3 ms·

> They don't get "shocked", or hit the wrong pedal, and have faster reaction times. Yes they do. You could perhaps argue they do so less.

by rue 16y ago

Groxx 16y ago
OK, kernel panics aside. Unless they're using a random number generator seeded with outside data, all computer operation can be called deterministic and 100% predictable. Because it's 100% repeatable given identical preconditions. People? Not so / not currently doable. If the wrong operation for a situation were performed, it'd be due to a higher-level decision going wrong, not the command. Meanwhile, people can "hit the brakes" (high-level command) and instead slam the accelerator (command not carried out). And fully believe they hit the brakes, and that the car accelerated uncontrollably.
- rue 16y ago
  Even discounting buggy software; think hardware, wiring. It is quite possible that a malfunction will take place (they do, constantly). I could go for "fully computerised traffic would be significantly safer than human traffic", but absolutes about the former being unfailing are just silly.
  - stretchwithme 16y ago
    I think we'll need multiple processors thinking independently and reaching consensus before the gas pedal is pushed and letting any single processor hit the brakes (and disengage the gas, of course). Also good would be to allow other cars to request that a car stop, perhaps even communicating with the traffic control network to make it stop. And of course, there will be the Android app the pedestrian can use as a last resort to avoid beng run over that stops any cars heading for the phone. :-)
    - rue 16y ago
      Reminds me of a 0.1-version of this application that I heard about some years ago: some guy saw a fairly fast-moving heading toward a red light with pedestrians crossing with the driver looking down at something. Guesstimating the car was about to be too close to stop in time, the guy threw his cell at the car and got the driver's attention.
      - stretchwithme 16y ago
        it was self defense, officer
- sukuriant 16y ago
  Even still, let's imagine they're performing using a pseudo-random number generator, and let's imagine they're using a trained neural network at some part of the program. The training (genetic or back-propagation based) didn't account for a very specific scenario, and the tool mis-classifies it, telling the computer to do the wrong thing. If you're suggesting that we know what a computer will do on every single given input, we don't. That's in a class of unsolvable problems, as it requires the halting problem to be solved. [Added the following section after being down-voted] A "shock", in the deterministic computer world, could be an incorrect classification resulting in an incorrect action. The results are similar to when a human is shocked and doesn't act correctly. Is the scenario I've painted limited to neural nets? No, any trained classifier, even any program at all may have "minor" slips like this. (When dealing with an analogue world, there's a ridiculously large number of situations that may exist). But you know what? Humans don't perform perfectly; and a computer that's been shown to perform the correct final* response 100% of the experimental time, and 99.9997% of the real time is competent at the activity, and trustworthy. Disclaimer: I love machine learning, I love neural networks, and I see great usefulness for them in this world; however, I also believe that the perfect combination of variables could yield unusual results. Is it unlikely? Incredibly. Possible? yes. * Consider a situation where a child is playing with a ball. The computer may not recognize that anomaly on the side of the road as the child and the ball, or even as something to be avoided. If the computer recognizes the anomaly as something that needs more information; then tells the car to slow down until it can acquire that information, then recognizes it as something that needs to be avoided and does so, successfully, safely, and with sufficient space for the conceivable outcomes that may have resulted with this child; then, while the computer couldn't discern the object at first, it did in the end and adapted to the situation appropriately. The "final response" was correct. [edit: rather than simply down-vote me, please tell me where I'm wrong so I can learn.]
- krschultz 16y ago
  Wow. I don't even know where to start on that one, because that is about 100% the opposite of true. I work on autonomous driving robots. Teams I have been on in the past won extremely competitive autonomous vehicle competitions. Our robots crashed all the time. ALL THE TIME! There is nothing deterministic about how autonomous systems work. In fact our algorithms were by definition random number generators! We randomly generated "hypotheses" and then had heuristic tests that figured out which was the most likely trail and then chose it. It worked well, 99% of the time. But that 1% was a killer. Mind you, since we were winning we were doing better than the other solutions which tried to be more deterministic and use maps or things like that. We were basically stateless which reduces some of the feedback associated with getting messed up in some of your estimations. The problem is all at the low level, not the high level. Give a robot a perfect map and it is easy to find a way around safely. The problem comes determining what the hell the camera is seeing (lasers are a lot easier, but not as flexible and more expensive). That is why the bulk of the effort is focused on computer vision not path finding, path finding algorithms are simple. Sure actuating the pedals is fairly trivial, but to call autonomous systems "deterministic" is scary to someone who knows how fickle they are. I'm a huge advocate of self-driving cars, but we are not there yet, and when we get there we need to do a pile of peer review on the code (which better be open source). You never know what race conditions will blow it all up.
  - Groxx 16y ago
    It's deterministic. You can run a fully-accurate simulation of your entire setup in a computer, feed in the current input data, and get the precise output in the simulation that the real one would get. That's essentially the definition of deterministic. If your machine is powerful enough, you can even do it before the real one completes its operations. Anything deterministic is predictable in this manner. That you can't predict it, or that you don't have a computer powerful enough to predict it has nothing to do with something not being deterministic, nor predictable. Even a massive, PRNG-influenced neural network is deterministic. ------------ On an aside for people who are thinking of quoting the halting problem at me: The halting problem requires that the detector returns an answer, and it must be true or false and accurate, or the proof through paradox fails. Therefore, it cannot simulate the operation of what it's detecting, because there could be an infinite loop which would cause it to run without end. You also cannot merely abort it after a while, as that would be either a 3rd return state ("unknown") or an inaccurate response.

