University of Kwazulu-Natal

Pdf Generative Artificial Intelligence Springer

Bonisiwe Shabane
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pdf generative artificial intelligence springer

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. [1]\fnmSandeep Singh \surSengar\orcidlink0000-0003-2171-9332

1]\orgdivCardiff School of Technologies, \orgnameCardiff Metropolitan University, \orgaddress \cityCardiff, \postcodeCF5 2YB, \countryUnited Kingdom 2]\orgdivDepartment of Computer Science and Engineering, \orgnameDelhi Technological University, \orgaddress\cityNew Delhi, \postcode110042, \countryIndia 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... 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. The recent advancement in Artificial Intelligence has been mainly the result of Generative Artificial Intelligence (often referred to as Generative AI or GenAI) being introduced. Generative AI encompasses artificial intelligence systems with the ability to create text, images, or various forms of media through the utilization of generative models.

These models acquire an understanding of the underlying patterns and structures within their training data, subsequently producing fresh data that share similar traits and characteristics. The motivation of this systematic review is to gather, evaluate, and synthesize existing research on a GenAI. This paper presents a systematic review that highlights key applications and variations of the architecture of Generative Artificial Intelligence models and their performance. We conducted this review to (a) understand the state-of-the-art generative AI techniques including summarizing key methodologies, algorithms, and findings across a range of studies (b) systematically review a large body of literature, which includes... (e) identify ethical challenges and propose solutions for responsible AI development.

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1]\orgdivCardiff School Of Technologies, \orgnameCardiff Metropolitan University, \orgaddress \cityCardiff, \postcodeCF5

1]\orgdivCardiff School of Technologies, \orgnameCardiff Metropolitan University, \orgaddress \cityCardiff, \postcodeCF5 2YB, \countryUnited Kingdom 2]\orgdivDepartment of Computer Science and Engineering, \orgnameDelhi Technological University, \orgaddress\cityNew Delhi, \postcode110042, \countryIndia In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This ...

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