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Article: The Age of Generative AI and AI-Generated Everything

TitleThe Age of Generative AI and AI-Generated Everything
Authors
KeywordsAI-generated everything (AIGC)
Generative AI (GAI)
generative diffusion model
networks
Issue Date2024
Citation
IEEE Network, 2024, v. 38, n. 6, p. 501-512 How to Cite?
AbstractGenerative 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.
Persistent Identifierhttp://hdl.handle.net/10722/353193
ISSN
2023 Impact Factor: 6.8
2023 SCImago Journal Rankings: 3.896
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDu, Hongyang-
dc.contributor.authorNiyato, Dusit-
dc.contributor.authorKang, Jiawen-
dc.contributor.authorXiong, Zehui-
dc.contributor.authorZhang, Ping-
dc.contributor.authorCui, Shuguang-
dc.contributor.authorShen, Xuemin-
dc.contributor.authorMao, Shiwen-
dc.contributor.authorHan, Zhu-
dc.contributor.authorJamalipour, Abbas-
dc.contributor.authorPoor, H. Vincent-
dc.contributor.authorKim, Dong In-
dc.date.accessioned2025-01-13T03:02:33Z-
dc.date.available2025-01-13T03:02:33Z-
dc.date.issued2024-
dc.identifier.citationIEEE Network, 2024, v. 38, n. 6, p. 501-512-
dc.identifier.issn0890-8044-
dc.identifier.urihttp://hdl.handle.net/10722/353193-
dc.description.abstractGenerative 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.-
dc.languageeng-
dc.relation.ispartofIEEE Network-
dc.subjectAI-generated everything (AIGC)-
dc.subjectGenerative AI (GAI)-
dc.subjectgenerative diffusion model-
dc.subjectnetworks-
dc.titleThe Age of Generative AI and AI-Generated Everything-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/MNET.2024.3422241-
dc.identifier.scopuseid_2-s2.0-85197482810-
dc.identifier.volume38-
dc.identifier.issue6-
dc.identifier.spage501-
dc.identifier.epage512-
dc.identifier.eissn1558-156X-
dc.identifier.isiWOS:001360457500010-

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