2019 - ILIAS Distinguished Lectures

Distinguished Lectures - see also here

ORGANISERS:

Prof Christoph Schommer
Prof Leon van der Torre
Prof Raymond Bisdorff
Prof Martin Theobald
Prof Ulrich Sorger
Prof Pascal Bouvry
Prof Peter Ryan
 

DISTINGUISHED LECTURES (2022)

DISTINGUISHED LECTURE XXV

Dr. Kary Främling, Umea University, Sweden

Contextual Importance and Utility: how Could it Change Explainable AI?
21 March 2022, 16h00 via Webex

Meeting link: https://unilu.webex.com/unilu/j.php?MTID=m513f8ada798af729205020f4669e74bf
Meeting number: 2730 181 3468  --- Password: akRzARHp222

Abstract: Explaining the results of AI systems in ways that are understandable for different categories of end-users has been a challenge for AI since its beginning. This challenge has become even greater in recent years with increasingly complex machine learning models, leading to the development of numerous so called explainable AI (XAI) methods. In practice it seems like a majority of XAI research is focusing on trying to explain image classification, e.g. "I believe there's a cat in this image and I show you where it is". Such explanations are visually attractive and might help developers of deep neural networks to assess how well their network has learned what it is supposed to. However, such XAI methods do not give added value to the citizen whose loan application was refused or who didn't get called to a job interview, as decided by an AI system. It can be doubted whether current XAI methods are capable of providing such insight, and whether they actually allow to detect bias or discrimination in AI systems. Some solutions are presented, such as the Contextual Importance and Utility (CIU) method.

Host: Dr. Aleks Knoks, ICR Research Group, University of Luxembourg

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DISTINGUISHED LECTURES (2019)

DISTINGUISHED LECTURE XXIV

Prof Toby Walsh

Professor of artificial intelligence at the University of New South Wales

The AI Future

Time and Place: 27 June 2019, 16h00 - 17h00. NEW: Room MSA 3.370

Abstract: Artificial intelligence offers considerable promise - the robots can perhaps take the sweat, and do all the dirty, dull and difficult jobs - but how do we ensure the future is bright? How long even do we have before machines are as capable as us? And when they are, how can we be sure they'll behave ethically and to our benefit?
 
Bio: Toby Walsh is a leading researcher in Artificial Intelligence. He was named by the Australian newspaper as a "rock star" of Australia's digital revolution. He is Scientia Professor of Artificial Intelligence at UNSW, leads the Algorithmic Decision Theory group at Data61, Australia's Centre of Excellence for ICT Research, and is Guest Professor at TU Berlin. He has been elected a fellow of the Australian Academy of Science, and has won the prestigious Humboldt research award as well as the NSW Premier's Prize for Excellence in Engineering and ICT. He has previously held research positions in England, Scotland, France, Germany, Italy, Ireland and Sweden. He regularly appears in the media talking about the impact of AI and robotics. He is passionate that limits are placed on AI to ensure the public good. In the last two years, he has appeared in TV and the radio on the ABC, BBC, Channel 7, Channel 9, Channel 10, CCTV, CNN, DW, NPR, RT, SBS, and VOA, as well as on numerous radio stations. He also writes frequently for print and online media. His work has appeared in the New Scientist, American Scientist, Le Scienze, Cosmos, the Conversation and "The Best Writing in Mathematics". His twitter account has been voted one of the top ten to follow to keep abreast of developments in AI. He often gives talks at public and trade events like CeBIT, the World Knowledge Forum, TEDx, and Writers Festivals in Melbourne, Sydney and elsewhere. He has played a leading role at the UN and elsewhere on the campaign to ban lethal autonomous weapons (aka "killer robots").
 
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 DISTINGUISHED LECTURE XXIII

Prof Dr Marco Aldinucci, Associate Professor at Computer Science Department, University of TorinoPrincipal Investigator of the Parallel Computing group

The evolution of high-performance systems: from HPC to Big Data to Deep Learning

Time and Place: 5 June 2019, 15h00 - 16h00. Room MSA 4.510

Abstract : Computer science evolves through successive abstractions. Today, after 30 years of lethargy, high-performance computing (HPC) is extending beyond its traditional fields of application. For years HPC systems have been feeding with differential equations; the ability to calculate many mathematical operations per second (FLOPS) was the key to solving ever larger problems and to find ever more precise solutions. The explosion of data resulting from digital transformation has shifted the demand for high performance from traditional applications (equations, simulations, etc.) to methods for the analysis of large amounts of data (BigData, Deep Learning, etc). Under this impulse, the programming and use models of HPC systems are evolving towards much more abstract models, able to satisfy different application needs and to simplify the development of new applications. The challenges for designers are renewed: from FLOPS to the efficient management of data in memory; from mathematics in double precision to that in small but efficient precision for deep neural networks. A blow of life for high-performance systems researchers: experimenting with new workloads, platforms, programming models, provisioning models. To meet these challenges, the University of Turin and Polytechnic University of Turin have joined forces in the HPC4AI centre to create a federated competence centre on High-Performance Computing (HPC), Artificial Intelligence (AI) and Big Data Analytics (BDA). A centre capable to collaborate with entrepreneurs to boost their ability to innovate on data-driven technologies and applications. In the talk some recent results on distributed training at scale will be presented.
 
