In a previous paper we showed and discussed how computational information conservation theory (CICT) can help us to develop even competitive advanced quantum cognitive computational systems. 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, we propose an exponential, prespatial arithmetic scheme ("all-powerful scheme") by 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 frameworks for deep learning, towards deep thinking applications. 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.
|Titolo:||Deep Learning and Deep Thinking: New Application Framework by CICT|
|Autori interni:||FIORINI, RODOLFO|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|