4 ms·
> A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behav
by lostmsu 3y ago
> A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behavior is only a few tens of steps. We don't know how to make such a machine, and we wouldn't know how to program it.
IMHO, that makes no sense. Assuming we have a really large state machine with all many possible situations in a state of driving on a straight highway with a few vehicles with more or less constant relative velocity, determining if we need to make a turn is definitely doable in a few computation steps.
- leroy-is-here 3y agoWe attempted the symbolic route of AI programming and it didn’t work because it is explosive in computation time and exhaustive to model in code. We can’t program our AI “models” now because they are based on chains of probabilities. Therefore they can run in real time. They don’t have any real logic that we specifically programmed, only what the model was able to infer and then encode. Perhaps there should be a marriage between these two attempts of AI.
- marcosdumay 3y ago> it is explosive in computation time Hum... The numerical route for AI necessarily has the same complexity in computation time. Those two have equivalent semantics. If you can get a result with one, you can get the same result with the other, it can just differ on how easy it is to program. By the way, symbolic AI can learn too, you don't need to model things in code.
- leroy-is-here 3y agoI was talking about attempts such as expert systems. We attempted to symbolically encode things as sums of their parts in plain terms, e.g. a bicycle is two wheels, an axle, a chain, some peddles, etc. but in more detail than that. The problem is the level of detail required for general knowledge: firstly it is hard to codify, secondly as it grows computation becomes exponentially more expensive. We as humans have the intelligence to simply see for fractions of a second and then just know — we can know the answers to math problems and how a person is feeling. The symbolic AI programming was an attempt to codify what symbols we are seeing but they are so diverse and nuanced it became apparent this was an ineffective scientific route.
- marcosdumay 3y agoYeah, that's because you decided it should be a hand-coded expert system. If you review the set of hand-coded neural networks, you will discover they even more bloated, and have an extreme low ceiling on usefulness. Besides, keep in mind that the currently useful NNs are stuff that requires dozens of GB of memory to run. Anyway, training a NN is also an attempt to codify what symbols we see on the world.
- deleted 3y ago[deleted]
- lostmsu 3y agoI was talking about hardware rather then software here. How do lane keeping assists work?
- leroy-is-here 3y agoThere’s no difference between hardware and software when it comes to running a program, really. They are both machines at the end of the day.