Spatial and Temporal Abstraction in an Intelligent Machine

Current approaches to AI are limited by several intractable problems. Automated learning of data contexts will allow spatial and temporal abstractions, which will then allow inexact matching and inferencing and the creation of new ideas, thus overcoming current AI limitations. This approach does not depend on any understanding of stored data or computations based on subject matter. In the course of this investigation, a new and innovative data type was discovered. Using the new data type, mathematical models of necessary components of such an indeterminate cognitive automaton were developed to illustrates plausibility. Using these models, true parallel processing is within reach.