Exploring the Implication of ChatGPT AI for Business: Efficiency and Challenges
DOI:
https://doi.org/10.31098/quant.1385Keywords:
ChatGPT AI, Business operations, Benefits & challenges, Applications of AI, Efficiency, ImpactAbstract
This empirical study aims to examine the impact of ChatGPT AI on various business sectors, evaluating its benefits and challenges. Specifically, the study analyzes how ChatGPT AI is transforming business operations and enhancing customer experience in customer service, e-commerce, healthcare, finance, marketing, and developer business sectors. Using a comprehensive literature review approach, this study draws on relevant academic articles to identify and analyze the key applications of AI in the business sector, the benefits realized, and the challenges faced in adopting this technology. The study explores the implications of ChatGPT AI for businesses, including its potential to improve efficiency, reduce costs, and enhance competitiveness. Additionally, ethical and legal considerations surrounding the use of ChatGPT AI in business operations are evaluated, including issues related to data privacy, bias, and transparency. The study provides insights into future directions for the application of ChatGPT AI in business and the potential impact of this technology on various business sectors. The objective is to provide a comprehensive review of the impact of ChatGPT AI on businesses, its potential benefits, and challenges for businesses looking to adopt this technology. Overall, this study highlights the importance of considering both the benefits and limitations of AI adoption in businesses, as well as the ethical and legal implications of its use.
References
Adams, S., Arel, I., Bach, J., et al. (2012). Mapping the landscape of human-level artificial general intelligence. AI Magazine, 33(1), 25–42.
Bai, J., Wu, Y., Zhang, J., & Chen, F. (2015). Subset based deep learning for RGB-D object recognition. Neurocomputing, 165, 280-292.
Bostrom, N. (2006). How long before superintelligence? Linguistic and Philosophical Investigations, 5(1), 11–30.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press.
Dreyfus, H. L. (2012). A history of first step fallacies. Minds and Machines, 22(2), 87–99. Special issue “Philosophy of AI” ed. Vincent C. Müller.
Esteves, J. S., Carvalho, A., & Couto, C. (2003, June). Generalized geometric triangulation algorithm for mobile robot absolute self-localization. In 2003 IEEE international symposium on industrial electronics (Cat. No. 03TH8692) (Vol. 1, pp. 346-351). IEEE.
Gottfredson, L. S. (1998). The general intelligence factor.
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. London: Viking.
Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. Fundamental issues of artificial intelligence, 555-572.
Martínez, D. M., & Fernández-Rodríguez, J. C. (2015). Artificial intelligence applied to project success: a literature review. IJIMAI, 3(5), 77-84.
McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Retrieved October 2006, from http://wwwformal.stanford.edu/jmc/history/dartmouth/dartmouth.html
Niekum, S., Osentoski, S., Konidaris, G., Chitta, S., Marthi, B., & Barto, A. G. (2015). Learning grounded finite-state representations from unstructured demonstrations. The International Journal of Robotics Research, 34(2), 131-157.
Price, H. (2013, January 27). Cambridge, cabs and Copenhagen: My route to existential risk. The New York Times.http://opinionator.blogs.nytimes.com/2013/01/27/cambridge-cabs-andcopenhagen-my-route-to-existential-risk/?_php=true&_type=blogs&_r=0
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Shabbir, J., & Anwer, T. (2018). Artificial intelligence and its role in near future. arXiv preprint arXiv:1804.01396.
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