As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With such overwhelming media coverage, it is almost impossible for us to miss the opportunity to glimpse AIGC from a certain angle. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. Impressed by the capability of the ChatGPT, many people are wondering about its limits: can GPT-5 (or other future GPT variants) help ChatGPT unify all AIGC tasks for diversified content creation? Toward answering this question, a comprehensive review of existing AIGC tasks is needed. As such, our work comes to fill this gap promptly by offering a first look at AIGC, ranging from its techniques to applications. Modern generative AI relies on various technical foundations, ranging from model architecture and self-supervised pretraining to generative modeling methods (like GAN and diffusion models). After introducing the fundamental techniques, this work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc., which depicts the full potential of ChatGPT's future. Moreover, we summarize their significant applications in some mainstream industries, such as education and creativity content. Finally, we discuss the challenges currently faced and present an outlook on how generative AI might evolve in the near future.

翻译：随着ChatGPT的爆火，生成式人工智能（AIGC，又称AI生成内容）因其分析和创建文本、图像等能力而成为全球瞩目的焦点。在媒体铺天盖地的报道下，我们几乎不可能错过从某个角度一窥AIGC的机会。在人工智能从纯粹分析转向创作的变革时代，值得关注的是，ChatGPT及其最新的语言模型GPT-4仅仅是众多AIGC任务中的一个工具。惊叹于ChatGPT的能力之余，许多人开始思考它的极限：GPT-5（或其他未来GPT变体）能否帮助ChatGPT统一所有AIGC任务，实现多样化内容创作？为回答这一问题，我们需要对现有AIGC任务进行全面回顾。因此，我们的工作及时填补了这一空白，首次系统性地审视AIGC，涵盖其技术到应用领域。现代生成式AI依赖于多种技术基础，包括模型架构、自监督预训练、生成式建模方法（如GAN和扩散模型）。在介绍基础技术后，本工作聚焦于各类AIGC任务的技术发展，依据输出类型（包括文本、图像、视频、3D内容等）展开论述，揭示了ChatGPT未来的全部潜力。此外，我们总结了这些技术在教育、创意内容等主流行业中的重大应用。最后，我们探讨了当前面临的挑战，并对生成式AI在不久的将来的演进方向进行了展望。