Feb. 2, 2024, 9:47 p.m. | Christo Kurisummoottil Thomas Christina Chaccour Walid Saad Merouane Debbah Choong Seon Hong

cs.LG updates on arXiv.org arxiv.org

Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing "AI for wireless" paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitations of data-driven, training-intensive AI. These limitations include the black-box nature of the AI models, their curve-fitting nature, which can limit their ability to reason and adapt, their reliance on large amounts of training data, and …

ai-native artificial artificial intelligence basic challenges course cs.it cs.lg data data-driven extensions generation ai incremental indeed intelligence limitations math.it networks next reasoning technical training will wireless

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Data Engineer

@ Quantexa | Sydney, New South Wales, Australia

Staff Analytics Engineer

@ Warner Bros. Discovery | NY New York 230 Park Avenue South