A Survey of Generative Artificial Intelligence Techniques

Abstract

Generative artificial intelligence (AI) refers to algorithms capable of creating novel, realistic digital content autonomously. Recently, generative models have attained groundbreaking results in domains like image and audio synthesis, spurring vast interest in the field. This paper surveys the landscape of modern techniques powering the rise of creative AI systems. We structurally examine predominant algorithmic approaches including generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. Architectural innovations and illustrations of generated outputs are highlighted for major models under each category. We give special attention to generative techniques for constructing realistic images, tracing rapid progress from early GAN samples to modern diffusion models like Stable Diffusion. The paper further reviews generative modeling to create convincing audio, video, and 3D renderings, which introduce critical challenges around fake media detection and data bias. Additionally, we discuss common datasets that have enabled advances in generative modeling. Finally, open questions around evaluation, technique blending, controlling model behaviors, commercial deployment, and ethical considerations are outlined as active areas for future work. This survey presents both long-standing and emerging techniques molding the state and trajectory of generative AI. The key goals are to overview major algorithm families, highlight innovations through example models, synthesize capabilities for multimedia generation, and discuss open problems around data, evaluation, control, and ethics. Please let me know if you would like any clarification or modification of this proposed abstract.