The Age of Generative AI and AI-Generated Everything

计算机科学 生成语法 人工智能
作者
Hongyang Du,Dusit Niyato,Jiawen Kang,Zehui Xiong,Ping Zhang,Shuguang Cui,Xuemin Shen,Shiwen Mao,Zhu Han,Abbas Jamalipour,H. Vincent Poor,Dong In Kim
出处
期刊:IEEE Network [Institute of Electrical and Electronics Engineers]
卷期号:38 (6): 501-512 被引量:57
标识
DOI:10.1109/mnet.2024.3422241
摘要

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 interdependence 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. It is hoped that this article will pave the way for subsequent future research aimed at fully unlocking the potential of GAI and networks.
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