Challenging the Boundary between Self-Awareness and Self-Consciousness in AI from the Perspectives of Philosophy
DOI:
https://doi.org/10.57125/FP.2023.12.30.03Keywords:
consciousness, subjective experience, cognition, possibility of autonomous choice, principle of consciousness, programmeAbstract
With the progression of technology, scientists are dealing with the challenges brought about by the creation of smart AI models designed for actual use. This paper aimed to explore the complex boundary between consciousness and self-awareness in artificial intelligence (AI) systems from a philosophical perspective. Methods - The literature review conducted as part of this study was based on materials obtained from such databases as WOS, Web of Science, Scopus, and Google Scholar. The findings of the study indicated that, in the present day, philosophy is analysing how technological advancements have led to a focus on conceptual frameworks that define consciousness as the capacity of artificial intelligence to identify its functional states. Self-awareness is also being viewed as the ability to reflect and recognise oneself within a larger context. Drawing on the principles of symbolic AI and connectionism, the study considers whether AI can have a form of self-awareness similar to biological entities and how this relates to philosophical debates about consciousness. The study delved into the symbolic representation of knowledge and logical reasoning as tools for AI to achieve a primary form of self-awareness. This is contrasted with a connectionist approach that models the emergent properties of neural networks, suggesting a pathway to more complex forms of self-awareness. The article discussed the limitations and potential of these approaches, including the ethical implications of creating systems that can mimic aspects of human self-awareness and self-concept. By thoroughly examining the current AI technologies and their underlying philosophical principles, the article aims to give a detailed look at the ongoing debate on the cognitive abilities of AI and whether it can move beyond basic self-awareness to a more profound level of self-awareness. The conclusion is that although AI may exhibit behaviours indicative of self-awareness, the subjective experience of self-awareness remains a uniquely human trait, which calls into question the boundary between human and machine cognition.
References
Agarwal, A., & Edelman, S. (2020). Functionally effective conscious AI without suffering. Journal of Artificial Intelligence and Consciousness, 7(01), 39-50. https://doi.org/10.1142/S2705078520300030
Assunção, G., Patrão, B., Castelo-Branco, M., & Menezes, P. (2022). An overview of emotion in artificial intelligence. IEEE Transactions on Artificial Intelligence, 3(6), 867-886. https://doi.org/10.1109/TAI.2022.3159614
Bajohr, H. (2020). Algorithmic empathy: On two paradigms of digital generative literature and the need for a critique of AI works. doi:10.5451/UNIBAS-EP79106
Barron, L. (2023). AI and Literature. In AI and Popular Culture (pp. 47–87). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80382-327-020231003
Brinkman, D., & Grudin, J. (2023). Learning from a Generative AI Predecessor--The Many Motivations for Interacting with Conversational Agents. https://doi.org/10.48550/arXiv.2401.02978
Chun, J., & Elkins, K. (2022). What the Rise of AI Means for Narrative Studies: A Response to “Why Computers Will Never Read (or Write) Literature” by Angus Fletcher. Narrative, 30(1), 104-113. https://doi.org/10.1353/nar.2022.0005
Crane, G. (2019). Beyond Translation: Language Hacking and Philology. Harvard Data Science Review, 1(2). https://doi.org/10.1162/99608f92.282ad764
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., & Voorhees, E. M. (2020). Overview of the TREC 2019 deep learning track. https://doi.org/10.48550/arXiv.2003.07820
Da, N. Z. (2019). The computational case against computational literary studies. Critical Inquiry, 45(3), 601–639. https://doi.org/10.1086/702594
Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80. https://doi.org/10.1080/2573234X.2018.1543535
Dodda, S. B., Maruthi, S., Yellu, R. R., Thuniki, P., & Reddy, S. R. B. (2021). Ethical Deliberations in the Nexus of Artificial Intelligence and Moral Philosophy. Journal of Artificial Intelligence Research and Applications, 1(1), 31-43. https://aimlstudies.co.uk/index.php/jaira/article/view/25.
