>However, it does give us a concrete, non-hand-wavey algorithm which can reasonably be considered "AI, if we ignore resource constraints".
No, not really. "If we ignore resource constraints" is ignoring most of the problem. Using Kolmogorov complexity in the Solomonoff Measure also constitutes ignoring the problem of generalization by assuming an optimal compressor into existence, which again is an issue of the cognitive resources of training data and processing power. Bayesian updating means it will achieve optimal expected reward, but also that AIXI can be "fooled" by the hierarchical nature of real environments' variance[1].
And the whole thing pays no attention to knowledge representation whatsoever.
It's basically a grand victory for the fields of AI and Machine Learning that still tells us basically nothing about how an actually existing, embodied mind has to function, except that statistical learning is most likely the core mechanism in some fashion (after all, neural networks show that the real thing isn't even necessarily Bayesian in any sense).
[1] Benjamin B. Machta, Ricky Chachra, Mark K. Transtrum, and James P. Sethna. Parameter space compression underlies emergent theories and predictive models. Science, 342(6158):604–607, 2013.
> It's basically a grand victory for the fields of AI and Machine Learning that still tells us basically nothing about how an actually existing, embodied mind has to function
Special relativity tells us basically nothing about how an actually existing, physical spaceship has to function; but it does constrain our speculation about space travel (ie. no FTL, the fact that accelerating massive objects requires more and more energy, etc.).
It also provides some handy little suggestions that we may not have anticipated; eg. that mass can be converted into energy, which is certainly useful when trying to come up with practical designs.
But AIXI, I don't see how it constrains anything. It introduces a classification: AIXI type algorithms, and other algorithms. But we don't really know if this is a useful segmentation of the search space, or perhaps as useless as considering the merits of red spaceships vs. spaceships painted with other color.
No, not really. "If we ignore resource constraints" is ignoring most of the problem. Using Kolmogorov complexity in the Solomonoff Measure also constitutes ignoring the problem of generalization by assuming an optimal compressor into existence, which again is an issue of the cognitive resources of training data and processing power. Bayesian updating means it will achieve optimal expected reward, but also that AIXI can be "fooled" by the hierarchical nature of real environments' variance[1].
And the whole thing pays no attention to knowledge representation whatsoever.
It's basically a grand victory for the fields of AI and Machine Learning that still tells us basically nothing about how an actually existing, embodied mind has to function, except that statistical learning is most likely the core mechanism in some fashion (after all, neural networks show that the real thing isn't even necessarily Bayesian in any sense).
[1] Benjamin B. Machta, Ricky Chachra, Mark K. Transtrum, and James P. Sethna. Parameter space compression underlies emergent theories and predictive models. Science, 342(6158):604–607, 2013.