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Quantifying Consciousness in Artificial Intelligence: An Integrated Approach Using Quantum Mechanics, Information Theory, and Neuroscience

Resource type
Preprint
Author/contributor
Title
Quantifying Consciousness in Artificial Intelligence: An Integrated Approach Using Quantum Mechanics, Information Theory, and Neuroscience
Abstract
The quest to create artificial consciousness has long been a central challenge in the field of artificial intelligence (AI). While significant progress has been made in developing AI systems that can perform complex tasks and exhibit intelligent behavior, the question of whether these systems can truly be considered conscious remains open. In this paper, I present a novel approach to quantifying consciousness in AI systems by integrating principles from quantum mechanics, information theory, and neuroscience. The model incorporates key components such as self-awareness, subjective experience, intentionality, metacognition, integrated information processing, and dynamic cognition, which are thought to be essential for the emergence of conscious experience. I demonstrate the application of the model using a simulated AI system and discuss the implications of the findings for the development of artificially conscious agents. Furthermore, I argue that the pursuit of artificial consciousness is not only a scientific and technological endeavor but also a philosophical and ethical one, with profound implications for the understanding of the nature of mind and the relationship between humans and machines
Date
2024-03-25
Accessed
3/7/25, 7:49 AM
Short Title
Quantifying Consciousness in Artificial Intelligence
Library Catalog
Open Science Framework
Citation
Wilson, J. J. (2024). Quantifying Consciousness in Artificial Intelligence: An Integrated Approach Using Quantum Mechanics, Information Theory, and Neuroscience. https://doi.org/10.31219/osf.io/x26n8