A major difference between today’s AI agents and living creatures is the that former are designed for specific actions and the latter decide what to do on their own within the bounds of survival and reproduction. Creating life-like autonomous AI agents can bring benefits, such as more flexible services, new industries and scientific discoveries, but their potential dangers should be carefully assessed and preempted. Here we discuss how understanding of the architecture and functions of the brain can help creating autonomous AI agents and harnessing their behaviors to suit our society. It is argued that the democratic practices that evolved through human history are crucial in avoiding disasters from over-concentration of power, and that understanding the social brain mechanisms underlying considerate human behaviors is a key to building human-compatible AI systems.
Kenji Doya is a Professor of Neural Computation Unit, Okinawa Institute of Science and Technology (OIST) Graduate University. He took his PhD in engineering in 1991 at University of Tokyo. After postdoctoral training at U. C. San Diego and Salk Institute, in 1994 he joined Advanced Telecommunications Research International (ATR) as a Senior Researcher. In 2004, he was appointed as a Principal Investigator of the OIST Initial Research Project and started Okinawa Computational Neuroscience Course (OCNC) as the chief organizer. As OIST established itself as a Graduate University in 2011, he became a Professor and served as the Vice Provost for Research. He is interested in reinforcement learning in both natural and artificial creatures. He has served as a Co-Editor in Chief of Neural Networks from 2008 to 2021 and the Chairperson of Japan Neuroscience Society Meeting (Neuro2022) in Okinawa. He currently serves as a board member of International Neural Network Society (INNS) and Asia-Pacific Neural Network Society (APNNS), and the President of Japanese Neural Network Society (JNNS). He received INNS Donald O. Hebb Award in 2018, APNNS Outstanding Achievement Award and JNNS Academic Award in 2019, and age-group 2nd place at Ironman Malaysia in 2022.
Most network traffic is encrypted nowadays, including network traffic generated by massive low-end IoT devices. This poses great challenges to network traffic classification because traditional techniques like deep packet inspection cannot be applied to encrypted network traffic. In this talk, I will introduce the challenges in performing fine-grained encrypted network traffic classification and some recent solutions to address these challenges. Our investigation on recognizing IoT devices based on encrypted network traffic will also be introduced.
Shigeng Zhang is currently a full Professor of Computer Science at the School of Computer Science and Engineering, Central South University. He received the BSc, MSc, and DEng degrees, all in Computer Science, from Nanjing University, China, in 2004, 2007, and 2010, respectively. His research interests include IoT security, AI security, and smart wireless sensing. He has published more than 100 technique papers in top international journals and conferences including TIFS, JSAC, TMC, Ubicomp, Infocom, Mobihoc, and DSN. He is a Distinguished Member of CCF and a member of IEEE and ACM.
With the advances in Machine Learning techniques, extracting actionable intelligence from large data sets have changed many technologies. Concepts such as Industry 4.0, Threat Intelligence, Autonomous Vehicles are now commonplace with Machine Learning being a prime driver. In this talk, we discuss how Cyber Security — a necessary technology in today’s digitalized world has also been impacted by Machine Learning. In particular, we discuss two specific areas — malware analysis, and intrusion detection where we have been using machine learning to provide effective security to systems and networks. We also discuss briefly, how machine learning processes – i.e training, and classification/regression etc., are also being attacked by cyber attackers, and how machine learning techniques are evolving through these threats through adversarial machine learning.
Prof. Sandeep K. Shukla is an IEEE fellow, and ACM Distinguished Scientist. He is currently a professor of Computer Science and Engineering department at IIT Kanpur which he headed during 2017-2020. He was the editor in chief of the ACM Transactions on Embedded Computing Systems during 2013-2020. He is currently associate editors of ACM Transactions on Cyber Physical Systems, and Journal of the British Blockchain Association. In the past he has served as associate editors of IEEE Transactions on Computers, IEEE Transactions on Industrial Informatics, IEEE Design and Test, and IEEE Embedded Systems Letters. Before joining IIT Kanpur in 2015, he was a professor at Virginia Tech, USA. He served as ACM Distinguished Speaker, and IEEE Computer Society Distinguished Visitor in the past. He has authored over 200 peer reviewed journal and conference papers, and authored/edited 10 books. He was awarded the Presidential Early Career Award in Science and Engineering (PECASE) in 2004, The Bessel Award by Humboldt Foundation in 2009, a Distinguished Alumnus Award by SUNY Albany in 2007, a Ramanujan Fellowship in 2015. His major research interest is Cyber Security of Critical Infrastructures, Cyber Security of IT/OT systems, and Applications of Blockchain Technology in Security and Privacy.
To be announced soon.