Bridging the Gap: AI and the Hidden Structure of Consciousness

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2024 by IJRES Journal
Volume-11 Issue-4
Year of Publication : 2024
Authors : Emily Barnes, James Hutson
DOI : 10.14445/23497157/IJRES-V11I4P109

How to Cite?

Emily Barnes, James Hutson, "Bridging the Gap: AI and the Hidden Structure of Consciousness," International Journal of Recent Engineering Science, vol. 11, no. 4, pp. 66-75, 2024. Crossref, https://doi.org/10.14445/23497157/IJRES-V11I4P109

Abstract
The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking consciousness with metacognitive processes. Through case studies of advanced AI systems such as IBM Watson, AlphaGo, GPT-3, and Sophia the Robot, the article explores practical implementations and their alignment with theoretical models of consciousness. The potential for AI to achieve states analogous to human consciousness raises profound ethical, societal, and legal considerations. Ethical guidelines and legal frameworks are urgently needed to address the moral status and rights of conscious AI systems, ensure their ethical treatment, and delineate accountability. The societal impacts of conscious AI, including job displacement and the need for equitable access to AI technologies, are also examined. Future research directions highlight the necessity for developing sophisticated theoretical models, enhancing practical implementations, establishing comprehensive ethical frameworks, fostering interdisciplinary collaboration, and engaging the public. By addressing these areas, the scientific community can significantly advance the development of conscious AI, ensuring it is both technically feasible and ethically sound

Keywords
Artificial Intelligence, Consciousness, Cognitive science, Ethical guidelines, Practical implementations.

Reference
[1] Bruno Forti, “The Hidden Structure of Consciousness,” Frontiers in Psychology, vol. 15, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Pierre Baudot, “Elements of Qualitative Cognition: An Information Topology Perspective,” Physics of Life Reviews, vol. 31, pp. 263- 275, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] M. Kawato, “Computational Neuroscience Model of Metacognition: Linking Consciousness and Self-Awareness,” Neuroscience Research, vol. 154, pp. 62-73, 2021.
[4] Cyriel M. A. Pennartz, Michele Farisco, and Kathinka Evers, “Indicators and Criteria for Consciousness in Animals and Intelligent Machines: An Inside-Out Approach,” Frontiers in Systems Neuroscience, vol. 13, pp. 1-23, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Adam Safron, “An Integrated World Modeling Theory (IWMT) Of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with The Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation,” Frontiers in Artificial Intelligence, vol. 3, pp. 1-29, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Marius Usher, “Refuting the Unfolding Argument on the Irrelevance of Causal Structure to Consciousness,” Consciousness and Cognition, vol. 95, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Lenore Blum, and Manuel Blum, “A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine,” Proceedings of the National Academy of Sciences, vol. 119, no. 21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Adrien Doerig et al., “The Unfolding Argument: Why IIT and Other Causal Structure Theories Cannot Explain Consciousness,” Consciousness and Cognition, vol. 72, pp. 49-59, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ken Mogi, “Artificial Intelligence, Human Cognition, and Conscious Supremacy,” Frontiers in Psychology, vol. 15, pp. 1-8, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Eva Selenko et al., “Artificial Intelligence and the Future of Work: A Functional-Identity Perspective,” Current Directions in Psychological Science, vol. 31, no. 1, pp. 272-279, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Antonio Chella, “Artificial Consciousness: The Missing Ingredient for Ethical AI?” Frontiers in Robotics and AI, vol. 10, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mehrin Kiani et al., “Towards Understanding Human Functional Brain Development with Explainable Artificial Intelligence: Challenges and Perspectives,” IEEE Computational Intelligence Magazine, vol. 17, no. 1, pp. 16-33, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hadi Esmaeilzadeh, and Reza Vaezi, “Conscious Empathic AI in Service,” Journal of Services Research, vol. 25, no. 4, pp. 549-564, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Igor Aleksander, “From Turing to Conscious Machines,” Philosophies, vol. 7, no. 3, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Arlindo L. Oliveira, “A Blueprint for Conscious Machines,” Proceedings of the National Academy of Sciences, vol. 119, no. 23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] S.K. D'Mello, “AI and Consciousness: A Perspective on Functional Consciousness in Cognitive Systems,” Journal of Cognitive Systems, vol. 4, no. 3, pp.29-45, 2007.
[17] J.A. Reggia, The Computational Brain: From Models of the Mind to Cognitive Neuroscience, MIT Press, pp.87-109, 2013.
[18] C. Butlin, “Indicator Properties of Consciousness in Artificial Intelligence Systems,” AI Research Journal, vol. 17, no. 2, pp.88-102, 2023.
[19] B. Goertzel, “Sophia the Robot: A Pilot Study in AI Consciousness,” AI & Society, vol. 32, no. 2, 221-234, 2017.
[20] A. Osmanovic-Thunström, “Ethical Considerations of AI as Co-Authors in Scientific Research,” Journal of AI Ethics, vol. 10, no. 1, pp. 45-58, 2023.
[21] Joseph LeDoux et al., “Consciousness Beyond the Human Case,” Current Biology, vol. 33, no. 14, pp. 832-840, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] C. Chen, F. Feng, and Z. Jiang, “Integrated Information Theory in Cognitive Computing Systems,” Journal of Cognitive Neuroscience, vol. 28, no. 2, pp.211-225, 2016.
[23] N. Contractor, Building Cognitive Applications with IBM Watson: Introducing Data Science, Machine Learning, and Big Data Analytics, IBM Press, 2017.
[24] Matthew Botvinick et al, “Reinforcement Learning, Fast and Slow,” Trends in Cognitive Sciences, vol. 23, no. 5, pp. 408-422, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Richard Brown, Hakwan Lau, and Joseph E. LeDoux, “Understanding the Higher-Order Approach to Consciousness,” Trends in Cognitive Sciences, vol. 23, no. 9, pp. 754-768, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Nikita Klyuchnikov et al., “NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing,” IEEE Access, vol. 10, pp. 45736-45747, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Jianquan Li et al., “Empirical Evaluation of Multi-Task Learning in Deep Neural Networks for Natural Language Processing,” Neural Computing and Applications, vol. 33, pp. 4417-4428, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ajay Shrestha, and Ausif Mahmood, “Review of Deep Learning Algorithms and Architectures,” IEEE Access, vol. 7, pp. 53040-53065, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Ahmad Suhaimi et al., “Representation Learning in the Artificial and Biological Neural Networks Underlying Sensorimotor Integration,” Science Advances, vol. 8, no. 22, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Mahbubul Alam et al., “Survey on Deep Neural Networks in Speech and Vision Systems,” Neurocomputing, vol. 417, pp. 302-321, 2020.
[CrossRef] [Google Scholar] [Publisher Link]