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by coldtea
727 days ago
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>AIXI doesn’t exactly need you to give it a training set, just put it in an environment where you give it a way to select actions, and give it a sensory input signal, and a reward signal. That's still a training set, just by another name. And with the environment being the world we live in, it would be constrained by the local environment's possible states, the actions it can perform to get feedback on, and the rate of environment's response (the rate of feedback). Add the quick state-space inflation in what it is considering, and it's an even tougher deal than getting more training data for an LLM. |
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I don’t understand what you mean by the comment about state-space inflation. Do you mean that the world is big, or that the number of hypotheses it considers is big, or something else?
If the world is computable, then after enough steps it should include the true hypothesis describing the world among the hypotheses it considers. And, the probability it assigns to hypotheses which make contrary predictions should go down as soon as it sees observations that contradict those other hypotheses. (Of course, “the actual world” including its place in it, probably has a rather long specification (if it even has a finite one), so that could take a while, but similar things should apply for good approximations to the actual world.)
As for “it’s possible actions”, “moving around a robot with a camera” and “sending packets on the internet” seem like they would constitute a pretty wide range of possible actions.
Though, even if you strip out the “taking actions” part, and just consider the Solomonoff induction part (with input being, maybe a feed of pairs of “something specifying a source for some information, like a web-address or a physical location and time, along with a type of measurement, such as video” and “encoding of that data”, should get very good at predicting what will happen, if not “how to accomplish things”. Though I suppose this would involve some “choosing a dataset”.
AIXI would update its distribution over environments based on its observations even when its reward signal isn’t changing.