The program of SETN 2020 includes invited talks from internationally distinguished AI researchers.
Day 1 – September 2nd, 11:30-12:30
Keynote 1: Understanding the Humans and Machines System?
Theodoros Evgeniou, INSEAD
Since the development of the early recommender systems, in the mid 90s, a lot of AI research has been focusing on developing machine learning algorithms to better understand human behaviour – with mega-marketing firms such as Google or Facebook driving part of the research agenda. Very recently the discussion has been shifting towards understanding what these algorithms do (e.g., AI explainability) and how they may impact human behaviour – and society – instead. For example, we see new research directions towards fair, accountable, and transparent AI, while regulators now consider how to manage (“new”) AI risks – a couple of decades after the AI community developed massive commercial AI systems. Research questions now shift from developing algorithms to understanding the “humans and machines system”: it is not only about developing algorithms to analyse human behaviour data (e.g., what products, movies, or ads people like, what reviews they write, how they connect with others, trade, drive, etc), but also about developing algorithms to understand AI algorithms themselves (e.g., explainability methods) and, more broadly, methods to understand how humans and machines co-evolve – affecting each other. This talk will be about algorithms to understand human behaviour, algorithms to understand machine behaviour, and questions to explore about how humans and machines may best work together and co-evolve while considering both AI opportunities and risks.
Theodoros Evgeniou is a Professor of Decision Sciences and Technology Management at INSEAD. He has been working on Machine Learning and AI for more than 20 years, in areas ranging from Computer Vision, to Marketing, Healthcare, and Finance, among others. He has received four degrees from MIT, two BSc degrees simultaneously, one in Computer Science and one in Mathematics, as well as Master and PhD degrees in Computer Science. His recent interests focus on the broader topic of AI, business and society, working on areas ranging from AI and regulation to AI innovations for business process optimization and improving decisions as well as on new Machine Learning methods. Professor Evgeniou gives talks and consults for a number of organisations in his areas of expertise.
Day 2 – September 3rd, 11:30-12:30
Keynote 2: Is there such a thing as ethics of AI? What form should it take?
Vincent Müller, Technical University of Eindhoven (TU/e)
The presentation will provide an overview of what is going on in ethics and policy of AI – and ask people what would actually be useful for AI researchers and practitioners.
Main themes in ethics of AI
1 Privacy & Surveillance
2 Manipulation of Behaviour
3 Opacity of AI Systems
4 Bias in Decision Systems
5 Human-Robot Interaction
6 Automation and Employment
7 Autonomous Systems
8 Machine Ethics
9 Artificial Moral Agents
Current policy of AI
1 EU High-level expert group on ethics of AI (HLEG)
2 UNESCO et al.
3 Global Partnership on AI (GPAI)
4 Outlook: EU Cooperation tender (EU +UN + G7 + UNESCO)
Vincent C. Müller is Professor for Philosophy of Technology at the Technical University of Eindhoven (TU/e), University Fellow at the University of Leeds and Turing Fellow at the Alan Turing Institute, London – as well as President of the European Society for Cognitive Systems and Chair of the euRobotics topics group on ‘ethical, legal and socio-economic issues’. He studied philosophy with cognitive science, linguistics and history at the universities of Marburg, Hamburg, London and Oxford. Later, he was Professor at Anatolia College/ACT (Thessaloniki), Stanley J. Seeger Fellow at Princeton University and James Martin Research Fellow at the University of Oxford.
Müller is known for his research on theory and ethics of disruptive technologies, particularly artificial intelligence. He has published >40 academic papers as well as 16 edited volumes on the philosophy of AI and cognitive science, philosophy of computing, philosophy of language, applied ethics, etc. (citations: h-16). He has organised ca. 25 conferences or workshops and presents invited papers around the world. Müller edits the “Oxford handbook of the philosophy of artificial intelligence”, organizes a conference series on the Theory and Philosophy of AI and is principal investigator of a EU-funded project on “Inclusive Robotics for a Better Society” (INBOTS) and on the large AI platform project AI4EU. He is a senior participant on the NL ‘Gravitation’ grant on the “Ethics of Disruptive Technologies” (26.8M€). He has generated ca. 3.9 mil.€ research income for his institutions.
