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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 eve

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.

philippoi 9y ago
Usually. Drunk and sleepy drivers blur this distinction, but you still make a good point.
Retric 9y ago
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.
- dsfyu404ed 9y ago
  >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.
  - Retric 9y ago
    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.
    - intended 9y ago
      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."
  - jclardy 9y ago
    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.
foota 9y ago
Aren't general adversarial networks designed to solve this?
eeZah7Ux 9y ago
> 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.
taneq 9y ago
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.
- ebalit 9y ago
  And it's not limited to cyclists. I think the general term used in cognitive science is "Looked but failed to see".

philippoi 9y ago

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

Retric 9y ago

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.

dsfyu404ed 9y ago
>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.
- Retric 9y ago
  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.
  - intended 9y ago
    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."
- jclardy 9y ago
  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.

foota 9y ago

Aren't general adversarial networks designed to solve this?

eeZah7Ux 9y ago

> 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.

taneq 9y ago

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.

ebalit 9y ago
And it's not limited to cyclists. I think the general term used in cognitive science is "Looked but failed to see".