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Royal Truman, en su análisis publicada en el ''[[Journal of Creation]]'' analiza dos familias de enfoques: El una La primera derivada del trabajo de Shannon y la otra segunda derivada de la obra de [[Werner Gitt|Gitt]].<ref name=truman1>{{cita publicación|título=Information Theory-Part 1:Overview of Key Ideas|autor=Truman, Royal|año=2012|publicación=[[Journal of Creation]]|volumen=26|número=3|página=101-106|issn=1036-2916}}</ref> Truman also mention the algorithm definition of informationtambién menciona la definición algorítmica de la información, developed by desarrollada por Solomonoff, Kolmogorov and with contributions from y con contribuciones de [[Gregory Chaitin]] but that has not been discussed in his articlepero que no ha sido discutida en su artículo.<ref name=truman1 /> According to [[Stephen Meyer]], scientists usually distinguish two basic types of information: meaningful or functional information and so-called Shannon information<ref name=doubt>{{cite book|author=Meyer, Stephen C|authorlink=Stephen Meyer|title=Darwin's Doubt: The Explosive Origin of Animal Life and the Case for Intelligent Design|publisher=HarperOne|/HarperCollins Publishers|location=Seattle, WA|year=2013|page=164-168|isbn=978-0-06-207147-7}}</ref> (named after Claude Shannon, who developed statistical information theory). Shannon information is really not the same thing as meaningful information. Meaningful information, encoded into a language, can be measured statistically, per Shannon, but the measure is of the redundancy of the symbols, the so-called Shannon entropy,<ref group=nota>Shannon entropy is the average unpredictability in a random variable, which is equivalent to its information content.</ref> not a measure of the "information content", or meaning. Shannon, for example, can be uses to measure the "information content" of a set of random symbols that have no meaning.