General Intelligent Network (GIN) and Generalized Machine Learning Operating System (GML) for Brain-Like Intelligence

EasyChair Preprint no. 11690

Abstract

This paper introduces a preliminary concept aimed at achieving Artificial
General Intelligence (AGI) by leveraging a novel approach rooted
in two key aspects. Firstly, we present the General Intelligent Network
(GIN) paradigm, which integrates information entropy principles with
a generative network, reminiscent of Generative Adversarial Networks
(GANs). Within the GIN network, original multimodal information is
encoded as low information entropy hidden state representations (HPPs).
These HPPs serve as efficient carriers of contextual information, enabling
reverse parsing by contextually relevant generative networks to reconstruct
observable information.
Secondly, we propose a Generalized Machine Learning Operating System
(GML System) to facilitate the seamless integration of the GIN
paradigm into the AGI framework. The GML system comprises three
fundamental components: an Observable Processor (AOP) responsible
for real-time processing of observable information, an HPP Storage System
for the efficient retention of low entropy hidden state representations,
and a Multimodal Implicit Sensing/Execution Network designed to handle
diverse sensory inputs and execute corresponding actions.

Keyphrases: AI, GML, GNL

@Booklet{EasyChair:11690,
  author = {Budee U Zaman},
  title = {General Intelligent Network (GIN) and Generalized Machine Learning Operating System (GML) for Brain-Like Intelligence},
  howpublished = {EasyChair Preprint no. 11690},

  year = {EasyChair, 2024}}