Intelligence Complexity details a theory of intelligence complexity based on discrete levels of intelligence: Data, Information, Knowledge and Wisdom (DIKW). The report provides detailed descriptions of each of these defined levels of intelligence and puts forward a framework that can be used to measure the intelligence complexity of any intelligent system. Intelligence Complexity’s DIKW framework provides an alternative to the Turing Test as a measure of a system’s ability to reach defined levels of intelligence.

Intelligence Complexity also introduces a new concept (I = E x C) developed by author Michael Swetnam to explain what drives intelligent systems to learn. This theory posits that intelligence is inextricably linked to emotion, which is a key force that drives the development of human intelligence forward. The authors present a thermodynamic argument of emotion that attempts to explain the human intelligence system in terms of complexity, efficiency and entropy.