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Comment by Daviey | Hacker News Reader

You've never been temporary blinded by a low-sun? Never sneezed and had your eyes closed at the wrong moment?

This is the same thing..

Humans know when they can't see properly. The problem with adversarial examples is that the neural net thinks it's doing great. You have to manually specify every possible failure mode for the neural net before hand -- if you miss one, you might have an accident.

Usually. Drunk and sleepy drivers blur this distinction, but you still make a good point.

The real world is not filled with adversarial examples. Further, NN often does not think it's doing great when providing off the wall examples. Further this is a car, with GPS, where simply stopping is a completely viable option 99% of the time.

The truth is people really really stuck a driving, but it's easy enough that the vast majority of mistakes don't result in any problems.

>simply stopping is a completely viable option 99% of the time.

For a naive definition of "vaiable" that does not take into account other people that need to use the roadways. A car stopping or even slowing drastically in a lane will have the same negative affect on the transportation system as a minor accident.

These cars are unlikely to stay stopped for long as there are going to be passengers in them.

Now, we could get into hypothetical situations, but the reality is minor accidents are already really common like with so many other things machines can be better than people without being perfect.

I really want to see how cars deal with the massive code explosion as they have to account for things like tak tak gangs.

I posit now: "as the problems with automated cars become evident, solutions provided will tend towards Skynet as t increases."

You mean like we have every day already? Human drivers constantly make mistakes, and in many cases they aren't even aware of it or their affect on the drivers around them. Most don't cause an accident, but the behavior doesn't get corrected and that is what it eventually leads to.

Slowing for a turn then deciding to continue on, merging without checking blindspots, running red lights, speeding next to the short on-ramp lane, not following a yield to right turn sign, ignoring people in crosswalks, etc. All of these things happen all the time, I see at least one example every time I go out somewhere.

Aren't general adversarial networks designed to solve this?

> Humans know when they can't see properly

There's plenty of conditions that are more difficult to humans: cars that are painted with the same color of asphalt seem to disappear in highways. Also, fog.

Motorcyclists have a word for this phenomenon. It's called SMIDSY - "sorry mate, I didn't see you." Clear road, good vision, bike traveling in a straight line within the speed limit, car still somehow pulls out and hits the bike. Even worse, the times when the bike is stationary at a red light and the car just drives through it.

And it's not limited to cyclists. I think the general term used in cognitive science is "Looked but failed to see".

It's not. When you're being blinded by sun, you don't suddenly see elephants or spaceships or comic books. You also don't think the world just disappeared - you know to just ignore the visual input.

Also, I'm more worried about temporary, unexpected configuration of things that suddenly make a NN see something weird.

That doesn't sound as an insurmountable problem.

Humans don't see elephants suddenly because a prior is telling them that an elephant is improbable in that context.

Something like that in a computer system shouldn't be above the current capabilities.

I agree it shouldn't be above current capabilities. But I'm yet to see anyone doing something like this. And I'm definitely wary about taking the results of toying with "deep learning" and saying we've can put that on a car and expect good results - which is an impression I sometimes get in articles about those topics.

Also, this will stop working the day an elephant escapes from a ZOO and manages to get onto a highway. All the autopilots will declare the elephant's presence as impossible, and will happily crash into it.

Not likely. Computers can do a double-take, just like humans.

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