Dr. Winston Ewert, a Senior Research Scientist at both Biologic Institute and the Evolutionary Informatics lab, discusses the mathematical foundation for why we know Mount Rushmore is designed and Mount Fuji isn’t. The mathematical theory of algorithmic specified complexity is introduced and illustrated. A single complex snowflake, for example, displays essentially zero algorithmic specified complexity whereas two identical snowflakes earns a high algorithmic specified complexity. The model discussed by Dr. Ewert can also measure algorithmic specified complexity in units of bits in the context of poker. Dr. Ewert explains how a Royal Flush has a high algorithmic specified complexity of about 16 bits whereas a poker hand with a single pair has essentially zero algorithmic specified complexity. The theory Dr. Ewert discusses is developed in the paper:
Winston Ewert, William A. Dembski, Robert J. Marks II, “Algorithmic Specified Complexity,” in Engineering and the Ultimate: An Interdisciplinary Investigation of Order and Design in Nature and Craft, edited by Jonathan Bartlett, Dominic Halsmer and Mark Hall (Blyth Institute Press, 2014), pp.131-149.
Conway’s Game of Life is played on a rectangular grid. Cells live or die depending on the cells that surround them. Hobbyists have designed highly complex and interesting patterns using Conway’s four simple rules of birth, death and survival. Patterns include oscillators, spaceships and glider guns. Dr. Winston Ewert explains how the theory of algorithmic specified complexity can be applied to measure, in bits, the degree to which these cellular automata life forms are designed. The discussion centers around the peer-reviewed journal article:
Winston Ewert, William A. Dembski and Robert J. Marks II, “Algorithmic Specified Complexity in the Game of Life,” IEEE Transactions on Systems, Man and Cybernetics: Systems, Volume 45, Issue 4, April 2015, pp. 584-594 DOI: 10.1109/TSMC.2014.2331917
In this, the third and final podcast of the series, Dr. Winston Ewert explains the role of context in measuring meaning in images. A non-humanoid gelatinous alien would assign no meaning to the faces on Mount Rushmore if the alien had never before seen a humanoid. Humans, on the other hand, have the context of familiarity with human heads and historical figures that allow them to assxign high algorithmic specified complexity when viewing Mount Rushmore. Information theoretic-based algorithmic specified complexity applied to images is developed in the peer-reviewed archival journal article:
Winston Ewert, William A. Dembski, Robert J. Marks II. “Measuring meaningful information in images: algorithmic specified complexity,” IET Computer Vision, 2015, Vol. 9, #6, pp. 884–894 DOI: 10.1109/TSMC.2014.2331917
See also: Robert Marks on the paradox challenging physics
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