Quantum cognitive computation by CICT

Fiorini, Rodolfo A.

doi:10.1109/icci-cc.2016.7862085

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…Let's say we need a fresh "Science 2.0" approach. We need tools able to manage ontological uncertainty more effectively (Fiorini, 2015d;2016c).…”

Section: Extending Our Understandingmentioning

confidence: 99%

“…CICT new awareness of a discrete HG (hyperbolic geometry) subspace (reciprocal space, RS) of coded heterogeneous hyperbolic structures (Fiorini, 2014a), underlying the familiar Q Euclidean (direct space, DS) surface representation can open the way to "holographic information geometry" (Fiorini, 2014b;Fiorini & Laguteta, 2013), to recover system lost coherence and to overall system energy minimization and entropy maximization at the same time. This is the main reason why CICT is a natural framework for arbitrary multi-scale computational modeling in the current landscape of modern computational QFT (Fiorini, 2015d;2016c).…”

Section: Cictmentioning

confidence: 99%

“…In short, it is necessary to find a mathematical method to aggregate the external information coming from environment with system internal information in an efficient and fast way that is both immune from computational polynomial mirroring and classic noise effect by design (Fiorini & Santacroce, 2013). Furthermore, the mathematical method would possess anticipatory properties needed to the realization of system able to interact with its environment in real time (leading property) (Fiorini, 2016c;Rosen, 1985).…”

Section: Turning Theory Into Practicementioning

confidence: 99%

See 2 more Smart Citations

New CICT Framework for Deep Learning and Deep Thinking Application

Fiorini

2016

International Journal of Software Science and Computational Intelligence

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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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“…Let's say we need a fresh "Science 2.0" approach. We need tools able to manage ontological uncertainty more effectively (Fiorini, 2015d;2016c).…”

Section: Extending Our Understandingmentioning

confidence: 99%