Algorithmic Specified Complexity Part III: Measuring Meaning in Images
On this episode of ID The Future, Robert Marks and Winston Ewert, both of the Evolutionary Informatics Lab, discuss three of their recently published papers dealing with evolutionary informatics, algorithmic specified complexity and how information makes evolution work.
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
The paper is available online at: https://robertmarks.org/REPRINTS/2015%20Measuring%20meaningful%20information%20in%20images.pdf