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Intentionality for better communication in minimally conscious AI design

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R.R. Poznanski,  L.A. Cacha,  V. Sbitnev,  N. Iannella,  S. Parida,  R. Van Schalkwyk,   A. Bandyopadhyay & E.J. Brändas  (2023). Intentionality for designers of machine understanding. Journal of Multiscale Neuroscience 3(1) 

 

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Abstract
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BRIEF REPORT

We consider the nature of consciousness to be intentionality and treat the process as multiscalar and self-referential. The capabilities exhibiting purposeful, directed actions based on the act of understanding uncertainty is the main qualifier of understanding. Developing model emulations and exploring fundamental mechanisms of how machines understand meaning is central to the development of minimally conscious AI. It has been shown by Alemdar and colleagues [New insights into holonomic brain theory: implications for active consciousness. Journal of Multiscale Neuroscience 2 (2023), 159-168] that a framework for advancing artificial systems through understanding uncertainty derived from negentropic action to create intentional systems entails quantum-thermal fluctuations through informational channels instead of recognizing (cf., introspection) sensory cues through perceptual channels. The model is a blueprint when better communication arises from dipole-like proton interactions, as evolving boundary conditions are an information motif of energy flow. A schema for machine understanding through memristors implemented in a protonic ’wetwire’ filament model is a future work in progress. It does not describe the engineering aspect, which must be conceptualized before minimally conscious AI becomes operational.

Keywords: Minimally conscious AI, intentionality, machine understanding, artificial experientiality, protonic 'wetware', memristors, hydrodynamic pairs, dipole-like protonic resonance, energy flow, fluctuations.

Conflict of Interest

The authors declare no conflict of interest

 

This article belongs to the Special IssueThis article belongs to the Special Issue

     Multiscalar brain adaptability in AI Systems

                Lead Editor:  Dr. Shantipriya Parida

                                          Senior Scientist
                                    Silo AI,  Helsinki, Finland

Copyright: © 2023 The Author(s). Published by Neural Press.

This is an open access article distributed under the terms and conditions of the CC BY 4.0 license.

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