General purpose representation and association machine part 1: Introduction and illustrations

Using lessons learned from error control coding, and multiple areas of life science, we propose a general purpose representation and association machine (GPRAM). GPRAM uses a versatile approach with hierarchical representation and association structures, each with different degrees of vagueness, over-completeness, and deliberate variation. GPRAM machines use vague measurements to do a quick and rough assessment on a task; then use approximated message-passing algorithms to improve assessment; and finally selects ways closer to a solution, eventually solving it. We illustrate concepts and structures using simple examples.