ADVANCED ARTIFICIAL INTELLIGENCE AND GENERATIVE MODELS: INNOVATIONS, APPLICATIONS, AND FUTURE RESEARCH DIRECTIONS

Abstract

This paper gives a general discussion of recent advances in artificial intelligence generative models and their applications, future perspectives and limitations. The history pf AI and the development of the paradigms of automatic learning and deep learning through the development of powerful basic models such as large Language models, diffusion models and multi model’s systems. These technologies have demonstrated high potential in other fields for example health, education, engineering, software, industries, and scientific research. The drawbacks of these models are such as hallucinations, threats to privacy, biases interpret ability and high computational costs that make them difficult to use widely. In this article, it is critically examining these limitations and more generally the ethical, social issues, legal and emphasises the imperative of responsible practices in AI for good governance and human regulation. Besides this study proposes a conceptual framework that involves AI innovation model architecture, risks, application domains and future research. It also identifies the most important trends in the part such as development of trustworthy and interpretable AI energy saving systems domains specific models and assessment procedures. Comprehensively this article offers an overview of the field of generative AI and presents valuable information on how to create reliable sustainable and human centered AI systems.

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