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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 i

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.
- krschultz 16y ago
  Ok yes, if I give the robot the same exact pictures and random generator seeds, it is deterministic. But in the field, even if it is stationary the pictures are never the same from frame to frame and everything relies on a lot a randomness, and random noise. Simulators are almost a waste of time because the real challenge is handling that noise, NOT dealing with the information extracted after the noise. Once you have a clear view of the world, everything it is easy, it is getting the eyes to actually understand anything that is hard.

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.

krschultz 16y ago
Ok yes, if I give the robot the same exact pictures and random generator seeds, it is deterministic. But in the field, even if it is stationary the pictures are never the same from frame to frame and everything relies on a lot a randomness, and random noise. Simulators are almost a waste of time because the real challenge is handling that noise, NOT dealing with the information extracted after the noise. Once you have a clear view of the world, everything it is easy, it is getting the eyes to actually understand anything that is hard.