(in alphabetical order)
Vrije Universiteit Amsterdam & CWI, The Netherland
Reflections on the State of Semantics and Semantic Computing
Abstract: During the past decade, the role of semantic computing has grown from a novelty to a mainstream technology. Where once youthful enthusiasm, supported by broadly-based claims of lightly-verified impact, dominated most research results, the time seems ripe for a new era of academic responsibility and rigor. In this talk, we reflect on three areas of semantic computing and look at past results and future potential. Using the human body as a framework, we consider problems of semantic reasoning (the head), of content-based perception (the nervous system) and the study of the emotional limitations of the machine (the heart). Our goal in each area is to contrast past work with the paths and needs for both a more robust technology and a more robust society.
Bio: Dick Bulterman is Fellow at CWI: the Center Mathematics and Computer Science in Amsterdam, where he has held numerous positions since 1988. He is also Emeritus Professor of Computer Science at the Vrije Universiteit, also in Amsterdam. He is also the Chair of the ACM Web Conference steering committee. In addition to his appointment at CWI and the VU, Bulterman and founder and managing director of Oratrix Development in Amsterdam and was President/CEO of FXPAL in Palo Alto. His research has concentrated on computer networking, temporal specification languages and systems to support the creation and deployment of rich media content across the public Internet. He is former Chair of ACM SIGWEB and has served in multiple capacities for ACM SIGMM. He was the recipient of the SIGMM Lifetime Technical Achievement Award in 2014. He has been associate editor of every major journal in his research areas and has been general chair or program chair for over a dozen international events. He has been a frequent speaker on matters of technology and society. He has been active with the IEEE International Conference on Semantic Computing for nearly a decade.
C.-C. Jay Kuo
University of Southern California, USA
Green Learning: Introduction, Examples, and Outlook
Abstract: The rapid advances in artificial intelligence in the last decade are primarily attributed to the wide applications of deep learning (DL). Yet, the high carbon footprint yielded by larger DL networks is a concern to sustainability. Green learning (GL) has been proposed as an alternative to address this concern. GL is characterized by low carbon footprints, small model sizes, low computational complexity, and mathematical transparency. It offers energy-effective solutions in cloud centers and mobile/edge devices. It has three main modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) decision learning. GL has been successfully applied to a few applications. This talk provides an overview on the GL solution, its demonstrated examples, and its technical outlook. The connection between GL and DL will also be discussed.
Bio: Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as William M. Hogue Professor, Distinguished Professor of Electrical and Computer Engineering and Computer Science, and Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, NAI, IEEE, and SPIE and an Academician of Academia Sinica. Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was Editor-in-Chief for the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 165 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows.
Bank of America, USA
Opportunities in Semantic Computing
Abstract: Graph representation and knowledge graphs provide unique opportunities in representing complex systems. Enterprise knowledge graphs have become increasingly popular in recent years. They are actively used in natural language processing applications, recommender systems, drug discovery, image understanding and semantic search. Semantic networks are seen as important tools in the future of machine learning systems and AI. Reasoning over knowledge graphs enables opportunities in complementing the pattern detection capabilities of the traditional machine learning solutions with interpretability and reasoning. Knowledge graphs are frequently challenged by the size and complexity of real-world applications and face significant limitations in representing complex relationships. In this talk, we explore the issues in representing complex, transient, uncertain real-world relationships in knowledge graphs. We argue that constraining semantic networks to purely data storage systems may be a restricting assumption. Integration of computing, memory and communication capabilities in non Von Neumann architectures provides unique opportunities in advancing graph-based system capabilities. We look at architectural and system design paradigms to explore potential solution paths towards truly semantic computing.
Bio: Dr. Eren Kurshan is the Executive Head of AI and Machine Learning for Client Protection at Bank of America Corporation. In this role she is responsible for leading the development of custom Machine Learning and Deep Learning solutions for fraud detection, prevention and operational improvement for Bank of America. Dr. Kurshan and her team built the first generation of in-house AI and Machine Learning models for Bank of America's payment systems portfolio (including Credit Card, Debit Card, ATM, Wire, ACH, P2P Payments, Checks, Deposits, Online/Bill Pay transactions, Alert Processing and Prioritization etc) processing over 50MM transaction/day volume in real-time. Prior to her role at Bank of America, Dr. Kurshan has served as the executive lead for various AI and Data Science Programs at Columbia University, J.P. Morgan Corporate and Investment Bank, and IBM. Dr. Kurshan was a Visiting Fellow at Princeton University Center for Information Technology Policy during 2015-2016. She has been serving as an Adjunct Professor of Computer Science at Columbia University since 2014. Dr. Kurshan received her Ph.D. in Applied Algorithms and Theoretical Computer Science from the University of California. She has over 60 peer reviewed technical conference and journal publications and over 100 patents. She was the recipient of 2 Best Paper Awards from IEEE and ACM Conferences, Outstanding Research and Corporate Accomplishment Awards from IBM.
University of Colorado Boulder, USA
Large Language Models as Support for People with Disabilities
Abstract: Large Language Model technology, seen for example in GPT-3, LaMDA, or chatGPT, may provide new ways to support people with disabilities. Automatic text simplification may become practical, as well as assistance in navigating complex Web sites. Artificial personal assistants, flexible enough to deal with a useful range of situations, may be possible. The new technology may be able to meet the wide-ranging critiques of earlier systems, posed by Harold Garfinkel, Philip Agre, and Virginia Eubanks, a development with theoretical, as well as practical, implications.
Bio: Clayton Lewis is Emeritus Professor of Computer Science at the University of Colorado Boulder. Lewis served previously as Co-Director for Technology for the Coleman Institute for Cognitive Disabilities, and Fellow of the Institute of Cognitive Science, at CU. He is known for his research on evaluation methods in user interface design. He has also contributed to cognitive assistive technology, to programming language design, to educational technology, and to cognitive theory in causal attribution and learning. He has been honored by appointment to the ACM SIGCHI Academy, by the SIGCHI Social Impact Award, and by the ACM SIGACCESS Outstanding Contribution Award.