Evolving AI Communities in the Era of Postmodernity: Dilemmas, Perils, and Prospects
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Abstract
The significance of studying artificial intelligence within our contemporary society holds immense importance as the world has embraced an era of novel innovations. The objective of this investigation is to conceptualize the advancement of an artificial intelligence-centered society within the framework of risks and challenges characteristic of postmodernity, while also exploring its potential integration across all facets of human existence. The study encompasses three key objectives:
- Exploring the evolution of artificial intelligence through its four distinct waves, as it progressively becomes more intricate and exerts an impact on human life.
- Investigating the developmental trajectories of artificial intelligence (AI) within the context of the growth of smart societies and smart technologies.
- Developing the conceptualization of artificial intelligence within the dynamic landscape of technological shifts and the digital economy.
Upon analysis, it becomes evident that the progression of an artificial intelligence-based society within the realm of the digital economy is in a state of perpetual evolution, leading to enhancements and the emergence of novel challenges, issues, and risks. The article presents a comprehensive exploration of the development of an artificial intelligence-driven society through its successive "four waves," each characterized by increasing complexity and influence on human existence. The pathways of artificial intelligence development within the domain of smart societies and smart technologies are thoroughly examined.
DOI:
https://doi.org/10.61439/BLPA2923
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Schmidhuber, J. (2013). PowerPlay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00313
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An Introduction. MIT Press.
Turing, A. (1950). Computing Machinery and Intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/lix.236.433
Wagner, D. N. (2020). The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm. Telecommunications Policy, 44(6), 101954. https://doi.org/10.1016/j.telpol.2020.101954
Wakunuma, K., Jiya, T., & Aliyu, S. O. (2020). Socio-ethical implications of using AI in accelerating SDG3 in Least Developed Countries. Journal of Responsible Technology, 4, 100006. https://doi.org/10.1016/j.jrt.2020.100006
Andriukaitiene, R., Voronkova, V., Kivlyuk, O., Romanenko, T., & Rizhova, I. (2017). Conceptualization of smart society and smart technologies in the context of the development of modern civilization. In Mokslas Irpraktika: Aktualijos Ir Perspektyvos (pp. 11–12). Lietuvos sporto universitetas.
Black, J. S., & Van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001
Brooks, R. A. (2013). Robots at work: towards a smarter factory. The Futurist, 47(3), 24–27.
Britchenko, I., & Polishchuk, Y. (Eds.). (2018). Development Of Small And Medium Enterprises: The Eu And East-Partnership Countries Experience. Tarnobrzeg.
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Cherep, A., Voronkova, V., & Nikitenko, V. (2020). The reverse side of technological innovations and their consequences in the conditions of the innovation-information society. In Mokslas ir praktika: aktualijos ir perspektyvos (pp. 462–471). Lietuvos sporto universitetas.
Clifford, C. (2018). Elon Musk: ‘Mark my words — A.I. is far more dangerous than nukes.’ CNBC. https://www.cnbc.com/2018/03/13/elon-musk-at-sxsw-a-i-is-more-dangerous-than-nuclear-weapons.html
Cohen, I. G., Evgeniou, T., Gerke, S., & Minssen, T. (2020). The European artificial intelligence strategy: implications and challenges for digital health. The Lancet Digital Health, 2(7), e376–e379. https://doi.org/10.1016/s2589-7500(20)30112-6
Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. https://doi.org/10.1016/j.jbusres.2020.08.019
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013). Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1915–1929. https://doi.org/10.1109/tpami.2012.231
Feijóo, C., Kwon, Y., Bauer, J. M., Bohlin, E., Howell, B., Jain, R., Potgieter, P. H., Vu, K., Whalley, J., & Xia, J. (2020). Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy. Telecommunications Policy, 44(6), 101988. https://doi.org/10.1016/j.telpol.2020.101988
Jo, E. A., & Gebru, T. (2020). Lessons from archives: strategies for collecting sociocultural data in machine learning. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 306–316. https://doi.org/10.1145/3351095.3372829
Gruetzemacher, R., Paradice, D., & Lee, K. B. (2020). Forecasting extreme labor displacement: A survey of AI practitioners. Technological Forecasting and Social Change, 161, 120323. https://doi.org/10.1016/j.techfore.2020.120323
Huzhva, A. (2020, March 21). What is the world coming to? Security concept in the face of 21st century threats. Granit of Science. https://un-sci.com/ru/2020/03/21/k-chemu-idet-mir-konczepcziya-bezopasnosti-v-usloviyah-ugroz-xxi-veka/
Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Penguin.
LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lee, K. (2018). AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin.
Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave New World? on AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44–56. https://doi.org/10.1016/j.intmar.2020.04.002
McCarthy, J. (1986). Applications of circumscription to formalizing common-sense knowledge. Artificial Intelligence, 28(1), 89–116. https://doi.org/10.1016/0004-3702(86)90032-9
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/bf02478259
Mitchell, T., Michalski, R., & Carbonell, J. (1983). Machine learning: An Artificial Intelligence Approach. Tioga Press.
Moravec, H. P. (1999). Robot: Mere Machine to Transcendent Mind. Oxford University Press.
Nikitenko, V., Andriukaitiene, R., & Punchenko, O. (2019). Formation of sustainable digital economical concept: challenges, threats, priorities. Humanities Studies, 1(78), 140–153.
Ng, A. (2016). What artificial intelligence can and can’t do right now. Harvard Business Review, 9(11), 1–4.
Norvig, P., & Russell, S. (2011). Artificial intelligence: A Modern Approach. Pearson Higher Ed.
Notes from the AI frontier: Modeling the impact of AI on the world economy. (2018, September 4). McKinsey & Company. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy
Purdy, M., & Daugherty, P. (2016). Why Artificial Intelligence is the Future of Growth. https://dl.icdst.org/pdfs/files2/2aea5d87070f0116f8aaa9f545530e47.pdf
Rodrigues, R. (2020). Legal and human rights issues of AI: Gaps, challenges and vulnerabilities. Journal of Responsible Technology, 4, 100005. https://doi.org/10.1016/j.jrt.2020.100005
Samuel, A. L. (2000). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 44(1.2), 206–226. https://doi.org/10.1147/rd.441.0206
Sandhana, L. (2015, May 2). 47% of US jobs under threat from computerization according to Oxford study. New Atlas. https://newatlas.com/half-of-us-jobs-computerized/29142/
Schwab, K. (2017). The Fourth Industrial Revolution. Penguin UK.
Schmidhuber, J. (1992). Learning complex, extended sequences using the principle of history compression. Neural Computation, 4(2), 234–242. https://doi.org/10.1162/neco.1992.4.2.234
Schmidhuber, J. (2013). PowerPlay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00313
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An Introduction. MIT Press.
Turing, A. (1950). Computing Machinery and Intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/lix.236.433
Wagner, D. N. (2020). The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm. Telecommunications Policy, 44(6), 101954. https://doi.org/10.1016/j.telpol.2020.101954
Wakunuma, K., Jiya, T., & Aliyu, S. O. (2020). Socio-ethical implications of using AI in accelerating SDG3 in Least Developed Countries. Journal of Responsible Technology, 4, 100006. https://doi.org/10.1016/j.jrt.2020.100006