Generative AI: Crafting Tomorrow’s Creativity
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Abstract
The rapid development of artificial intelligence (AI) has impacted creativity, providing artists, writers, designers, and innovators with powerful tools. This research explores the intersection of AI and human creativity, demonstrating AI’s ability to create unique content, perform tasks, and introduce new drama. Disruption poses significant challenges, including misinformation, transfer of labor, fraud, and bias in AI output. Addressing these issues requires strict regulation, greater transparency, and public awareness of the risks involved. Future efforts should focus on addressing ethical implications, ensuring transparency, and aligning technological changes with the needs of society. This is good for creating stability and balance, but people use these thing for fraud that is very harmfull for our society. Solutions include using intelligence-based search tools, improving cybersecurity, encouraging ethical behavior, and developing a skilled workforce. This study highlights the importance of balancing the benefits and risks of generative AI to foster meaningful creativity as well as its role in integrating into society. With strong ethical protections, generative AI offers many opportunities for innovation. The Telecom company should block all kinds of messages such as,(Phishing, lottery/prize scams, tech support scams, love scams, bill or payment scams, tax scams, investment scams) for the customers, and also send the alert message for the scam to all the users via Voice.
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References
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159. https://proceedings.mlr.press/v81/binns18a.html
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... &Bengio, Y. (2014). DOI: https://doi.org/10.48550/arXiv.1406.2661
Generative Adversarial Nets. Advances in Neural Information Processing Systems,27. https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). Can a Neural Network Be Creative? arXiv preprint arXiv:1706.07068. https://arxiv.org/pdf/1706.07068
Herremans, D., & Papadopoulos, G. (2017). A Functional Approach to Music Composition with AI. Journal of New Music Research, 46(2), 99-110. https://www.researchgate.net/publication/320073044_A_Functional_Taxonomy_of_Music_Generation_Systems
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27. https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html
Julia Angwin, Je Larson, Surya Mattu, and Lauren Kirchner. Machine bias. Pro Publica, 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Richard J Arneson. Equality and equal opportunity for welfare. Philosophical studies, 56(1): 7793, 1989. http://philosophyfaculty.ucsd.edu/faculty/rarneson/documents/writings/equality-and-equal-opportunity-for-welfare.pdf
Solon Barocas and Andrew D Selbst. Big datas disparate impact. Cal. L. Rev., 104:671, 2016. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899
Reuben Binns, Michael Veale, Max Van Kleek, and Nigel Shadbolt. Like trainer, like bot? in heritance of bias in algorithmic content mod eration. In International Conference on Social Informatics, pages 405415. Springer, 2017. https://arxiv.org/abs/1707.01477.
Simon Colton, Jakob Halskov, Dan Ventura, Ian Gouldstone, Michael Cook, and Blanca Perez-Ferrer. The painting fool sees! new projects with the automated painter. In Proceedings of the 6th International Conference on Computational Creativity, pages 189–196, 2015. https://dblp.org/rec/conf/icccrea/ColtonHVGCF15.html.
Derrall Heath and Dan Ventura. Before a computer can draw, it must first learn to see. In Proceedings of the 7th International Conference on Computational Creativity, 2016. https://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Before-A-Computer-Can-Draw-It-Must-First-Learn-To-See.pdf.
Bing Xu, NaiyanWang, TianqiChen, andMuLi. Empiricalevaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853, 2015. https://arxiv.org/abs/1505.00853
A.R.J. François, I. Schankler, and E. Chew. 2013. Mimi4x: An interactive audio-visual installation for high-level structural improvisation. International Journal of Arts and Technology 6, 2 (2013), 138–151. https://www.researchgate.net/publication/224176746_Mimi4x_An_interactive_audio-visual_installation_for_high-level_structural_improvisation
J.A. Franklin. 2001. Multi-phase learning for jazz improvisation and interaction. In Proceedings of the Eighth Biennial Symposium for Arts & Technology. http://cs.smith.edu/~jfrankli/papers/CtColl01.pdf
J. Gillick, K. Tang, and R.M. Keller. 2010. Machine learning of jazz grammars. Com puter Music Journal 34, 3 (2010), 56–66.
https://www.jstor.org/stable/40963033.
Lim, W.M.; Gunasekara, A.; Pallant, J.L.; Pallant, J.I.; Pechenkina, E. Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. Int. J. Manag. Educ. 2023, 21, 100790. https://www.researchgate.net/publication/371972703_Generative_AI_and_the_future_of_education_Ragnarok_or_reformation_A_paradoxical_perspective_from_management_educators
Dwivedi, Y.K.; Kshetri, N.; Hughes, L.; Slade, E.L.; Jeyaraj, A.; Kar, A.K.; Baabdullah, A.M.; Koohang, A.; Raghavan, V.; Ahuja, M.; et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 2023, 71, 102642. https://www.sciencedirect.com/science/article/pii/S0268401223000233
Kasneci, E.; Seßler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. ChatGPT for good? On opportunities
and challenges of large language models for education. Learn. Individ. Differ. 2023, 103, 102274. https://sciencedirect.com/science/article/abs/pii/S1041608023000195
Baidoo-Anu, D.; Owusu Ansah, L. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN 2023. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337484
Zhai, X. ChatGPT user experience: Implications for education. SSRN 2022. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4312418
Divya Saxena University Research Facility in Big Data Analytics (UBDA) https://arxiv.org/ftp/arxiv/papers/2005/2005.00065.pdf
Reddy, K. R. (2024). Achieving Fairness with Intelligent Co Agents. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 4, Issue 1, pp. 11–13). DOI: https://doi.org/10.54105/ijainn.a1080.04011223
Lalaei, R. A., & Mahmoudabadi, Dr. A. (2024). Promoting Project Outcomes: A Development Approach to Generative AI and LLM-Based Software Applications’ Deployment. In International Journal of Soft Computing and Engineering (Vol. 14, Issue 3, pp. 6–13). DOI: https://doi.org/10.35940/ijsce.d3636.14030724
Krishna, G. G. (2023). Generative AI. In International Journal of Advanced Engineering and Nano Technology (Vol. 10, Issue 8, pp. 1–3). DOI: https://doi.org/10.35940/ijaent.g0474.0810823
Gumabay, Dr. C. A. N., & Gumabay, Dr. M. V. N. (2024). Opportunities and Challenges for Information Technology and Business Educators in Implementing Generative Artificial Intelligence in Instruction. In International Journal of Management and Humanities (Vol. 11, Issue 4, pp. 1–7). DOI: https://doi.org/10.35940/ijmh.d1769.11041224