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Comparative Analysis of Several Different Multimodal Methods for the Development of Generative Artificial Intelligence
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Author(s): Saranya M. (Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India)and Amutha B. (Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India)
Copyright: 2025
Pages: 18
Source title:
Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices
Source Author(s)/Editor(s): Loveleen Gaur (University of South Pacific, Fiji & Taylor's University, Malaysia)
DOI: 10.4018/979-8-3693-3691-5.ch005
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Abstract
Generative AI models may generate massive amounts of fresh material from their training data. Besides text, they may create graphics, music, video, and more. One explanation for their unexpected popularity is its widespread effect on numerous sectors. Text, picture, and music creation are among their numerous uses. Further uses include healthcare, education, and met aversion. However, these models' design and execution remain difficult. Problems include dependability, biased material, overfitting, and restrictions. This study seeks to examine multimodal generative AI systems' similarities and differences. These criteria involve input, output, development authority, frameworks, and tools. These examples show how multimodal generative AI models are used in many industries.
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