> They don't get "shocked", or hit the wrong pedal, and have faster reaction times. Yes they do. You could perhaps argue they do so less.

by rue 16y ago

> They don't get "shocked", or hit the wrong pedal, and have faster reaction times. Yes they do. You could perhaps argue they do so less.

Groxx 16y ago

OK, kernel panics aside. Unless they're using a random number generator seeded with outside data, all computer operation can be called deterministic and 100% predictable. Because it's 100% repeatable given identical preconditions. People? Not so / not currently doable. If the wrong operation for a situation were performed, it'd be due to a higher-level decision going wrong, not the command. Meanwhile, people can "hit the brakes" (high-level command) and instead slam the accelerator (command not carried out). And fully believe they hit the brakes, and that the car accelerated uncontrollably.

rue 16y ago
Even discounting buggy software; think hardware, wiring. It is quite possible that a malfunction will take place (they do, constantly). I could go for "fully computerised traffic would be significantly safer than human traffic", but absolutes about the former being unfailing are just silly.
- stretchwithme 16y ago
  I think we'll need multiple processors thinking independently and reaching consensus before the gas pedal is pushed and letting any single processor hit the brakes (and disengage the gas, of course). Also good would be to allow other cars to request that a car stop, perhaps even communicating with the traffic control network to make it stop. And of course, there will be the Android app the pedestrian can use as a last resort to avoid beng run over that stops any cars heading for the phone. :-)
  - rue 16y ago
    Reminds me of a 0.1-version of this application that I heard about some years ago: some guy saw a fairly fast-moving heading toward a red light with pedestrians crossing with the driver looking down at something. Guesstimating the car was about to be too close to stop in time, the guy threw his cell at the car and got the driver's attention.
    - stretchwithme 16y ago
      it was self defense, officer
sukuriant 16y ago
Even still, let's imagine they're performing using a pseudo-random number generator, and let's imagine they're using a trained neural network at some part of the program. The training (genetic or back-propagation based) didn't account for a very specific scenario, and the tool mis-classifies it, telling the computer to do the wrong thing. If you're suggesting that we know what a computer will do on every single given input, we don't. That's in a class of unsolvable problems, as it requires the halting problem to be solved. [Added the following section after being down-voted] A "shock", in the deterministic computer world, could be an incorrect classification resulting in an incorrect action. The results are similar to when a human is shocked and doesn't act correctly. Is the scenario I've painted limited to neural nets? No, any trained classifier, even any program at all may have "minor" slips like this. (When dealing with an analogue world, there's a ridiculously large number of situations that may exist). But you know what? Humans don't perform perfectly; and a computer that's been shown to perform the correct final* response 100% of the experimental time, and 99.9997% of the real time is competent at the activity, and trustworthy. Disclaimer: I love machine learning, I love neural networks, and I see great usefulness for them in this world; however, I also believe that the perfect combination of variables could yield unusual results. Is it unlikely? Incredibly. Possible? yes. * Consider a situation where a child is playing with a ball. The computer may not recognize that anomaly on the side of the road as the child and the ball, or even as something to be avoided. If the computer recognizes the anomaly as something that needs more information; then tells the car to slow down until it can acquire that information, then recognizes it as something that needs to be avoided and does so, successfully, safely, and with sufficient space for the conceivable outcomes that may have resulted with this child; then, while the computer couldn't discern the object at first, it did in the end and adapted to the situation appropriately. The "final response" was correct. [edit: rather than simply down-vote me, please tell me where I'm wrong so I can learn.]
krschultz 16y ago
Wow. I don't even know where to start on that one, because that is about 100% the opposite of true. I work on autonomous driving robots. Teams I have been on in the past won extremely competitive autonomous vehicle competitions. Our robots crashed all the time. ALL THE TIME! There is nothing deterministic about how autonomous systems work. In fact our algorithms were by definition random number generators! We randomly generated "hypotheses" and then had heuristic tests that figured out which was the most likely trail and then chose it. It worked well, 99% of the time. But that 1% was a killer. Mind you, since we were winning we were doing better than the other solutions which tried to be more deterministic and use maps or things like that. We were basically stateless which reduces some of the feedback associated with getting messed up in some of your estimations. The problem is all at the low level, not the high level. Give a robot a perfect map and it is easy to find a way around safely. The problem comes determining what the hell the camera is seeing (lasers are a lot easier, but not as flexible and more expensive). That is why the bulk of the effort is focused on computer vision not path finding, path finding algorithms are simple. Sure actuating the pedals is fairly trivial, but to call autonomous systems "deterministic" is scary to someone who knows how fickle they are. I'm a huge advocate of self-driving cars, but we are not there yet, and when we get there we need to do a pile of peer review on the code (which better be open source). You never know what race conditions will blow it all up.
- Groxx 16y ago
  It's deterministic. You can run a fully-accurate simulation of your entire setup in a computer, feed in the current input data, and get the precise output in the simulation that the real one would get. That's essentially the definition of deterministic. If your machine is powerful enough, you can even do it before the real one completes its operations. Anything deterministic is predictable in this manner. That you can't predict it, or that you don't have a computer powerful enough to predict it has nothing to do with something not being deterministic, nor predictable. Even a massive, PRNG-influenced neural network is deterministic. ------------ On an aside for people who are thinking of quoting the halting problem at me: The halting problem requires that the detector returns an answer, and it must be true or false and accurate, or the proof through paradox fails. Therefore, it cannot simulate the operation of what it's detecting, because there could be an infinite loop which would cause it to run without end. You also cannot merely abort it after a while, as that would be either a 3rd return state ("unknown") or an inaccurate response.