Algorithmic specified complexity.
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Information theory is a well developed field, but does not capture the essence of what information is. Shannon Information captures something in its definition of improbability as information. But not all improbable events convey information. Kolmogorov complexity captures the idea of information as something easily described. But not all easily described objects are information. The proposed Algorithmic Specified Complexity takes into account both Shannon Information and Kolmogorov complexity to gain a fuller evaluation of information. We demonstrate this concept and develop several examples. We show the low probability of high Algorithmic Specified Complexity. We apply the concept to both images and functional machines from the Game of Life.