Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.

人工智能 (AI) 领域的最新发展催生了机器处理的新范式，从数据驱动的、有判别性的 AI 任务转向通过生成式 AI 执行复杂的、创造性的任务。利用深度生成模型，生成式人工智能能够根据基本的用户提示为各个领域（例如文本、图像或编程代码）生成新颖且真实的内容。在本文中，我们全面概述了生成人工智能的基础知识及其基础概念和前景。我们对相关术语和技术进行了概念性介绍，概述了构成生成式人工智能的固有属性，并详细阐述了其潜力和挑战。我们强调研究人员和从业者有必要理解生成人工智能的独特特征，以便利用其潜力，同时降低其风险，并促进主要理解。