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.

翻译：无线网络的演进正朝着互联智能的方向发展，这一概念设想在超连接的网络物理世界中，实现人、物与智能之间的无缝互联。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人工智能服务，成为实现互联智能的一种有前景的解决方案。本文介绍了一种自主边缘人工智能系统，该系统能够自动组织、适应和优化自身，以满足用户的多样化需求。该系统采用云-边-端分层架构，其中大语言模型（即生成式预训练Transformer，GPT）部署在云端，而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，我们提出了一个多功能框架，该框架高效协调边缘人工智能模型以满足用户的个性化需求，同时自动生成代码，通过边缘联邦学习训练新模型。实验结果展示了该系统在准确理解用户需求、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方面的卓越能力。