Generative artificial intelligence: a systematic review and applications

In 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 perplexin...

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Veröffentlicht in:Multimedia tools and applications Jg. 84; H. 21; S. 23661 - 23700
Hauptverfasser: Sengar, Sandeep Singh, Hasan, Affan Bin, Kumar, Sanjay, Carroll, Fiona
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2025
Springer Nature B.V
Schlagworte:
Architecture
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Diffusion models
Generative artificial intelligence
Language
Large language models
Machine learning
Multimedia Information Systems
Natural language processing
Special Purpose and Application-Based Systems
Systematic review
Track 6: Computer Vision for Multimedia Applications
Unsupervised learning
Variational autoencoder
Generative adversarial networks
Transformers
Segmentation
Diffusion
Generative artificial intelligence
ISSN:1573-7721, 1380-7501, 1573-7721
Online-Zugang:Volltext
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Zusammenfassung:In 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.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-20016-1