Beyond best effort: Reliable and efficient AI for wireless systems
9AI has the potential to revolutionize telecommunication networks, enhancing efficiency, automation, and decision-making. However, most AI models function as black boxes, making their integration into telecom systems inherently risky in terms of reliability and predictability. Additionally, both training and running AI models demand significant computational resources, vast datasets, and high energy consumption. This talk explores innovative solutions to tackle these challenges, focusing on enhancing reliability and efficiency in AI-driven telecom networks. Key topics include pre-deployment calibration for ensuring AI robustness, online monitoring to detect and mitigate failures in real time, semi-supervised learning to reduce data dependency, and neuromorphic computing for energy-efficient AI processing.
Speakers:
Osvaldo Simeone
Co-director, Centre for Intelligent Information Processing Systems, King's College London, UK
Moderators:
Ian F. Akyildiz
ITU J-FET Editor-in-Chief and Truva Inc., USA
Wisdom corner
Moderators:
Alessia Magliarditi
ITU Journal Manager, International Telecommunication Union (ITU)
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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.