Abstract
Generative Artificial Intelligence has transformed the technological landscape by enabling machines to produce text, images, sound, and even video that closely emulate human creativity. The journey of generative AI began with simple statistical models and evolved into transformer-based architectures capable of generating coherent and contextually meaningful content. Models such as Generative Pretrained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and diffusion-based visual systems like DALL·E and Stable Diffusion have redefined the boundaries of computational creativity. These advancements have not only enhanced automation but also revolutionized sectors including education, healthcare, entertainment, and scientific research. The convergence of multimodal systems that integrate vision, language, and reasoning has given rise to a new era of artificial generalization, wherein machines can synthesize and comprehend multiple forms of data simultaneously. This paper explores the historical evolution, architectural milestones, and interdisciplinary applications of generative AI, while addressing the ethical and societal implications of its rapid proliferation. By analyzing the trajectory from GPT to multimodal systems, this study underscores how generative AI represents both a technological triumph and a profound shift in human-machine interaction paradigms.
Keywords
Generative Artificial Intelligence
GPT
Transformers
Multimodal Systems
Deep Learning
Diffusion Models
Artificial Creativity
Natural Language Generation
Visual Synthesis
Machine Reasoning
Information Processing Systems.
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