Learning modes and algorithms

Define learning modes: serial, parallel, associative, interrelational, logical, primary, hypertrees, and discover new learning modes.

Research opportunity

Abstract: Learning is defined by the Autosophy information theory as the accumulation of knowledge, where knowledge is represented as locations in omni dimensional hyperspace. There are seven known learning modes or Omni Dimensional Networks. Only the “serial” network is currently used commercially in: V.42bis modem compression, the gif and tif standard for still image compression, and in the LZW (Lempel Ziv Welch) compression code. The “parallel” networks have been simulated in software for still image compression and live video. The “associative” networks are implemented in a demonstration system of brain-like databases to connect related data patterns. The other learning modes are only known in theory. The purpose of this research would be to define all the known network algorithms and the methods for discovering additional learning modes. The Autosophy databases of the future may have exotic new learning modes that are not possible in the human brain. New learning modes may be discovered by defining new numbering systems, which are used for both addressing and quantities.

Applications: This advanced research would go beyond mere data compression, communication, and computing into possible and exotic learning modes, which may lead to future commercial applications.

Keywords: Autosophy, Omni Dimensional Networks, Self-assembling data structures.

Available downloadable documents:

Publication 1996 – Information theory – Webpage htm

Publication 1994 – Hyperspace data storage – Webpage htm

Publication 1993 – Video databases – Webpage htm

Demonstration 1988 – Autosopher – Webpage htm

First publication 1977 – Webpage htm