This is an outdated version published on 2019-12-09. Read the most recent version.
Preprint / Version 11

Energy Decay Network

##article.authors##

  • Jamie Nicholas Shelley

DOI:

https://doi.org/10.31224/osf.io/dfyzn

Keywords:

AGI, Artificial General Intelligence, Artificial Intelligence, EDeN, Energy Decay, Energy Decay Network

Abstract

This paper and accompanying Python/C++ Framework is the product of the Authors perceived problems with narrow (Discrimination based) AI. (Artificial Intelligence) The Framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value (‘energy’) to create a model whereby neural architecture and all unit processes are co-dependently developed . These expressions are born from fractal definition, stochastically tuned and managed by genetic experience; successful routes are maintained through global rules: (Stability of signal propagation/function over cross functional (external state, internal immediate state, and genetic bias towards selection of previous expressions)). These principles are aimed towards creating a diverse and robust network, hopefully reducing the need for transfer learning and computationally expensive translations as demand on compute increases.

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