BRIEF REPORT

Consciousness is the ability to have intentionality, which is a process that operates at various temporal scales. To qualify as conscious, an artificial device must express functionality capable of solving the Intrinsicality problem, where experienceable form or syntax gives rise to understanding 'meaning' as a noncontextual dynamic prior to language. This is suggestive of replacing the Hard Problem of consciousness to build conscious artificial intelligence (AI). 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. Improving communication in conscious AI requires both software and hardware implementation. The software can be developed through the brain-machine interface of multiscale temporal processing, while hardware implementation can be done by creating energy flow using dipole-like hydrogen ion (proton) interactions in an artificial 'wetwire' protonic filament. Machine understanding can be achieved through memristors implemented in the protonic 'wetwire' filament embedded in a real-world device. This report presents a blueprint for the process, but it does not cover the algorithms or engineering aspects, which need to be conceptualized before minimally conscious AI can become operational.

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

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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

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Copyright: © 2024 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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Disclaimer: The statements, opinions, and data in the Journal of Multiscale Neuroscience are solely those of the individual authors and contributors, not those of the Neural Press™ or the editors(s).