General purpose representation and association machine part 2: Biological implications

Abstract

Using lessons learned from error control coding, and multiple areas of life science, we propose a general purpose representation and association machine (GPRAM). In this part of paper, we illustrate our methodology, four principles, and our understanding of intelligence. We then introduce hierarchical structure and reasons to be vagueness, overcompleteness, and deliberate variation. After that, we show possible features and how to explain some visual illusions. Lastly, we illustrate a possible vague computational architecture to perform quick and rough estimation for general purpose.