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seabet Online real money casino report by Professor Chen Kai of Hong Kong University of Science and Technology and researcher Chen Kai of Chinese Academy of Sciences

Source: Click: Time: August 23, 2024 16:56


Reporter:Hong Kong seabet Online real money casino of Science and TechnologyProfessor seabet Online real money casino Kai

Reporting location:Messagefloor114Conference Room

seabet Online real money casino time:2024year8month24Day (weeksix下注noon3points

seabet Online real money casino title:Towards AI-centric Networking: Challenges and Opportunities

Personal introduction: seabet Online real money casino Kai, Professor, Department of Computer Science and Engineering, Hong Kong seabet Online real money casino of Science and Technology,Intelligent Network and System Laboratory(iSING Lab)Director, HKUST-Director of Inspur Advanced Cloud Intelligent Technology Joint Laboratory, Hong Kong seabet Online real money casino of Science and Technology-Director of WeChat Artificial Intelligence Technology Joint Laboratory, Hong Kong seabet Online real money casino of Science and Technology-Deputy Director of China Unicom Joint Laboratory, Hong KongRGCChief Scientist of Theme seabet Online real money casino Program。Mainly engaged in data center networking、High performance network、AIseabet Online real money casino on computing power networks, machine learning systems, etc., on many occasionsACM SIGCOMM, USENIX NSDI/OSDI, IEEE/ACM TONPublished articles in top international seabet Online real money casino conferences and journals, GetIEEE ICNP 2023Best Paper Award andACM SIGCOMM 2010Best Paper Nomination Award, served asSIGCOMM, NSDI, EuroSys, TONProgram member and editorial board member of other international conferences and journals,is an Asia Pacific webinarAPNetInitiator and Steering Committee Chairman.

Abstract:The ever-growing AI and ML workloads present unprecedented challenges as well as opportunities for designing AI-centric networking for AI clusters. In this presentation, I will first talk about the special characteristics of communication with the distributed AI/ML training and then discuss how to explore these characteristics in designing next-generation network architecture and protocols for AI and ML workloads.



Reporter:Researcher seabet Online real money casino Kai, Chinese Academy of Sciences

Reporting location:Messagefloor114Conference Room

seabet Online real money casino time:2024year8month24Day (weeksix下注noon4points

seabet Online real money casino title:Large Model Security: Opportunities and Challenges

Personal introduction: seabet Online real money casino Kai,National leading talents in science and technology innovation,Researcher at the Institute of Information Technology, Chinese Academy of Sciences、Doctoral Supervisor,Director of Frontier Innovation and Science and Education Integration Center,Director of the Editorial Department of "Journal of Information Security"。Won the Young Scientist Award of the Chinese Academy of Sciences、CCF-IEEE CSYoung Scientist Award、Beijing Science and seabet Online real money casino Award for Outstanding Youth Zhongguancun Award、NASACYouth Software Innovation Award、Beijing “Outstanding Youth” Fund、Beijing Zhiyuan Young Scientist and many other honors。Main seabet Online real money casino areas include system security、Artificial Intelligence Security。atIEEE S&PUSENIX SecurityACM CCSNDSSICSEPublished seabet Online real money casino papers at high-level conferences and journals in the field of information security200Multiple articles。Presided over a major research project of the National Natural seabet Online real money casino Foundation、Key projects, etc.。

Abstract:The emergence of large models has had an important impact on security seabet Online real money casino。On the one hand, it can empower traditional offensive and defensive methods,Make it smarter and more efficient;On the other hand,Its own security issues,such as"Jailbreak" problem、"Illusion" problem、Privacy issues, etc.,Also raises concerns when using large models。This seabet Online real money casino will focus on these two aspects,Focus on how to use large models to empower traditional security offense and defense,We will also talk about the safety issues of the large model itself, etc.。



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