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New CICT Framework for Deep Learning and Deep Thinking Application

New CICT Framework for Deep Learning and Deep Thinking Application

Rodolfo A. Fiorini (Politecnico Di Milano University, Italy)
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-0414-7.ch020
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Abstract

To achieve reliable system intelligence outstanding results, current computational system modeling and simulation community has to face and to solve two orders of modeling limitations at least. As a solution, the author proposes an exponential, pre-spatial arithmetic scheme (“all-powerful scheme”) by computational information conservation theory (CICT) to overcome the Information Double-Bind (IDB) problem and to thrive on both deterministic noise (DN) and random noise (RN) to develop powerful cognitive computational framework for deep learning, towards deep thinking applications. In a previous paper the author showed and discussed how this new CICT framework can help us to develop even competitive advanced quantum cognitive computational systems. An operative example is presented. This paper is a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to develop deeper learning and deep thinking system and application at your fingertips and beyond.
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Current Systems Overview

Traditional machine learning (ML) approach is based on a two-part system usually: feature extraction and selection module plus trainable classifier module. Since the early 1930s, when Nicolas Rashevsky developed the first model of neural network (McCulloch & Pitts, 1943), many of us have been practicing with artificial neural networks (ANNs) derived or not derived from biological neural networks (BNNs) for decades. Some others have started after convolutional neural networks (CNNs) and deep learning (DL) showed their amazing impact on applications. Some others are following the Big data and data analytics mood.

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