one of humans, I don't care if the car crashed because the human driver looked at a sexy woman on an ad on a billboard or because a variable was poorly set in the car's code.

[ipiz0618]

I also agree on this. I think in terms of liability humans who one can sue when they make a mistake is more valuable than a machine.

That's why in life critical applications companies who are capable of taking the risk are scarce, because when accidents happen, the company has to take responsibility. It cannot be resolved by just firing employees.

[henearkr]

You can fix a software, but you can only punish a human driver, hoping it will fix itself. Also, both can be forced to train, but you can reproducibly test only the software, no guarantee that your retrained human driver will not succomb to the same road rage in the near future.

[redis_mlc]

Nonsense.

You can't fix a model to handle unknowns, and you can't test that.

We've seen with Tesla's autopilot software that things like obsolete road markers and overturned trucks are meaningless to software.

[henearkr]

Of course you can!!

Even in something not very well defined as a neural network, you can try to retrain it, or also to modify its architecture, or its postprocessing, and verify reproductibly on test cases that it behaves better.

Also, to address your critics, you can add test cases (just like in any sotware. But actually they also do exactly that for hardware too).