More than any field since pre-relativity physics, the science driving Artificial Intelligence is ripe for a paradigm shift. For years, the world of computing has sought to achieve intelligence by increasing the complexity of its algorithms. High-speed data processors latched to monstrous data stores have been ever more prevalent in the industry of machine learning. Yet somewhere along this path, brute force computation supplanted adaptive self-architecture, and the original objective of AI research was all but forgotten.

True intelligence, artificial or otherwise, must be intrinsically bound to memory; data processors must be inextricably bound to the data. The components will be simple. The algorithms will be simple. The dynamics, once set in motion, will be complex beyond imagining.