Michael Schrefl heads the Department of Business Informatics—Data and Knowledge Engineering at Johannes Kepler University of Linz. He has gained substantial experience in the areas of semantic systems and business analytics for over 20 years in Austria and Australia. His research has been funded by the EU (Horizon-2020), FFG, and the Australian Research Council. Partners involved in industry-linked collaborative research projects include Frequentis, SINTEF, and Euro Control. The outcomes of his research have been published in over 150 peer-reviewed conference and journal publications.
Business Analytics goes beyond mere reporting and data analysis. Data drive decisions. Interactive analysis tools and AI techniques are used to gain insight from past business performance and to apply insights when continuously monitoring business operations to pro-actively drive decisions on the management of individual business cases and the re-planning of overall business operations. The talk focuses not on data analysis itself but on the overall analytics process from data collection to interpreting analysis results, exemplifying caveats and pitfalls, based on our own experience and examples from literature.
Sepp Hochreiter is heading the Institute for Machine Learning, the LIT AI Lab and the AUDI.JKU deep learning center at the Johannes Kepler University of Linz and is director of the Institute of Advanced Research in Artificial Intelligence (IARAI). He is regarded as a pioneer of Deep Learning as he discovered the fundamental deep learning problem: deep neural networks are hard to train, because they suffer from the now famous problem of vanishing or exploding gradients. He is best known for inventing the long short-term memory (LSTM) in his diploma thesis 1991 which was later published in 1997. LSTMs have emerged into the best-performing techniques in speech and language processing and are used in Google’s Android, in Apple’s iOS, Google’s translate, Amazon’s Alexa, and Facebook’s translation. Currently, Sepp Hochreiter is advancing the theoretical foundation of Deep Learning, investigates new algorithms for deep learning, and reinforcement learning. His current research projects include Deep Learning for climate change, smart cities, drug design, for text and language analysis, for vision, and in particular for autonomous driving.