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Dick Bulterman

Vrije Universiteit Amsterdam & CWI, The Netherland

Pre-reflections on a Post-truth Society​

 

Abstract: Provable truth would provide the foundation of all forms of human intelligence and would form the basis for computational understanding of many forms of information. ‘Computational truth’ would allow the automated means of resolving personal, societal and scientific conflicts and it would serve the public good.This talk provides a short history of ‘truth’. It reviews the ways in which thinkers have tried to encode truths that were previously self-evident, and it describes challenges in using provable truths to influence public and private debates. It is clear that, in the short term, truth has fallen out of favor in guiding the public debate, but it still provides a glimmer of hope as a possible way to restore public confidence and consensus.

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Bio: Dick Bulterman is CWI Fellow at Centrum Wiskunde & Informatica, where he was senior researcher and head of the group Distributed and Interactive Systems from 1988 to 2014. He is also emeritus professor of computer science at the Vrije Universiteit in Amsterdam, where was Chair of the Department of Computer Science from 2016-2018. From 2013-2016 he was President and CEO of FX Palo Alto Laboratories in California (USA). Between 1997 and 2002, Bulterman was the Managing Director of Oratrix Development bv, a CWI spin-off technology company. Before joining CWI, he was a professor of computer engineering at Brown University in Providence, RI.

 

Bulterman has been active in the multimedia community since 1993 and has served in various roles on ACM MM organizing committees.  He is the chair of the ACM Web Conference steering committee and is a past chair of ACM SigWEB. He was a founding editorial board member of ACM TOMCCAP and ACM/Springer Multimedia Tools and Applications, and is associate editor of Springer Multimedia Systems. He is past chair of W3C's Synchronized Multimedia working group and was involved in the development of a host of W3C standards. He is the recipient of the ACM SIGMM Lifetime Technical Achievement Award (2014).

 

Bulterman received a Ph.D. in computer science from Brown University in 1981.

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Sven Helmer

University of Zurich, Switzerland

Prediction is Hard, Especially about the Future

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Abstract: The intrinsic predictability of a time series implies how well an (ideal) prediction algorithm would be able to predict it, i.e., how close the algorithm can come to what is potentially achievable. This is an interesting fundamental problem that has not received a lot of attention, even though prediction models are a hot topic. Being able to quantify the intrinsic predictability would allow us to benchmark prediction algorithms in a model-independent way and help developers and researchers in making a decision whether it is worth pursuing a better solution for a given prediction problem or not. After introducing the concept of intrinsic predictability, we focus on entropy-based approaches and illustrate the challenges they face trying to quantify predictability.

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Bio: Sven Helmer is a senior researcher in the Department of Informatics at the University of Zurich, Switzerland, after holding positions as Associate Professor in the Faculty of Computer Science at the Free University of Bozen-Bolzano, Italy, and as Senior Lecturer at Birkbeck, University of London. He acquired a PhD from the University of Mannheim, Germany, an MSc in Computer Science from the University of Karlsruhe, Germany, and also spent some time as a visiting professor at the University of Heidelberg. He has taught and is teaching courses on data science, databases, and information security; his research interests include database systems, cloud computing, Raspberry Pis, query optimization, route planning, complex event detection, as well as interdisciplinary research in the areas of information systems and ethnography. He has published more than 90 peer-reviewed papers and book chapters.

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C.-C. Jay Kuo

University of Southern California, USA

Mobile/Edge Visual Analytics via Green AI

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Abstract: Mobile/edge visual analytics will prevail in the modern AI era. Most researchers focus on deep-learning-based model compression to achieve this goal. Model compression can reduce the model size by 50-80% with slight performance degradation. Model compression relies on an existing larger model. The training cost of such a large model remains. The compression step also demands resources. I have worked on green AI since 2014, published many papers on this topic, and coined this emerging field “green learning.” Green learning demands low power consumption in both training and inference. It has attractive characteristics, such as small model sizes, fewer training samples, mathematical transparency, ease of incremental learning, etc. It can reduce the model size of its deep-learning counterpart by 95-99%. The training can be conducted from scratch. The resulting model is inherently smaller. It is ideal for mobile and edge devices. Green learning relies on signal-processing disciplines such as filter banks, linear algebra, subspace learning, probability theory, etc. Although it exploits optimization, it avoids end-to-end system optimization, a non-convex optimization problem. Instead, it adopts modularized optimization, and each optimization problem can be cast as convex optimization. In this example, I will use several examples to demonstrate the advantages of green learning in visual analytics for mobile/edge devices.

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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 the Ming Hsieh Chair Professor, a Distinguished Professor of Electrical and Computer Engineering and Computer Science, and the Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, ACM, IEEE, NAI, 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 the Editor-in-Chief of 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 179 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows.

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