Bert de Vries received MSc (1986) and PhD (1991) degrees in Electrical Engineering from Eindhoven University of Technology (TU/e) and the University of Florida, respectively. From 1992 until 1999 he worked at Sarnoff Research Center in Princeton (NJ), where he contributed to research projects over a wide range of signal and image processing topics such as word spotting, financial market prediction, and breast cancer detection from mammograms. Since April 1999 he has been employed in the hearing aids industry (currently at GN Hearing), both in research and managerial roles. Since January 2012 he is also a full professor at the Signal Processing Systems Group at TU/e, where he directs the BIASlab research team of graduate students with whom he conducts research on transferring a Bayesian brain theory (the Free Energy Principle) to practical engineering solutions.
Keynote Title: Natural Artificial Intelligence
Abstract: Large language model-based chatbots such as chatGPT are very impressive, but you cannot ask them to go out and learn how to ride a bike. Learning how to ride a bike is about an agent that learns a skill through efficient, real-time interactions with a dynamic environment. In this presentation, I will discuss the underlying technology that enables brains to efficiently learn new skills and acquire new knowledge solely through unsupervised environmental interactions. How much do we understand about what brains compute? And is this knowledge transferable to engineering systems? I will discuss Karl Friston’s Free Energy Principle, which is the theory on what, why and how brains compute. Then I will discuss the efforts of our research lab (http://biaslab.org) at TU Eindhoven to transfer these ideas to working engineering tools.
Dr. Arne Hamann obtained his PhD in Computer Science in 2008 from the Technical University of Braunschweig Germany. He is Chief Expert for "Distributed Intelligent Systems" at Bosch Research. Like the Bosch product portfolios his range of actives is very broad encompassing complex embedded systems where the interaction between physical processes hardware and software plays a major role through to distributed IoT systems with elements of (edge) cloud computing. In the academic contexts he is member of the editorial board of the ACM journal “Transactions on Cyber Physical Systems” and regularly serves as program committee member for international conferences such as EMSOFT, RTSS, RTAS, DAC, ETFA, and ICCPS.
Keynote Title: The Cyber-Physical Metaverse - Building Better Intelligent Systems
Abstract: The concept of Digital Twins (DTs) has been discussed intensively for the past couple of years. Today we have instances of digital twins ranging from static descriptions of manufacturing data and material properties to live interfaces to operational data of cyber physical systems (CPS) and the functions and services they provide. Currently, there are no standardized interfaces to aggregate atomic DTs (e.g., the twin of the lowest-level function of a machine) to higher-level DTs providing more complex services in the virtual world. Additionally, there is no existing infrastructure to reliably link the DTs in the virtual world to the CPSs in the real world. This keynote will address how the Metaverse can become the virtual world where DTs of machines live and how to reliably connect DTs in real-time to the physical world. It will be discussed how this CPS Metaverse is the basis for a paradigm shift in the way intelligent systems are built in the future. Automated driving, for example, is currently mostly thought of from the ego perspective of each individual vehicle. The CPS Metaverse enables us to shift to a bird's eye view - a collaborative perspective - as it allows information from CPSs to be reliably fused into a real-time distributed DT enabling better and more intelligent decisions. Insights in current activities of Bosch Research and its academic partners to move towards this vision will be provided.
Mehdi Dastani is a professor of Artificial Intelligence and chair of the Intelligent Systems group of the department of Information and Computing Sciences at Utrecht University, the Netherlands. His research focuses on formal and computational models in artificial intelligence. Inspired by knowledge and insights from other scientific disciplines such as philosophy, psychology, economy and law, Dastani investigates and develops computer models for autonomous agents whose behaviors are decided based on reasoning about social and cognitive concepts such as knowledge, desires, norms, responsibility and emotions.
Taco Cohen is a machine learning researcher at Qualcomm AI Research in Amsterdam. He received a BSc in theoretical computer science from Utrecht University, and a MSc in artificial intelligence and PhD in machine learning (with prof. Max Welling) from the University of Amsterdam. He was a co-founder of Scyfer, a company focussed on deep active learning, acquired by Qualcomm in 2017. His research interests include equivariant networks and geometric deep learning, causality and interactive learning. During his studies he has interned at Google Deepmind (working with Geoff Hinton) and OpenAI. He received the 2014 University of Amsterdam MSc thesis prize, a Google PhD Fellowship, ICLR 2018 best paper award for “Spherical CNNs”, was named one of 35 innovators under 35 by MIT Tech Review, and won the 2022 ELLIS PhD Award and 2022 Kees Schouhamer Immink prize for his PhD research.