  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.
