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Abstract

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 cr eate 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.