Day 3 – September 4th, 11:30-12:30
Keynote 3: Scalable Machine Learning on Large Sequence Collections
Themis Palpanas, University of Paris
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to analyze very large collections of sequences, or data series. Examples of such applications come from scientific, manufacturing and social domains, where in several cases they need to apply machine learning techniques for knowledge extraction. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics.
In this talk, we argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analytics of very large sequence collections. We describe recent efforts in designing techniques for indexing and analyzing truly massive collections of data series that will enable scientists to run complex analytics on their data. Finally, we present open research directions in the area of big sequence management.
Themis Palpanas is Senior Member of the French University Institute (IUF), a distinction that recognizes excellence across all academic disciplines, and professor of computer science at the University of Paris (France), where he is director of the Data Intelligence Institute of Paris (diiP), and director of the data management group, diNo. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. He has previously held positions at the University of California at Riverside, University of Trento, and at IBM T.J. Watson Research Center, and visited Microsoft Research, and
the IBM Almaden Research Center.His interests include problems related to data science (big data analytics and machine learning applications). He is the author of nine US patents, three of which have been implemented in world-leading commercial data management products. He is the recipient of three Best Paper awards, and the IBM Shared University Research (SUR) Award. He is currently serving on the VLDB Endowment Board of Trustees, as an Editor in Chief for the BDR Journal, Associate Editor in the TKDE, and
IDA journals, as well as on the Editorial Advisory Board of the IS journal, and the Editorial Board of the TLDKS Journal. He has served as General Chair for VLDB 2013, Associate Editor for VLDB 2019 and 2017, Research PC Vice Chair for ICDE 2020, and Workshop Chair for EDBT 2016, ADBIS 2013, and ADBIS 2014, General Chair for the PDA@IOT International Workshop (in conjunction with VLDB 2014), and General Chair for the Event Processing Symposium 2009.
Day 3 – September 4th, 15:15-16:15
Keynote 4: The action grammar
Yiannis Aloimonos, University of Maryland
Actions (what an intelligent autonomous system does) are the fundamental building blocks of the mind of the system. Actions however reside in different spaces, the visual space (actions seen), the auditory space (actions heard), the sensori-motor space (actions performed) and the language space (actions talked about). To achieve intelligence we must be able to map the different spaces to each other. The grammatical structure of these spaces point to a new approach. I will introduce the syntax of action and model action as a formal system, namely as a program in a special language named AL (Action Language). We focus on manipulation actions and we conceive such mappings as compilers, interpreters or translators that take action descriptions in one space and turn them into the equivalent description in another space.
In this talk, I will outline a system called VALC (VALC: Visual AL Compiler) that will automatically translate visual observations of complex manipulation actions to AL programs. I will also outline MALC (MALC: Motor AL Compiler) that will automatically translate programs in AL to a motor execution plan for any robot whose specification is provided. MALC will also allow new ways of approaching reinforcement learning and provide a universal language for cognitive robot programming. Furthermore, I will introduce VALD (Visual AL Debugger), an Augmented Reality system, which will guide a human through the course of an AL program execution, providing visual instructions and feedback upon program violations. Finally, I will briefly describe VALT (Video Action Language Translator), a multimedia system that will produce semantic descriptions (and descriptions in English) of videos containing manipulation actions. In closing, I will describe the relationship of the grammar of action with Chomsky’s universal grammar.
Yiannis Aloimonos ( https://www.prg.cs.umd.edu ) is Professor of Computational Vision and Intelligence at the Department of Computer Science, University of Maryland, College Park, and the Director of the Computer Vision Laboratory at the Institute for Advanced Computer Studies (UMIACS). He is also affiliated with the Department of Electrical and Computer Engineering, the Institute for Systems Research, the Neural and Cognitive Science Program and the Maryland Robotics Center. He was born in Sparta, Greece and studied Mathematics in Athens and Computer Science at the University of Rochester, NY (PhD 1990). He is interested in Active Perception and the modeling of vision as an active, dynamic process for real time robotic systems. For the past ten years he has been working on bridging signals and symbols, specifically on the relationship of vision to reasoning, action and language. He received the Presidential Young Investigator Award from President Bush and the Bodossaki Award in Artificial Intelligence.