TY - JOUR
T1 - The Age of Generative AI and AI-Generated Everything
AU - Du, Hongyang
AU - Niyato, Dusit
AU - Kang, Jiawen
AU - Xiong, Zehui
AU - Zhang, Ping
AU - Cui, Shuguang
AU - Shen, Xuemin
AU - Mao, Shiwen
AU - Han, Zhu
AU - Jamalipour, Abbas
AU - Poor, H. Vincent
AU - Kim, Dong In
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - AI-generated everything (AIGC)
KW - Generative AI (GAI)
KW - generative diffusion model
KW - networks
UR - https://www.scopus.com/pages/publications/85197482810
U2 - 10.1109/MNET.2024.3422241
DO - 10.1109/MNET.2024.3422241
M3 - Article
AN - SCOPUS:85197482810
SN - 0890-8044
VL - 38
SP - 501
EP - 512
JO - IEEE Network
JF - IEEE Network
IS - 6
ER -