Progress in autonomous vehicles and digital assistants shows that today’s machines can perform certain human tasks with remarkable accuracy. This has led to a gold rush mentality around AI, yet a critical assessment suggests that current technologies still lack versatility and only work within limited domains for which large sets of training data are available. Transfer of knowledge, common sense, and an understanding of causality are open problems. These limitations are related to how we perform machine learning, the technology powering AI: existing methods build on statistics, but one can also try to go beyond this, assaying causal structures underlying statistical dependences. Can causal knowledge help machine learning tasks, by being more robust to changes that occur in real world datasets?
This is but one example of the open issues in current AI research, and with the constant stream of talented young scientists flocking into machine learning, significant developments are to be expected. We will discuss how Europe can partake in these developments by playing an active role in public AI research. AI technologies have the potential to improve our lives. The development is still in its infancy, and we should ensure that the highest level of research in this field will continue to be performed in the open societies of Europe.
Bernhard Schölkopf's scientific interests are in machine learning and causal inference. He has applied his methods to a number of different fields, ranging from biomedical problems to computational photography and astronomy. Bernhard has researched at AT&T Bell Labs, at GMD FIRST, Berlin, and at Microsoft Research Cambridge, UK, before becoming a Max Planck director in 2001. He is a member of the German Academy of Sciences (Leopoldina), has received the J.K. Aggarwal Prize of the International Association for Pattern Recognition, the Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, the Royal Society Milner Award, and is an Amazon Distinguished Scholar.
Bernhard co-founded the series of Machine Learning Summer Schools, and currently acts as co-editor-in-chief for the Journal of Machine Learning Research, an early development in open access and today the field's flagship journal.
Thursday, June 6, 2019, 6 – 7 pm
Raiffeisen Lecture Hall, IST Austria, Klosterneuburg
This lecture is jointly organized by the Austrian Academy of Sciences (ÖAW) and IST Austria.
Please register below by May 28.
Free shuttle buses are provided to/from campus:
The regular IST shuttle bus #142 will depart from U4 Heiligenstadt/public bus stop at 5:03 pm and 5:33 pm. An extra IST shuttle bus has been organized and will depart from Schwedenplatz/night bus stop at 5 pm.
The IST shuttle bus #142 returning to Vienna will depart at 7:40 pm. An extra shuttle returning to Schwedenplatz will depart at 8 pm.