Ultimate Intelligence Part II: Physical Measure and Complexity of Intelligence
Abstract
We continue our analysis of volume and energy measures that are appropriate for quantifying inductive inference systems. We extend logical depth and conceptual jump size measures in AIT to stochastic problems, and physical measures that involve volume and energy. We introduce a graphical model of computational complexity that we believe to be appropriate for intelligent machines. We show several asymptotic relations between energy, logical depth and volume of computation for inductive inference. In particular, we arrive at a "black-hole equation" of inductive inference, which relates energy, volume, space, and algorithmic information for an optimal inductive inference solution. We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to the physical limits of intelligent computation in our universe.
Keywords
Cite
@article{arxiv.1504.03303,
title = {Ultimate Intelligence Part II: Physical Measure and Complexity of Intelligence},
author = {Eray Özkural},
journal= {arXiv preprint arXiv:1504.03303},
year = {2016}
}
Comments
This paper was initially submitted to ALT-2014. We are taking the valuable opinions of the anonymous reviewers into account. Many thanks to Laurent Orseau for his constructive comments on the draft, which inspired this revision. arXiv admin note: substantial text overlap with arXiv:1501.00601 This is a major revision over the last version edited