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zhang2019.txt
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zhang2019.txt
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Multiversal SpaceTime (MSpaceTime) Not Neural Network as Source of
Intelligence in Generalized Quantum Mechanics, Extended General
Relativity, Darwin Dynamics for Artificial Super Intelligence Synthesis
From Synthesis perspective, whether Logic Synthesis, Physical Synthesis,
Chemical Synthesis, or Biological Synthesis, Physical Geometry such as
Universal Geometry and Quantum Geometry, and Biological Geometry like
Conformal Geometry supported by Tensors and Manifolds, are the outcome
of physical laws and biological laws in modeling non-linear physical and
biological dynamics as opposed to traditional partial
differential/difference equation way. We discover that Multiversal
SpaceTime instead of Neural Network, governing physical and biological
world at macroscopic and microscopic level, is the ultimate source of
intelligence. With that we propose Multiversal Synthesis-based
Artificial Design Automation (ADA), a bio-physical inspired model based
on Multiverse in Darwin Dynamics, Generalized Quantum Mechanics, and
Extended General Relativity, for Artificial Super Intelligence (ASI)
implementation. Based on Schrodinger Equation of Quantum Mechanics, we
generalize the 4-Dimensional Hilbert Space based Discrete Quantum
SpaceTime to N-Dimensional 1 << N < M with M is limited by Planck
Length) Hilbert Space based Discrete MSpaceTime as part of MSpaceTime,
in modeling both Micro-Environment Intelligence and Micro-Agent
Intelligence of ASI; likewise based on Einstein Equations of General
Relativity, we make a T-Symmetry extension first, and then extend the
4-Dimensional Pseudo-Riemannian Manifold based Continuous Curved
SpaceTime as part of MSpaceTime to N-Dimensional 1 << N < ∞
Pseudo-Riemannian Manifold based Continuous MSpaceTime extension, in
modeling both Macro-Environment Intelligence and Macro-Agent
Intelligence of ASI. Our discovery only solves the black box puzzle of
AI, but also paves the way in achieving ASI through ADA. Of course, our
Multiverse Endeavor will never stop from there.
This abstract was downloaded from bioRxiv on 2020-01-07. It is available
at https://www.biorxiv.org/content/10.1101/858423v1 and licensed under a
CC-BY-ND-4.0 International license
(http://creativecommons.org/licenses/by-nd/4.0/).
NOTE THAT THIS ABSTRACT HAS BEEN RETRACTED. The retraction notice is
available here: https://www.biorxiv.org/content/10.1101/858423v2.