The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world. Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence by delivering high-quality, low-latency, and privacy-preserving AI services at the network edge. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements, leveraging the power of large language models (LLMs), i.e., Generative Pretrained Transformer (GPT). By exploiting the powerful abilities of GPT in language understanding, planning, and code generation, as well as incorporating classic wisdom such as task-oriented communication and edge federated learning, we present a versatile framework that efficiently coordinates edge AI models to cater to users' personal demands while automatically generating code to train new models in a privacy-preserving manner. Experimental results demonstrate the system's remarkable ability to accurately comprehend user demands, efficiently execute AI models with minimal cost, and effectively create high-performance AI models at edge servers.

翻译：无线网络的演进正趋向于互联智能，这一概念设想在超连接的数字物理世界中，实现人、物与智能的无缝互联。边缘人工智能通过在网络边缘提供高质量、低延迟且保护隐私的AI服务，是实现互联智能的一种有前景的解决方案。本文提出了一种自主边缘AI系统的愿景，该系统利用大型语言模型（如生成式预训练Transformer）的强大能力，能够自动组织、适应并优化自身，以满足用户的多样化需求。通过发挥GPT在语言理解、规划和代码生成方面的强大能力，并结合任务导向通信和边缘联邦学习等经典智慧，我们提出了一种通用框架，该框架能够高效协调边缘AI模型以满足用户的个性化需求，同时以保护隐私的方式自动生成代码来训练新模型。实验结果表明，该系统具有准确理解用户需求、以最低成本高效执行AI模型以及在边缘服务器上有效创建高性能AI模型的卓越能力。