Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AI-Generated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the inter-dependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks.

中文翻译：

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生成式人工智能时代和人工智能生成一切


生成式人工智能 (GAI) 已成为人工智能领域的一项重大进步，以其语言和图像生成能力而闻名。本文提出了“AI 生成的一切”(AIGX)，这一概念将 GAI 扩展到单纯的内容创建，扩展到跨不同技术领域的实时适应和控制。在网络方面，AIGX与物理层、数据链路层、网络层和应用层紧密协作，增强实时网络管理，响应各种系统和服务设置以及应用和用户需求。反过来，网络在 AIGX 生命周期中作为进一步 AIGX 能力优化的关键组成部分，即数据收集、分布式预训练和快速决策，从而建立相互增强的相互作用。此外，我们还提供了专注于功率分配的深入案例研究，以说明 AIGX 和网络系统之间的相互依赖性。通过这一探索，本文分析了 GAI 对于网络的重要作用，阐明了网络增强 AIGX 功能的方式，并强调了它们形成的良性交互循环。本文为后续旨在充分释放 GAI 和网络潜力的研究铺平了道路。