Generative Artificial Intelligence: A Historical and Future Perspective

Abstract

The artificial intelligence field has seen a surge in development, particularly after the advancement of Generative Adversarial Network (GAN) models, resulting in a diverse range of applications. The varied usage of generative models significantly enhances the importance of this domain. The primary focus of this article is the history of generative models, aiming to provide insights into how the field has evolved and to comprehend the complexities of contemporary models. The diversity in application areas and the advantages introduced by these technologies are explored in detail to facilitate a thorough understanding, with the expectation that this knowledge will expedite the emergence of new models and products. The advantages and innovative applications across sectors underscore the critical role these models play in industry. Distinguishing between traditional artificial intelligence and generative artificial intelligence, the article examines the differences. The architecture of generative models, grounded in deep learning and artificial neural networks, is compared briefly with other generative models. Lastly, the article delves into the future of artificial intelligence, addressing associated risks and proposing solutions. It concludes by emphasizing the significance of the article for new research endeavors, serving as a guiding resource for researchers navigating critical discussions in the field of generative models and artificial intelligence.