Durmishi, L., & Durmishi, A. (2022). A philosophical assessment of social networks impact on adolescents' development in conditions of unlimited access to information. Future Philosophy, 1(2), 27-41. https://doi.org/10.57125/FP.2022.06.30.03
Edelman, S. (2023). On the ethics of constructing conscious AI. In Computational Approaches to Conscious Artificial Intelligence (pp. 273-294). https://doi.org/10.1142/9789811276675_0010
Esmaeilzadeh, H., & Vaezi, R. (2021). Conscious AIhttps://doi.org/10.48550/arXiv.2105.07879
Fenves, P. (2019). “Einstein's Brain” in Three Parts. The Yearbook of Comparative Literature, 62(1), 174-188. https://doi.org/10.3138/ycl.62.005
Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds and machines, 30(3), 411-437. https://link.springer.com/article/10.1007/s11023-020-09539-2
Gervais, D. J. (2021). AI Derivatives: The Application to the Derivative Work Right to Literary and Artistic Productions of AI Machines. Seton Hall L. Rev., 52, 1111. https://heinonline.org/HOL/LandingPage?handle=hein.journals/shlr52&div=37&id=&page=
Ghoshal, B., & Tucker, A. (2020). Estimating uncertainty and interpretability in Deep Learning for Coronavirus (COVID-19) detection. In arXiv [eess.IV]. http://arxiv.org/abs/2003.10769
Gillies, D., & Gillies, M. (2022). Artificial Intelligence and Philosophy of Science from the 1990s to 2020. In Current Trends in Philosophy of Science: A Prospective for the Near Future (pp. 65-79). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-01315-7_4
Glikson, E., & Woolley, A. W. (2020 ). Human trust in artificial intelligence: A review of empirical research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
Haikonen, P. O. (2020). On artificial intelligence and consciousness. Journal of Artificial Intelligence and Consciousness, 7(01), 73-82. https://doi.org/10.1142/S2705078520500046
Harris, P., Nambiar, R., Rajasekharan, A., & Gupta, B. (2020). AI Powered Analytics App for Visualising Accident-Prone Areas. In EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing: BDCC 2018 (pp. 361-367). Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-19562-5_36
Heflin, J. J. A. (2020). AI-generated literature and the vectorised Word (Doctoral dissertation, Massachusetts Institute of Technology). https://dspace.mit.edu/handle/1721.1/127563
Jalilbayli, O. B. (2022). Philosophy of linguistic culture and new perspectives in modern azerbaijani linguistics. Future Philosophy, 1(4), 53-65. https://doi.org/10.57125/FP.2022.12.30.05
Jones, N. (2022). Experiential Literature? Comparing the Work of AI and Human Authors. APRIA Journal, 5(5), 41-57. https://doi.org/10.37198/APRIA.04.05.a5
Juliani, A., Arulkumaran, K., Sasai, S., & Kanai, R. (2022). On the link between conscious function and general intelligence in humans and machines. https://doi.org/10.48550/ARXIV.2204.05133
Kaliuta, K. (2023). Integration of AI for Routine Tasks Using Salesforce. Asian Journal of Research in Computer Science, 16(3), 119-127. https://doi.org/10.9734/ajrcos/2023/v16i3350
Kampen, K., Romanchuk, S., & Palij, D. (2022). Confessional style of the Ukrainian language. Occasional Papers on Religion in Eastern Europe, 42(2), 11. https://doi.org/10.55221/2693-2148.2331
Lee, R. S. T. (2020). AI and self-consciousness. In Artificial Intelligence in Daily Life (pp. 349–368). Springer Singapore. https://link.springer.com/chapter/10.1007/978-981-15-7695-9_13
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep learning for generic object detection: A survey. International Journal of Computer Vision, 128(2), 261–318. https://doi.org/10.1007/s11263-019-01247-4
Maidaniuk, I., Tetiana, T. S. O. I., Hoian, I., Doichyk, M., Patlaichuk, O., & Stupak, O. (2022). The problem of artificial intelligence in contemporary philosophy. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 13(4), 436-449. https://edusoft.ro/brain/index.php/brain/article/view/1348.
