Abstract
AbstractIn recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.
Publisher
Springer Science and Business Media LLC
Reference178 articles.
1. Ahmad B, Sun J, You Q, Palade V, Mao Z (2022) Brain tumor classification using a combination of variational autoencoders and generative adversarial networks. Biomedicines 10(2):223
2. Ahuja K, Diddee H, Hada R, Ochieng M, Ramesh K, Jain P, Nambi A, Ganu T, Segal S, Axmed M, Bali K, Sitaram S (2023) Mega: Multilingual evaluation of generative ai
3. Akbik A, Blythe D, Vollgraf R (2018) Contextual string embeddings for sequence labeling. In: Proceedings of the 27th international conference on computational linguistics, pp 1638–1649
4. Al-Sabahi K, Zuping Z, Nadher M (2018) A hierarchical structured self-attentive model for extractive document summarization (hssas). IEEE Access 6:24205–24212
5. Ali H, Biswas MR, Mohsen F, Shah U, Alamgir A, Mousa O, Shah Z (2022) The role of generative adversarial networks in brain mri: a scoping review. Insights Imaging 13(1):98
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献