JOURNAL of MULTISCALE NEUROSCIENCE

ORIGINAL RESEARCH

The potential of conscious artificial intelligence (AI), with its functional systems that surpass automation and rely on elements of understanding, is a beacon of hope in the AI revolution. The shift from automation to conscious AI, once replaced with machine understanding, offers a future where AI can comprehend without needing to experience, thereby revolutionizing the field of AI. In this context, the proposed Dynamic Organicity Theory of consciousness (DOT) stands out as a promising and novel approach for building artificial consciousness that is more like the brain with physiological nonlocality and diachronicity of self-referential causal closure. However, deep learning algorithms utilize "black box" techniques such as “dirty hooks” to make the algorithms operational by discovering arbitrary functions from a trained set of dirty data rather than prioritizing models of consciousness that accurately represent intentionality as intentions-in-action. The limitations of the “black box” approach in deep learning algorithms present a significant challenge as quantum information biology, or intrinsic information, is associated with subjective physicalism and cannot be predicted with Turing computation. This paper suggests that deep learning algorithms effectively decode labeled datasets but not dirty data due to unlearnable noise, and encoding intrinsic information is beyond the capabilities of deep learning. New models based on DOT are necessary to decode intrinsic information by understanding meaning and reducing uncertainty. The process of “encoding” entails functional interactions as evolving informational holons, forming informational channels in functionality space of time consciousness. The “quantum of information” functionality is the motivity of (negentropic) action as change in functionality through thermodynamic constraints that reduce informational redundancy (also referred to as intentionality) in informational pathways. It denotes a measure of epistemic subjectivity towards machine understanding beyond the capabilities of deep learning.

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Keywords: Deep learning, dynamic organicity theory, quantum information biology, motivity of action, epistemic subjectivity

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Conflict of Interest

The author/s declare that they were an editorial board member of JMN, at the time of submission. This had no impact on the peer review process and the final decision.

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This 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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