Introduction to Machine Intelligence Measurement

How to measure machine intelligence seem to be in the fields of computer science, however, the field is affected by interdisciplinary assumptions from other disciplines such as social sciences and biological (neuroscience) sciences. Machine intelligence measurement would help technology evaluators and policy makers assay the relevance of machines that claim intelligence. The technical (T) evaluators would focus on features that are of no qualitative meaning to humans. Policy makers would focus on whether a machine violated any regulation (O) during the course of the intelligent actions. End- users would focus on subtle features parallel to human intelligence (P). Coupling T with O and P with O measures (using a new calculus) reveals the overall intelligence. This book concerns consumers of nduologic systems (intelligent artificial systems and educational technologies). It shows the diversity of measures of machine intelligence and how to use the TOP model. It clarified the significances of machine intelligence quotient (the focus of this book) is quite different from those of machine intelligence.

V. C. I Ulinwa, Ph.D, Ph.D* Studied Computational Intelligence. He is a Systems Science researcher with interest on Nduologic systems (Machine Intelligence and its Measurement, Educational Technology, Mechanism design, and Epidemic Systems).He also has degrees in Public Administration and information management & telecommunication certificate.