Here's a transcript of the first half, with my changes to the original in brackets: OK, so AIXI is a really simply algorithm. The setting is: we have some sequence of input bits to an algorithm A. It's a stream: i1, i2, i3, etc. And you have an output, a1, a2, a3, etc. And this works in the obvious way: if the input's i0, the output's a0, etc. So the algorithm's going to be, 'A maintains distribution over possible worlds W" So it's going to model the world W as another step, that takes its actions and then returns the next input. [1:05] So the world produces this first input [i1], then A produces this first output [a1], and then the world produces the next input [i2]. And so we can imagine this as being, like A is a computer, and W is everything else in the world. The input stream i1, i2, i3... is the input wire from the world, and the output stream a1, a2, a3... is the output back to the world. Physics [takes the output and the prior world-state and generates the next world-state, which provides the next input]. So, 'A maintains a distribution over possible worlds W' Do you know much about algorithmic information theory? Nothing too deep there, just the definitions. Listener: Such as Solomonoff Induction, Kolmogorov Complexity? So, the universal prior is just this [prior probability that's equal to the sum of all 2^-l(p), where is l(p) is the length of any program p that outputs the given bits] [2:02] It starts with a description [indecipherable] a "uniform" distribution over programs. Obviously, in a uniform distribution, like in some prefix-free encoding, you want the shorter program to be more likely. So we start with a uniform distribution over programs and condition on agreement with observations, amongst all possible programs for the world that would have produced this sequence of inputs [i1, i2, i3...] in response to the outputs from AIXI. So it's conditioned based on this uniform distribution [the universal prior]. Let's call this step k...I've complet

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