Maraieva, U. (2022). On the formation of a new information worldview of the future (literature review). Future Philosophy, 1(1), 18-29. https://doi.org/10.57125/FP.2022.03.30.02
Müller, T. (2020). Conscious, thinking, and intelligent machines?: Some clarifying remarks in the field of articial intelligence. In Artificial Intelligence (pp. 3–14). Brill | mentis. https://doi.org/10.30965/9783957437488_003
Ng, G. W., & Leung, W. C. (2020). Strong artificial intelligence and consciousness. Journal of Artificial Intelligence and Consciousness, 7(01), 63-72. https://doi.org/10.1142/S2705078520300042
Nikolenko, K. (2022). Artificial Intelligence and Society: Pros and Cons of the Present, Future Prospects. Futurity Philosophy, 1(2), 54–67. https://doi.org/10.57125/FP.2022.06.30.05
Pak, A., Hurbanska, S., Boiko, O., Avramenko, V., & Katerynych, P. (2023). The formalized semantics of neologisms-slangisms in the context of the English translation of A military narrative. World Journal of English Language, 13(6), 537. https://doi.org/10.5430/wjel.v13n6p537
Parmar, D. (2023). Enhancing customer relationship management with Salesforce Einstein GPT [Bachelor’s thesis, Haaga-Helia University of Applied Sciences]. Theseus. https://www.theseus.fi/bitstream/handle/10024/812434/Parmar_Dipal.pdf?sequence=2&isAllowed=y
Pelau, C., Dabija, D.-C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122(106855), 106855. https://doi.org/10.1016/j.chb.2021.106855
Poznanski, R. R., Cacha, L. A., Sbitnev, V. I., Iannella, N., Parida, S., Brandas, E. J., & Achimowicz, J. Z. (2023). Intentionality for better communication in minimally conscious AI design. Journal of Multiscale Neuroscience, 3(1), 1-12. https://doi.org/10.56280/1600750890
Rafanelli, L. M. (2022). Justice, injustice, and artificial intelligence: Lessons from political theory and philosophy. Big Data & Society, 9(1), 205395172210806. https://doi.org/10.1177/20539517221080676
Reggia, J. A., Katz, G. E., & Davis, G. P. (2020). Artificial conscious intelligence. Journal of Artificial Intelligence and Consciousness, 7(01), 95-107. https://doi.org/10.1142/S270507852050006X
Sengupta, E., Garg, D., Choudhury, T., & Aggarwal, A. (2018, November). Techniques to eliminate human bias in machine learning. In 2018 International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 226-230). IEEE. https://doi.org/10.1109/SYSMART.2018.8746946
Shrivastava, M. (2017). Learning Salesforce Einstein. Packt Publishing Ltd. https://www.packtpub.com/product/learning-salesforce-einstein/9781787126893.
Skansi, S., & Kardum, M. (2021). A Prolegomenon on the Philosophical Foundations of Deep Learning as Theory of (Artificial) Intelligence. Disputatio philosophica: international journal on philosophy and religion, 23(1), 89-99. https://hrcak.srce.hr/ojs/index.php/disputatio-philosophica/article/view/22252
Suter, R. (2019). Artificial intelligence and the cloud. In Artificial Intelligence and Machine Learning for Business for Non-Engineers (p. 27). Retrieved from https://books.google.com.ua/books?hl=uk&lr=&id=-BTADwAAQBAJ&oi=fnd&pg=PA27&dq=Salesforce+Einstein+AI++linguistics&ots=9NESpYqW2a&sig=R_OccSawBiJmvpPgA_ycXh6HMgs&redir_esc=y#v=onepage&q&f=false
Van Heerden, I., & Bas, A. (2021). Viewpoint: AI as author – bridging the gap between machine learning and literary theory. The Journal of Artificial Intelligence Research, 71, 175–189. https://doi.org/10.1613/jair.1.12593
Wang, X., Zhao, Y., & Pourpanah, F. (2020). Recent advances in deep learning. International Journal of Machine Learning and Cybernetics, 11, 747-750. https://link.springer.com/article/10.1007/s13042-020-01096-5
Wei, C. (2022). Copyright protection and data reliability of AI-written literary creations in smart city. Security and Communication Networks, 2022, 1–13. https://doi.org/10.1155/2022/6498468
Yu, J. (2019). Getting started with Salesforce Einstein analytics: A Beginner's guide to building interactive dashboards. Apress. https://link.springer.com/10.1007/978-1-4842-5200-0
Zhao, G., Li, Y., & Xu, Q. (2022). From Emotion AI to cognitive AI. International Journal of Network Dynamics and Intelligence, 65–72. https://doi.org/10.53941/ijndi0101006
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