  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.
