New CICT Framework for Deep Learning and Deep Thinking Application

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.

Finiteness of Frobenius Traces of a Sheaf on a Flat Arithmetic Scheme

For a lisse $\ell $-adic sheaf on a scheme flat and of finite type over $\mathbb{Z}$, we consider the field generated over $ \mathbb{Q}$ by Frobenius traces of the sheaf at closed points. Assuming conjectural properties of geometric Galois representations of number fields and the Generalized Riemann Hypothesis, we prove that the field is finite over $\mathbb{Q}$ when the sheaf is de Rham at $\ell $ pointwise. This is a number field analog of Deligne’s finiteness result about Frobenius traces in the function field case.

Empowering Cognition by Precisation of Numeric Words

intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. The convergence of software and intelligent sciences forms the transdisciplinary field of computational intelligence. In 2008, Lotfi Zadeh concluded that to make significant progress toward achievement of human level machine intelligence a paradigm shift is needed. New computational information conservation awareness can open the way for an effective paradigm shift to recover lost coherence information in system description and to develop even advanced quantum cognitive systems. The author shows the fundamental pre-spatial geometro-arithmetic scheme defining optimized numeric word generators and relations to minimize the traditional multiscale statistic modeling veil opacity and information entropy generation. It is the first, fundamental step to a reliable progression from computing with numbers to computing with numeric words with precisation of their meaning.

New CICT Framework for Deep Learning and Deep Thinking Application

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.