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Algorithmic Specified Complexity: Measuring Mount Rushmore

Jonathan Witt
Photo credit: Adrian Yau via Unsplash.

On a classic episode of ID The Future, Robert J. Marks and Winston Ewert, both of the Evolutionary Informatics Lab, continue their conversation about three of their recently published papers dealing with evolution, biological information, and what is known as algorithmic specified complexity. Download the podcast or listen to it here.

In this final podcast of the series, Dr. Ewert explains the role of context in measuring meaning in images. A non-humanoid gelatinous alien might assign no meaning to the faces on Mount Rushmore if the alien had never seen a humanoid. But humans, and especially humans familiar with major figures in American history, have the context to recognize that the carved shapes tightly match pre-existing patterns and therefore contain considerable algorithmic specified complexity. Ewert applies all this to cases of high information complexity in biological systems, such as the bacterial flagellum. The focus of the conversation here is the journal article “Measuring Meaningful Information in Images: Algorithmic Specified Complexity,” by Ewert, Marks, and William Dembski.

Dr. Dembski, by the way, is among the speakers at the February 20 Dallas Conference on Science and Faith, sponsored by Discovery Institute Dallas.