Exploring techniques and overcoming hurdles in generative AI

Research Article
Open access

Exploring techniques and overcoming hurdles in generative AI

Jinjie Bai 1*
  • 1 University of Bristol    
  • *corresponding author py19136@bristol.ac.uk
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/36/20230455
ACE Vol.36
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-297-8
ISBN (Online): 978-1-83558-298-5

Abstract

The realm of artificial intelligence has witnessed significant advancements, with generative models standing at the forefront of this progress. Generative Artificial Intelligence concerns the development of algorithms and models equipped to generate novel content - be it images, text, or music. This paper delves into the primary techniques underpinning generative AI, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models like Transformers. These methodologies have enabled a myriad of applications, from synthesizing images to facilitating data augmentation and style transfer. While the results from generative models have been profoundly impressive, they are not devoid of challenges. The surge in their capabilities has brought forth issues related to ethics, inherent biases, scalability, and the quest for more stable training methods. This paper aims to provide an insightful exploration of the pivotal methods defining generative AI while shedding light on the prevailing challenges and ethical implications intertwined with its growth.

Keywords:

Generative Artificial Intelligence, GANs, VAEs, Transformers

Bai,J. (2024). Exploring techniques and overcoming hurdles in generative AI. Applied and Computational Engineering,36,248-253.
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References

[1]. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE signal processing magazine, 35(1), 53-65..

[2]. Yi-Lun, L., Dai Xing-Yuan, L. L., Xiao, W., & Fei-Yue, W. (2018). The new frontier of AI research: generative adversarial networks. Acta Automatica Sinica, 44(5), 775-792.

[3]. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484.

[4]. Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023). ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv preprint arXiv:2301.04655.

[5]. Solaiman, I. (2023, June). The gradient of generative AI release: Methods and considerations. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 111-122).

[6]. Aydın, Ö., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations?. What is beyond expectations.

[7]. Zohny, H., McMillan, J., & King, M. (2023). Ethics of generative AI. Journal of medical ethics, 49(2), 79-80.

[8]. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.

[9]. Weisz, J. D., Muller, M., He, J., & Houde, S. (2023). Toward general design principles for generative AI applications. arXiv preprint arXiv:2301.05578.

[10]. Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93.

Cite this article

Bai,J. (2024). Exploring techniques and overcoming hurdles in generative AI. Applied and Computational Engineering,36,248-253.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-297-8(Print) / 978-1-83558-298-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.36
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE signal processing magazine, 35(1), 53-65..

[2]. Yi-Lun, L., Dai Xing-Yuan, L. L., Xiao, W., & Fei-Yue, W. (2018). The new frontier of AI research: generative adversarial networks. Acta Automatica Sinica, 44(5), 775-792.

[3]. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484.

[4]. Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023). ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv preprint arXiv:2301.04655.

[5]. Solaiman, I. (2023, June). The gradient of generative AI release: Methods and considerations. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 111-122).

[6]. Aydın, Ö., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations?. What is beyond expectations.

[7]. Zohny, H., McMillan, J., & King, M. (2023). Ethics of generative AI. Journal of medical ethics, 49(2), 79-80.

[8]. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.

[9]. Weisz, J. D., Muller, M., He, J., & Houde, S. (2023). Toward general design principles for generative AI applications. arXiv preprint arXiv:2301.05578.

[10]. Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93.

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