Development And Challenges of Generative Artificial Intelligence in Education and Art
DOI:
https://doi.org/10.54097/vaeav407Abstract
Thanks to the rapid development of generative deep learning models, Artificial Intelligence Generated Content (AIGC) has attracted more and more research attention in recent years, which aims to learn models from massive data to generate relevant content based on input conditions. Different from traditional single-modal generation tasks that focus on content generation for a particular modality, such as image generation, text generation, or semantic generation, AIGC trains a single model that can simultaneously understand language, images, videos, audio, and more. AIGC marks the transition from traditional decision-based artificial intelligence to generative artificial intelligence, which has been widely applied in various fields. Focusing on the key technologies and representative applications of AIGC, this paper identifies several key technical challenges and controversies in the field. These include defects in cross-modal and multimodal generation, issues related to model stability and data consistency, privacy concerns, and questions about whether advanced generative models like ChatGPT can be considered general artificial intelligence (AGI). While this dissertation provides valuable insights into the revolution and challenge of generative AI in art and education, it acknowledges the sensitivity of generated content and the ethical dilemmas it may pose, and ownership rights for AI-generated works and the need for new intellectual property norms are subjects of ongoing discussion. To address the current technical bottlenecks in cross-modal and multimodal generation, future research aims to quantitatively analyze and compare existing models, proposing practical optimization strategies. With the rapid advancement of generative AI, we anticipate a transition from user-generated content (UGC) to artificial intelligence-generated content (AIGC) and, ultimately, a new era of human-computer co-creation with strong interactive potential in the near future.
Downloads
References
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt, 2023.
Jo, A. The promise and peril of generative AI. Nature, 2023, 614(1): 214-216.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536.
Hochreiter, S., & Schmidhuber, J. Long short-term memory. Neural computation, 1997, 9(8): 1735-1780.
Kingma, D. P., & Welling, M. Auto-encoding variational bayes, 2013.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. Generative adversarial networks. Communications of the ACM, 2020, 63(11): 139-144.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. Attention is all you need. Advances in neural information processing systems, 2017.
Sutton, R. S., & Barto, A. G. Reinforcement learning: An introduction. MIT press, 2018.
Bahdanau, D., Cho, K., & Bengio, Y. Neural machine translation by jointly learning to align and translate, 2014.
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, 2023.
Cetinic, E., & She, J. Understanding and Creating Art with AI: Review and Outlook. ACM Transactions on Multimedia Computing Communications and Applications, 2022, 18(2): 1–22.
GAO Lin Qi. A Model of Generative Artificial Intelligence in Personalized Learning. Journal of Tianjin Normal University (Basic Education Edition), 2023, (04): 36-40.
YANG Xiao Zhe, WANG Qing Qing, WANG Ruo Xin. The Limited Capabilities of Generative Artificial Intelligence and Educational Transformation. Global Education Perspectives. 2023, (06): 3-12.
Epstein, Z., Hertzmann, A., Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., Mahari, R., Pentland, A. S., Russakovsky, O., Schroeder, H., & Smith, A. Art and the science of generative AI. Science (American Association for the Advancement of Science), 2023, 380(6650): 1110–1111.
Goertzel, B. Artificial general intelligence: concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 2014, 5(1): 1.
Sun Q. A Study of Legal Issues in Regulating Providers of Generative Artificial Intelligence Products. Politics and Law, 2023, (07): 162-176.
WANG Kun Feng, Gou Chao, Duan Yan Jie, Lin Yi Lun, Zheng Xin Hu, WANG Fei Yue. Research Progress and Prospects of Generative Adversarial Network GAN. Journal of Automation, 2017, (03),321-332.
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., & Winther, O. Ladder variational autoencoders. Advances in neural information processing systems, 2016.
Mirza, M., & Osindero, S. Conditional generative adversarial nets, 2014.
Ray, P. P. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 2023.
Goertzel, B. Artificial general intelligence: concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 2014, 5(1): 1.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. On the dangers of stochastic parrots: Can language models be too big. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, 2021, 610-623.
Mordvintsev, A., Olah, C., & Tyka, M. Inceptionism: Going deeper into neural networks, 2015.
Daskalakis, C., Ilyas, A., Syrgkanis, V., & Zeng, H. Training gans with optimism, 2017.
Gatys, L. A., Ecker, A. S., & Bethge, M. A neural algorithm of artistic style, 2015.
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, 2017, 2223-2232.
Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., ... & Olah, C. Multimodal neurons in artificial neural networks. Distill, 2021, 6(3): e30.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding, 2018.
Gao, J., Shen, T., Wang, Z., Chen, W., Yin, K., Li, D., ... & Fidler, S. Get3d: A generative model of high quality 3d textured shapes learned from images. Advances In Neural Information Processing Systems, 2022, (35): 31841-31854.
Poole, B., Jain, A., Barron, J. T., & Mildenhall, B. Dreamfusion: Text-to-3d using 2d diffusion, 2022.
Peng, C. H. E. N., Qing, L. I., De-zheng, Z. H. A. N. G., Yu-hang, Y. A. N. G., Zheng, C. A. I., & Zi-yi, L. U. A survey of multimodal machine learning. Journal of Engineering Science, 2020, 42(5): 557-569.
Baltrušaitis, T., Ahuja, C., & Morency, L. P. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(2): 423-443.
S. K. D’mello and J. Kory, “A review and meta-analysis of multimodal affect detection systems,” ACM Comput. Surv, 2015, vol. 47, Art. no. 43.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. Improving language understanding by generative pre-training, 2018.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
