Bayesian Theory of Surprise to Quantify Degrees of Uncertainty
Nelly Bencomo, Durham University, United Kingdom
Keynote Lecture
Alberto Del Bimbo, Dipartimento Ingegneria dell'Informazione, Università degli Studi di Firenze, Italy
The Challenge of Computing Responsible AI
Thomas B. Moeslund, Aalborg University, Denmark
Bayesian Theory of Surprise to Quantify Degrees of Uncertainty
Nelly Bencomo
Durham University
United Kingdom
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Brief Bio
Nelly Bencomo is a professor in the CS Department at Durham University and the leader of the Research Team at SE@Durham. In 2019, Nelly was granted the Leverhulme Fellowship “QuantUn: quantification of uncertainty using Bayesian surprises. ”Before, she was awarded a Marie Curie Fellow at INRIA Paris - Roquencourt. The Marie Curie project is called Requirements-aware Systems (nickname: Requirements@run.time). Nelly exploits the interdisciplinary aspects of model-driven engineering (MDE), software engineering, comprising both technical and human concerns, while developing techniques for intelligent, autonomous and highly distributed systems. With other colleagues, she coined the research topic models@run.time. Her research informs the design of systems that involve communities of people and technology (https://aihs.webspace.durham.ac.uk/socio-technical-systems/). She is the PI of the EPSRC Twenty20Insight research project. Twenty20Insight is an interdisciplinary project bringing together academic experts in Software Engineering (SE), RE, Design Thinking and ML to help system stakeholders and developers understand and reason about the impact of intelligent systems on the world in which they operate. Twenty20Insight actively supports the explainability of the exposed behaviour by the running system. She also leads the EPSRC IAA Project weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms.
Nelly has actively participated in different European Projects and the EPSRC in the UK regarding self-adaptive and autonomous systems. She was the program chair of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) in 2014 and co-program chair of the 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in 2018 and the ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems MODELS’22. Nelly is an Associate Editor of ACM Transactions on Autonomous and Adaptive Systems and was an Associate Editor of IEEE Transactions on Software Engineering (TSE) and a member of the Editorial Board of the Journal of Software and Systems. She is also a member of the IEEE TCSE (Technical Council on Software Engineering) members-at-large (2020-24) and the Steering Committee of MODELS. She has served as a PC member and organizing team member of multiple SE-related Conferences (e.g., ICSE, ASE, MODELS, RE, REFSQ, ICSA).
Website: www.nellybencomo.me
Abstract
In the specific area of software engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and artificial intelligence (AI). We are just starting. In this talk, we will talk about a novel and formal Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behaviour. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a “surprising” event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening. We will discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian Inference and Partially Observable Markov Decision Processes (POMDPs). We will also discuss and cover different Surprise-based approaches to quantifying uncertainty (Bayesian Surprise, Shannon Surprise, Bayes Factor Surprise, and Bayes Factor Surprise) and work related to Digital Twins.
Keynote Lecture
Alberto Del Bimbo
Dipartimento Ingegneria dell'Informazione, Università degli Studi di Firenze
Italy
http://www.micc.unifi.it/delbimbo/
Brief Bio
Available soon.
The Challenge of Computing Responsible AI
Thomas B. Moeslund
Aalborg University
Denmark
Brief Bio
Professor Moeslund received his PhD from Aalborg University in 2003 and is currently leading the Visual Analysis and Perception lab at Aalborg University (~25 people), the Media Technology section at Aalborg University (~40 people) and the AI for the People Center at Aalborg University (~140 people). His research covers all aspects of software systems for automatic analysis of data - especially visual data. He has been involved in more than 50 national and international research projects. Professor Moeslund has (co-)edited eight special journal issues and (co-)chaired 30+ scientific events. He has published 300+ books, peer reviewed journal and conference papers, and been cited 19,500+ times (H-index 55). Awards include a most cited paper award, a teacher of the year award, an innovation award, and 8 best paper awards.
https://thbm.blog.aau.dk/
Abstract
Current and upcoming regulatory frameworks for governing the use of AI include notions like ‘Human-centric AI’, ‘Fair AI’, ‘Robust AI’, ‘The right to be forgotten’, etc. While such concepts seem reasonable it becomes a bit more fuzzy when defining how to make these auditable. This in turn has resulted in new and increased spotlight on both classical and novel scientific topics such as Machine Unlearning, XAI, Uncertainty, Model Drift and Accuracy. In this talk we will dwell into these matters by unfolding the relevant concepts and exemplifying the current gap between high-level notions and practical solutions.