2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/169239We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain the main concepts of this quantitative approach to defining `information'. We discuss the extent to which Kolmogorov's and Shannon's information theory have a common purpose, and where they are fundamentally different. We indicate how recent developments within the theory allow one to formally distinguish between `structural' (meaningful) and `random' information as measured by the Kolmogorov structure function, which leads to a mathematical formalization of Occam's razor in inductive inference. We end by discussing some of the philosophical implications of the theory.37 pages, 2 figures, pdf, in: Philosophy of Information, P. Adriaans and J. van Benthem, Eds., A volume in Handbook of the philosophy of science, D. Gabbay, P. Thagard, and J. Woods, Eds., Elsevier, 2008. In version 1 of September 16 the refs are missing. Corrected in version 2 of September 17Information TheoryMachine LearningStatistics Theorytext