Consider how this could be tested. One would write a program that generates a virtual rubik’s cube, and passes this on to the AI to be solved (this avoids the complexity of first having to learn how to control robotic hands). It can’t just randomly assign colours to sides, lest it end up with an unsolveable cube. Hence, the preparatory program starts with a solved cube, and then applies a random sequence of moves to it.
This will almost certainly be done on the same computer as the AI is running on. A good AI, therefore, should be able to learn to inspect its own working memory, and observe other running threads on the system—it will simply observe the moves used to shuffle the cube, and can then easily reverse them if asked.
It is possible, of course, for test conditions to be altered to avoid this solution. That would, I think, be a mistake—the AI will be able to learn a lot from inspecting its own running processes (combined with the research that led to its development), and this behaviour should (in a known Friendly AI) be encouraged.
the problem with this is the state space is so large that it cannot explore every transition, so it can’t follow transitions backwards in a straight forward manner as you’ve proposed. It needs some kind of intuition to minimize the search space, to generalize it.
Unfortunately I’m not sure what that would look like. :(
I wasn’t suggesting that the AI might try to calculate the reverse sequence of moves. I was suggesting that, if the cube-shuffling program is running on the same computer, then the AI might learn to cheat by, in effect, looking over the shoulder of the cube-shuffler and simply writing down all the moves in a list; then it can ‘solve’ the cube by simply running the list backwards.
I’m not 100% sure of the mechanism of said observations, but I’m assuming a real AI would be able to do things on a computer that we can’t—much as we can easily recognise an object in an image.
You’re assuming the AI has terminal access. Just because our brains are implemented as neurons doesn’t mean we can manipulate matter on a cellular scale.
Consider how this could be tested. One would write a program that generates a virtual rubik’s cube, and passes this on to the AI to be solved (this avoids the complexity of first having to learn how to control robotic hands). It can’t just randomly assign colours to sides, lest it end up with an unsolveable cube. Hence, the preparatory program starts with a solved cube, and then applies a random sequence of moves to it.
This will almost certainly be done on the same computer as the AI is running on. A good AI, therefore, should be able to learn to inspect its own working memory, and observe other running threads on the system—it will simply observe the moves used to shuffle the cube, and can then easily reverse them if asked.
It is possible, of course, for test conditions to be altered to avoid this solution. That would, I think, be a mistake—the AI will be able to learn a lot from inspecting its own running processes (combined with the research that led to its development), and this behaviour should (in a known Friendly AI) be encouraged.
the problem with this is the state space is so large that it cannot explore every transition, so it can’t follow transitions backwards in a straight forward manner as you’ve proposed. It needs some kind of intuition to minimize the search space, to generalize it.
Unfortunately I’m not sure what that would look like. :(
(Wow, this was from a while back)
I wasn’t suggesting that the AI might try to calculate the reverse sequence of moves. I was suggesting that, if the cube-shuffling program is running on the same computer, then the AI might learn to cheat by, in effect, looking over the shoulder of the cube-shuffler and simply writing down all the moves in a list; then it can ‘solve’ the cube by simply running the list backwards.
Oh I see: for that specific instance of the task.
I’d like to see someone make this AI, I want to know how it could be done.
Observe the contents of RAM as it’s changing?
I’m not 100% sure of the mechanism of said observations, but I’m assuming a real AI would be able to do things on a computer that we can’t—much as we can easily recognise an object in an image.
You’re assuming the AI has terminal access. Just because our brains are implemented as neurons doesn’t mean we can manipulate matter on a cellular scale.