Round table – Artificial Intelligence and the Platform Economy – a new Challenge for Quality Assurance
How AI will influence economy in future? What does it mean for Quality of products, services and QA practices? Will AI replace QA engineers?
Those questions will be discussed with the moderator Albert Eng, Comtrade Digital Services and four interesting panelists.
Albert Eng, Strategist at Comtrade Digital Services, USA
Albert is a Strategist as well as a Mergers and Acquisition leader in Fintech and AI. He’s advised C-suite executives on how to acquire leading edge IT capability via acquisition, joint ventures and partnerships. He will have the pleasure of stirring the pot with a panel of distinguished Academics on key AI topics in the Platform Economy.
Fons Rademakers, PhD, CERN openlab Chief Research Officer, Switzerland
Fons Rademakers received his Ph.D. in particle physics from the Univ. of Amsterdam in 1991 for his work on event displays and data analysis for the DELPHI experiment at CERN’s LEP collider. Since then he has worked at CERN and been involved in designing and developing data analysis programs. In 1991 he joined the PAW project where he developed the column wise-ntuples (a column-oriented storage system) and PIAF, a parallel data analysis system, which was sold to Hewlett-Packard for 1M$. In 1995, while working as Linux evangelist for HP at CERN, he started with Rene Brun the ROOT project and has been involved in all aspects of the system since then. In 2001 Fons joined the ALICE collaboration and has worked as software architect on the initial version of the AliRoot framework. In recent years his special attention has gone to high performance parallel computing using PROOF. Fons took over from Rene Brun as ROOT project leader in 2011. ROOT is used in all particle physics labs in the world and was the main statistical tool used for the discovery of the Higgs boson. Just after the release of the all new ROOT 6, Fons joined CERN openlab in June 2014 as CERN openlab CTO, taking over from Sverre Jarp.
Robert John Ross, PhD, Senior Lecturer in the School of Computing DIT, Ireland
Dr. Robert Ross is a Senior Lecturer in the School of Computing DIT and a Funded Investigator in the ADAPT Research Centre and a PI in the EI Funded CeADAR Centre for Applied Data Analytics. Robert studied for Bachelors and Masters Degrees in University College Dublin, before taking a PhD in the University in Bremen Germany on Situated Dialogue Systems.
Robert’s research interests are primarily in the areas of Dialogue Systems and more recently in the application of machine learning and deep learning techniques to text, sensor data streams and video.
Robert teaches modules on Deep Learning and Advanced Topics in Computational Intelligence. Robert has also worked on a wide range on collaborative industry projects in the past five years. While these projects vary in domain, at their core they are all focused on the practical application of Machine Learning and Data Analytics to real world industry problems.
Marko Robnik-Šikonja, PhD, Associate Professor, University of Ljubljana, Faculty of Computer and Information Science
He received his Ph.D. in Computer Science and Informatics from University of Ljubljana, Faculty of Computer and Information Sciences in 2001 (He graduated in 1993 and finished his M.Sc. in 1997 at the same institution). His research interests include machine learning, data mining, intelligent data analysis, cognitive modeling, artificial intelligence, and their applications. In machine learning and data mining he is interested in ensemble learning, feature evaluation, probability prediction, cost-sensitive learning, feature subset selection, regression, natural language processing, and constructive induction (recent papers, free software). He is a (co)author of approximately 50 publications. His papers have more than 2000 citations according to Scholar.
Vincent Lonij, PhD, Research Scientist, Cognitive Datascience, IBM Research – Dublin, Ireland
Vincent is a researcher at IBM Research Ireland. His work aims to push the boundaries of what is possible with AI. His recent work focusses on using deep learning for visual recognition and scene understanding in open-world setting.
He also specializes in applying machine learning models to physical systems including renewable energy, transportation, and atmospheric dynamics. He has worked on multiple client engagements including an grid-forecasting project in Vermont, recently nominated for the Royal Irish Academy AmCham innovation award.
Vincent obtained his PhD in physics and finance from the University of Arizona in 2011. He has conducted research at Princeton University in NJ, CNRS in France, and Leiden University in the Netherlands.