TU Wien Informatics

New Center for Artificial Intelligence and Machine Learning (CAIML)

  • By Stefan Woltran
  • 2021-12-02
  • Research
  • Machine Learning
  • AI

Opening of the new TU Wien Center for Artificial Intelligence and Machine Learning with a keynote by Turing Award winner Leslie Valiant.

Leslie Valiant
Leslie Valiant

The newly founded “Center for Artificial Intelligence and Machine Learning” (CAIML) has the aim to bundle and strengthen research activities in Artificial Intelligence and Machine Learning both in their foundations as well as in applications and to establish TU Wien as a center of excellence for Artificial Intelligence and Machine Learning. The center consists of three thematic pillars: (1) methods of symbolic AI, (2) methods of machine learning, and (3) explainable AI and AI aspects in the context of digital humanism. The CAIML board comprises twelve internationally renowned researchers in the faculties of Informatics and of Mathematics and Geoinformation of TU Wien.

The opening of CAIML takes place on Thu, December 2, 2021 at 16:15 CET (15:15 UTC). Due to stricter COVID-19 rules, this is now an online-only event. Please join us via Zoom (Meeting ID: 998 0978 1847) or on the DIGHUM YouTube channel.

Program

16:15 — Welcome & Introduction

  • Sabine Seidler, Rector of TU Wien
  • Gerti Kappel, Dean of the Faculty of Informatics
  • Wolfgang Wagner, Dean of the Faculty of Mathematics and Geoinformation

16:30 — Keynote Talk

How to Augment Supervised Learning with Reasoning

By Leslie Valiant, Harvard University, USA

Supervised learning is a cognitive phenomenon that has proved amenable both to theoretical analysis and exploitation as a technology. However, not all of cognition can be accounted for directly by supervised learning. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the major component of cognition that needs to be added. We suggest that the central challenge therefore is to unify the formulation of these two phenomena, learning and reasoning, into a single framework with a common semantics. In such a framework one would aim to learn rules with the same success that predicates can be learned by means of machine learning, and, at the same time, to reason with the rules with guarantees analogous to those of standard logic. We discuss how Robust Logic fulfills the role of such a theoretical framework. We also discuss the challenges of testing this experimentally on a significant scale, for tasks where one hopes to exceed the performance offered by learning alone.

About Leslie Valiant

Leslie Valiant was educated at King’s College, Cambridge; Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His work has ranged over several areas of theoretical computer science, particularly complexity theory, learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence and is the author of two books, Circuits of the Mind, and Probably Approximately Correct. He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, the European Association for Theoretical Computer Science EATCS Award in 2008, and the 2010 A. M. Turing Award. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).

17:50 — Presentation of CAIML

18:00 — Panel Discussion

AI Between Scientific Research And Societal Impact

  • Gerhard Friedrich, University of Klagenfurt, Austria
  • James Larus, EPFL, Switzerland
  • Helga Nowotny, Former President of the European Research Council
  • Paul Timmers, University of Oxford, UK and European University Cyprus

19:00 — Closing Remarks

Notes

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