Bio : Marco Aldinucci is an associate professor at Computer Science Department of the University of Torino (UNITO) since 2014. Previously, he has been postdoc at University of Pisa, researcher at Italian National Research Agency (ISTI-CNR), and University of Torino. He is the author of over a hundred papers in international journals and conference proceeding. He has been participating in over 20 national and international research projects concerning parallel and autonomic computing. He is the recipient of the HPC Advisory Council University Award 2011, the NVidia Research award 2013, the IBM Faculty Award 2015. He is the P.I. of the parallel computing group alpha@UNITO, the director of the “data-centric computing” laboratory at ICxT@UNITO innovation centre, vice-president of the C3S@UNITO competency centre, and the coordinator of HPC4AI. From Nov. 2018, he is a member of the Governing Board of the EuroHPC Joint Undertaking. He co-designed, together with Massimo Torquati, the FastFlow programming framework and several other programming frameworks and libraries for parallel computing. His research is focused on parallel and distributed computing.
 
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DISTINGUISHED LECTURE XXII

Prof. El-Ghazali Talbi, University of Lille & INRIA (France) and Invited Professor @ University of Luxembourg

How Machine Learning can help Optimization

Time and Place: 2 May 2019, 16h00, Room: MSA 3.540

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Abstract : During the last years, research in using machine learning (ML) in designing efficient and effective optimization algorithms such as metaheuristics have become increasingly popular. Many of those hybrid approaches have generated high quality results and represent state-of-the-art optimization algorithms. Although various hybrid appproaches have been used, there is a lack of a comprehensive survey on this research topic. In this talk we will investigate the different opportunities for using ML into metaheuristics. We define the various ways synergies may be achieved. A new detailed taxonomy is proposed according to the concerned search component: target optimization problem, low-level and high-level components of metaheuristics. We also identify some open research issues in this topic which needs further in-depth investigations.

Bio :
Prof. El-ghazali Talbi received the Master and Ph.D degrees in Computer Science, both from the Institut National Polytechnique de Grenoble in France. Then he became an Associate Professor in Computer Sciences at
the University of Lille (France). Since 2001, he is a full Professor at the University of Lille and the head of the optimization group of the Computer Science laboratory (CRISTAL). He is an invited Professor at the University of Luxembourg (2019-2022). His current research interests are in the field of optimization, parallel algorithms, metaheuristics, high-performnce computing, , optimization and machine learning, and application to energy, logistics/transportation, bioimedical and networks. Professor Talbi has to his credit more than 300 publications in journals, chapters in books, and conferences. He is the co-editor of ten books. He was a guest editor of more than 17 special issues in different journals (Journal of Heuristics, Journal of Parallel and Distributed Computing, European Journal of Operational Research, Theoretical Computer Science, Journal of Global Optimization). He was the head of the INRIA Dolphin project and the
bioinformatics platform of the Genopole of Lille. He has many collaborative national, European and international projects. He is the co-founder and the coordinator of the research group dedicated to Metaheuristics: Theory and Applications (META). He served as a conference chair of more than 20 international conferences (e.g. EA'2005, ROADEF'2006, META'2008, IEEE AICCSA'2010, META'2014, MIC'2015, OLA'2018, MOPGP'2019).
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DISTINGUISHED LECTURE XXI

Prof Dr Philipp Slusallek, University Saarbrücken and DFKI Saarbrücken

CLAIRE: A European Initiative for "Excellence in All of AI, for all of Europe, with a Human-Centered Focus

( Understanding the World with AI: Training and Validating Smart Machines Using Synthetic Data ) 

Time and Place: 7 March 2019, 16h00, Room: MSA 2.240

Abstract : The world around us is highly complex but Autonomous Systems must be able to reliably make accurate decisions that in many cases may even affect human lives. With Digital Reality we propose an approach that instead of only relying on real data, learns models of the real world and uses synthetic sensor data generated via simulations, for the training and -- even more importantly -- the validation of Autonomous Systems. This is extended by a continuous process of validating the models against the real world for improving and adapting them to a changing environment. A highly relevant application of this approach is in intelligent sensor systems. Using a model about the object to be measured and the measuring process these systems are aware of what and how they are measuring and can adapt the measuring strategy and parameters accordingly, e.g. to obtain accurate measurements or target high throughput.

Bio :
Philipp Slusallek is Scientific Director at the German Research Center for Artificial Intelligence (DFKI), where he heads the research area on Agents and Simulated Reality. At Saarland University he has been a professor for Computer Graphics since 1999, a principle investigator at the German Excellence-Cluster on “Multimodal Computing and Interaction” since 2007, and Director for Research at the Intel Visual Computing Institute since 2009. Before coming to Saarland University, he was a Visiting Assistant Professor at Stanford University. He originally studied physics in Frankfurt and Tübingen (Diploma/M.Sc.) and got his PhD in Computer Science from Erlangen University. He is associate editor of Computer Graphics Forum, a fellow of Eurographics, a member of acatech (German National Academy of Science and Engineering), and a member of the European High-Level Expert Group on Artificial Intelligence. His research covers a wide range of topics including artificial intelligence, simulated/digital reality, computational sciences, real-time realistic graphics, high-performance computing, motion modeling & synthesis, novel programming models, 3D-Internet technology, and others.