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On Abstract Intelligence: Toward a Unifying Theory of Natural, Artificial, Machinable, and Computational Intelligence

On Abstract Intelligence: Toward a Unifying Theory of Natural, Artificial, Machinable, and Computational Intelligence
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Author(s): Yingxu Wang (University of Calgary, Canada)
Copyright: 2009
Volume: 1
Issue: 1
Pages: 17
Source title: International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (National Institute of Technology, Kurukshetra, India) and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/jssci.2009010101

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Abstract

Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, the compatibility of natural and machine intelligence is revealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.

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