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> You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy yeah, instead you get hallucinations, mus

> You don't switch from seeing cars to seeing nothing to seeing an elephant just because the input got a little noisy yeah, instead you get hallucinations, muscle twitches, seizures, mental breaks and many more ways a brain can fail to do it's job.

TeMPOraL 9y ago
Sure, but a) we have thousands of years of experience with those, and b) none of those are caused by little random noise in the visual input. Brains are incredibly robust against noisy data.
- nkozyra 9y ago
  Robustness is a key property of the methods and models used in self driving cars. For a human, the noise isn't bad recognition, it's corrupted sensor data via distraction. It's one of the most common causes of accidents. For the AI agent an elephant will never appear unless it's been trained to be recognized. More likely it's recognized as an obstacle, which is ok. But keep in mind that years of Google's autonomous vehicle success make this pretty improbable, certainly less than in the average driver. Much of the fear here is irrational and natural when humans lose control.
  - TeMPOraL 9y ago
    I'm not trying to express a fear that comes from humans losing control. I don't fear that - in fact, I can't wait until the day comes no humans are allowed to drive on public roads. I was just answering about the concerns of self-driving systems relying solely (or primarily) on visual data from cameras. It's true this is how humans drive, but human visual system is much more complicated than what the current (published) state of the art in image processing seems to be. I do not trust visual-only systems today (especially if they employ deep learning shenanigans). I know those problems aren't insurmountable, but I'd feel much safer if they threw in a LIDAR there too.
    - nkozyra 9y ago
      While I'm sure more sensors gives a better chance at consensus I don't think relying solely on visual data is a problem if done well (low false positive false negative rate, comprehensive trained, fault tolerant)
- jclardy 9y ago
  Our vision and persistent world model are good, but all humans are overconfident in their ability to drive in poor conditions (or even normal conditions, for many people.) A computer isn't going to look down at a text message on their phone, fall asleep at the wheel, or get in a car while intoxicated. All which occur literally every day. People take driving for granted, because (at least in the US) we have to. Our sprawling suburbs couldn't function without it, so the barrier to entry is simply being a teenager. Would I completely trust Tesla's auto-driving? No, not at all. But I at least trust them as much as I do an average driver, which is still just barely. The difference with Tesla's system is that one failure leads to improvement of the entire fleet, whereas one human failure maybe improves that one human driver. That means self-driving cars will continuously improve, while humans will just continue to be terrible drivers.
  - photojosh 9y ago
    Yes! Tesla will take any death due to a failure of self-driving quite seriously indeed and work to fix the failure mode. Whereas humans get the same kinds of fatal crashes over, and over, and over... The Australian government has a "Black Spot" [1] program to provide federal funds (often millions) to fix up locations with a history of crashes. Bet it's cheaper to make those fixes to software. [1] http://investment.infrastructure.gov.au/funding/blackspots/ http://investment.infrastructure.gov.au/funding/blackspots/
- taneq 9y ago
  Some adversarial methods work on black-box networks - has any work been done on applying those to human vision? We know there are a wide range of simple inputs that produce unexpected results (eg. optical illusions).
- keymone 9y ago
  a) well, no. we only have about 100 years dealing with these problems in context of driving cars and we're really bad at it; b) that seems like shifting goalposts, what we care about is whether driver's picture of the world is correct and actions are sensible with respect to that picture, it just so happens that driverless cars' picture is visual (or close to visual), but if you're judging them on entirety of input and side-effects it's only fair to judge our brains by the same standard.

TeMPOraL 9y ago

Sure, but a) we have thousands of years of experience with those, and b) none of those are caused by little random noise in the visual input. Brains are incredibly robust against noisy data.

nkozyra 9y ago
Robustness is a key property of the methods and models used in self driving cars. For a human, the noise isn't bad recognition, it's corrupted sensor data via distraction. It's one of the most common causes of accidents. For the AI agent an elephant will never appear unless it's been trained to be recognized. More likely it's recognized as an obstacle, which is ok. But keep in mind that years of Google's autonomous vehicle success make this pretty improbable, certainly less than in the average driver. Much of the fear here is irrational and natural when humans lose control.
- TeMPOraL 9y ago
  I'm not trying to express a fear that comes from humans losing control. I don't fear that - in fact, I can't wait until the day comes no humans are allowed to drive on public roads. I was just answering about the concerns of self-driving systems relying solely (or primarily) on visual data from cameras. It's true this is how humans drive, but human visual system is much more complicated than what the current (published) state of the art in image processing seems to be. I do not trust visual-only systems today (especially if they employ deep learning shenanigans). I know those problems aren't insurmountable, but I'd feel much safer if they threw in a LIDAR there too.
  - nkozyra 9y ago
    While I'm sure more sensors gives a better chance at consensus I don't think relying solely on visual data is a problem if done well (low false positive false negative rate, comprehensive trained, fault tolerant)
jclardy 9y ago
Our vision and persistent world model are good, but all humans are overconfident in their ability to drive in poor conditions (or even normal conditions, for many people.) A computer isn't going to look down at a text message on their phone, fall asleep at the wheel, or get in a car while intoxicated. All which occur literally every day. People take driving for granted, because (at least in the US) we have to. Our sprawling suburbs couldn't function without it, so the barrier to entry is simply being a teenager. Would I completely trust Tesla's auto-driving? No, not at all. But I at least trust them as much as I do an average driver, which is still just barely. The difference with Tesla's system is that one failure leads to improvement of the entire fleet, whereas one human failure maybe improves that one human driver. That means self-driving cars will continuously improve, while humans will just continue to be terrible drivers.
- photojosh 9y ago
  Yes! Tesla will take any death due to a failure of self-driving quite seriously indeed and work to fix the failure mode. Whereas humans get the same kinds of fatal crashes over, and over, and over... The Australian government has a "Black Spot" [1] program to provide federal funds (often millions) to fix up locations with a history of crashes. Bet it's cheaper to make those fixes to software. [1] http://investment.infrastructure.gov.au/funding/blackspots/ http://investment.infrastructure.gov.au/funding/blackspots/
taneq 9y ago
Some adversarial methods work on black-box networks - has any work been done on applying those to human vision? We know there are a wide range of simple inputs that produce unexpected results (eg. optical illusions).
keymone 9y ago
a) well, no. we only have about 100 years dealing with these problems in context of driving cars and we're really bad at it; b) that seems like shifting goalposts, what we care about is whether driver's picture of the world is correct and actions are sensible with respect to that picture, it just so happens that driverless cars' picture is visual (or close to visual), but if you're judging them on entirety of input and side-effects it's only fair to judge our brains by the same standard.