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Deep Learning Framework for Artificial General Intelligence

EasyChair Preprint no. 7921, version 2

Versions: 12history
12 pagesDate: May 12, 2022

Abstract

This paper proposes two Deep Learning (DL) related models, to serve potentially as parts of an AGI agent. The first one is designed bottom-up, i.e. it is mostly based on DL. The second one is a partial AGI model, specifically concerning the thinking process. It is designed top-down, i.e. it is mainly based on cognition and communication. The latter has not yet been fully designed for implementation. It only describes the representation of the data, and its relevance to DL is by being triggered by some Deep Neural Network (DNN).

Keyphrases: associative thinking, deep learning, Neuro Science

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:7921,
  author = {Shimon Komarovsky},
  title = {Deep Learning Framework for Artificial General Intelligence},
  howpublished = {EasyChair Preprint no. 7921},

  year = {EasyChair, 2022}}
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