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 AI emerges as a
promising solution to achieve connected intelligence by delivering
high-quality, low-latency, and privacy-preserving AI services at the network
edge. In this article, we introduce an autonomous edge AI system that
automatically organizes, adapts, and optimizes itself to meet users' diverse
requirements. The system employs a cloud-edge-client hierarchical architecture,
where the large language model, i.e., Generative Pretrained Transformer (GPT),
resides in the cloud, and other AI models are co-deployed on devices and edge
servers. By leveraging the powerful abilities of GPT in language understanding,
planning, and code generation, 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 via edge federated
learning. 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 through federated
learning.

无线网络的演变趋势是朝着连接智能化发展，边缘人工智能作为一种有前景的解决方案，通过在网络边缘提供高质量、低延迟和保护隐私的人工智能服务来实现连接智能化。本文介绍了一种自主边缘人工智能系统，它自动组织、自适应并优化自身以满足用户的多样化需求。该系统采用云 - 边缘 - 客户端分层架构，其中大型语言模型（即 GPT）位于云端，其他人工智能模型则共同部署在设备和边缘服务器上。通过利用 GPT 在语言理解、规划和代码生成方面的强大能力，我们提出了一种多功能框架，它能够高效协调边缘人工智能模型以满足用户的个性化需求，同时通过边缘联合学习自动生成训练新模型的代码。实验结果表明，该系统具有精确理解用户需求、以最小成本高效执行人工智能模型以及通过联邦学习有效创建高性能人工智能模型的非凡能力。