Emergence Of Artificial Cognition, The: An Introduction To Collective Learning

Based on materials discussed in the various quantum probability conferences, this text aims to provide an update on the rapidly growing field of classical probability, quantum physics and functional analysis. In this book, a pioneer in the research on collective learning systems (an adaptive learning paradigm for artificial intelligence) describes the processes and mechanisms of human and artificial cognition, defines a fundamental building block for assembling large-scale adaptive systems (the learning cell), and proposes a design for the ultimate: a hierarchical network of 100 million learning cells that exhibits the full range of cognitive capabilities of the human cerebral cortex. The author demonstrates that using the classical expert system approach to create such a vast knowledge base would require thousands of years to program all the necessary rules. He then explains how an adaptive collective learning system could achieve this goal in a matter of 20 years, much as humans do. Based on natural anatomical and behavioral precedents, collective learning enables a machine to learn the appropriate rules through trial-and-error interaction with the real world. In the course of explaining the principles of collective learning and his design for the ultimate machine, the author introduces a new theory of games for modelling the processes of the universe and discusses the philosophical issues raised by the prospect of creating machines that exhibit human-like intelligence. In addition to a number of small-scale illustrations of Collective Learning, the final chapter presents the remarkable results of a research project directed by the author: a simulatin of the sub-symbolic image-processing functions of the primary visual cortex of the brain.