報告時間：2025年3月3日9:30-11:00
報告地點：電航樓218
報告摘要：The evolution of generative artificial intelligence (GenAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE's efficiencies, GenAI still faces challenges in resource utilization when deployed on local user devices. Therefore, we first propose mobile edge networks supported MoE-based GenAI. Rigorously, we review the MoE from traditional AI and GenAI perspectives, scrutinizing its structure, principles, and applications. Next, we present a new framework for using MoE for GenAI services in Metaverse. Moreover, we propose a framework that transfers subtasks to devices in mobile edge networks, aiding GenAI model operation on user devices. Moreover, we introduce a novel approach utilizing MoE, augmented with Large Language Models (LLMs), to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts, and weights each decision from the participating experts. In this process, the LLM acts as the gate network to oversee the expert models, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore, it can also leverage LLM's advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly, we insightfully identify research opportunities of MoE and mobile edge networks.
報告人簡介：Dusit (Tao) Niyato is a professor in the College of Computing and Data Science, at Nanyang Technological University, Singapore. He is an IEEE Fellow and an IET Fellow. His research interests include generative artificial intelligence, the Internet of Things, edge intelligence metaverse, mobile and distributed computing, and wireless networks. He has received numerous academic awards and honors, including the IEEE ComSoc Asia-Pacific Best Young Researcher Award, the 2011 IEEE Communications Society Fred W. Ellersick Paper Award, and the 2022 Distinguished Technical Achievement Recognition Award. He currently serves as the Editor-in-Chief of IEEE Transactions on Network Science and Engineering and is the Area Editor of IEEE Communications Surveys and Tutorials and IEEE Transactions on Vehicular Technology. He has been recognized as a Highly Cited Researcher in the field of Computer Science by Clarivate Analytics for several consecutive years.
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信息科學技術學院
2025年3月3日
English
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