Intelligent Machines Symposium

MACHINE LEARNING IN RESEARCH AND APPLICATIONS

CALL FOR POSTERS AND DEMONSTRATIONS

INTELLIGENT MACHINES SYMPOSIUM

17 NOVEMBER 2010

“DE VEREENIGING” IN NIJMEGEN Organisation

Prof. dr. H.J. Kappen | Radboud Universiteit Nijmegen / SNN A. Wanders | SNN SNN

SNN is a non-for-profit organization that aims to stimulate research and applications on machine learning, Bayesian inference and neural networks in the Netherlands. SNN facilitates a network of researchers, organizations and companies in this field. Current participants are:

Prof. Dr. H.J. Kappen, dr. W. Wiegerinck | Radboud Universiteit Nijmegen / SNN

Prof. Dr. T. Heskes | Radboud Universiteit Nijmegen

Prof. Dr. H.J. van den Herik | Tilburg center for Cognition and Communication

Prof. Dr. E. Postma | Tilburg center for Cognition and Communication

Prof. Dr. A. Van den Bosch | Tilburg center for Cognition and Communication

Prof. Dr. J.N. Kok | Universiteit Leiden

Prof. P. Grünwald | Universiteit Leiden

Prof. Dr. R. Gill | Universiteit Leiden

Prof. Dr.L. Schomaker, dr. M. Wiering | Rijksuniversiteit Groningen

Prof. Dr. G. Weiss, dr. K. Tuyls | Universiteit Maastricht

Prof. Dr. F. Groen, dr. B. Krose, dr. S. Whiteson | Universiteit van Amsterdam

Prof. Dr. H. La Poutre, dr. S. Bohte | Centrum voor Wiskunde en Informatica

Dr. K. Nieuwenhuis, dr. G. Pavlin | Thales Research

Prof. Dr. M. Reijnders, dr. B. Duin, dr. M. Loog | Technische Universiteit Delft

Prof. Dr. A. van der Vaart | Vrije Universiteit Amsterdam Intelligent Machines

Can machines think? This has been a conundrum for philosophers for years, but the answer to this question also has real social importance. Modern robots can assist us in our homes and have human-like qualities. The internet provides us with personalized tools that learn from our behavior. It is therefore of more than academic importance that we learn to think clearly about the actual cognitive powers of computers, and what we can expect of them in the future.

On November 17, SNN organizes a one-day symposium in Nijmegen, entitled "Intelligent Machines". During the day, invited research leaders in the field of machine learning and robotics will present examples of intelligence in computers and will discuss their views for the future.

In addition, posters will provide a comprehensive overview of the research activities on machine learning in the Netherlands. Based on our experience with similar events in the past, we expect approximately 40 poster presentations. There is also an opportunity for companies to present their research and products.

The purpose of this meeting is to provide a platform for discussion and interaction between the academic and industrial research and development communities in the Netherlands.

For Whom

The symposium is designed to address a broad audience. The plenary talks aim at anyone who is interested in how intelligent computers may affect our society, now and in the future. The poster sessions present more technical results and aim provide concrete examples of how machine learning research is used in numerous applications.

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What is machine learning?

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn behaviors based on empirical data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too complex to describe generally in programming languages, so that in effect programs must automatically describe programs. Machine learning methods are at the basis of many applications, ranging from vision to language processing, forecasting, pattern recognition, games, data mining, expert systems and robotics. The modern field of machine learning integrates many distinct approaches such as probability theory, logic, combinatoric optimization, search, statistics, reinforcement learning and control theory.

Where and When

The meeting will take place on Wednesday the 17 th of November 2010 from 9:30 a.m. until 6:00 p.m. in the Concert building “De Vereeniging” in Nijmegen. This symposium has no registration or attendance costs and includes a free lunch.

There are some parkingplaces available.

It’s about 5 minutes walking from Central Train Station Nijmegen. 3

Program

09:30 Registration 09:50 Opening Prof. Bert Kappen | Radboud University Nijmegen, SNN

10:00 Tutorial: Bridging the gap between machines and people I Prof. Nicholas Roy | Massachusetts Inst. of Technology | Cambridge | USA

10:45 Coffee

11:00 Tutorial: Bridging the gap between machines and people II Prof. Nicholas Roy | Massachusetts Inst. of Technology | Cambridge | USA

11:45 Lunch and Posters There will be a lunch buffet held across the different meeting rooms. During lunch, stands and poster presentations can be mounted for display.

14:30 Poster session

15:45 What is Intelligence? Prof. Nello Cristianini | University of Bristol | Bristol | UK 16:30 Panel discussion moderated by Bas Haring

17.00 Drinks and Posters

18:00 Closing 10:00

Machine Learning and Robotics: Bridging the gap between machines and people Nicholas Roy from Massachusetts Institute of Technology

In the last few years, how robots operate in the world has advanced considerably. Examples include the autonomous vehicles in the DARPA Grand Challenges and Urban Challenge, the considerable work in robot mapping, and the growing interest in home and service robots. However, these example technologies and systems are still mostly restricted to research prototypes. One obstacle to getting more widely useful robots is that the way robots reason about their world is still pretty different to how people reason. Robots think in terms of point features, dense occupancy grids and action cost maps. People think in terms of landmarks, segmented objects and tasks (among other representations). There are good reasons why these are different, and robots are unlikely to ever reason about the world in the same way that people do. But, there has been recent work in bridging the gap between low-level geometry and control, and higher-level semantic representations.

Nicholas Roy is an Associate Professor in the Department of Aeronautics & Astronautics at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He received his Ph.D. in Robotics from Carnegie Mellon University in 2003. His research interests include autonomous systems, micro air vehicles, mobile robotics, human-computer interaction, decision- making under uncertainty and machine learning.

11:45 Lunch and Posters

There will be a lunch buffet held across the different meeting rooms. During lunch, stands and poster presentations can be mounted for display.

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13:00

Machine Learning at Yahoo! Kishore Papineni, Yahoo! Research New York

As a large internet portal with diverse audience, Yahoo! is constantly faced with choosing the most appropriate object (a news story, a set of urls, an advertise- ment) for a specific user in context. It must choose the object(s) out of a large number of choices (millions to billions) in a short time (20-50 milliseconds). Extreme personalization means modeling the user’s needs at that moment well. Serving systems must adapt to changes in the environment — e.g shifts in advertising marketplace, drifts in user’s needs and tastes, hot news stories becoming cold, etc. The underlying learning machinery must sometimes learn in an adversarial setting (eg email spam detection). While the training data is web- scale that grows terabytes a day, it is still sparse because only a tiny fraction of all possible combinations of webpages, users, and advertisements will be seen in the historical data. A rich combination of mathematics, statistics, computer science, and economics forms the basis of many serving systems at Yahoo. This talk gives an overview of some of the learning/optimization challenges and examines one aspect in depth.

Kishore Papineni graduated with a PhD in Electrical Engineering specializing in feedback control theory from Rice University in 1995. From 1995 to 2006, he was a Research Staff Member at the IBM T.J. Watson Research Center, Yorktown Heights, New York. During this period, he worked on various natural language processing technologies such as natural language understanding, dialog management and statistical machine translation, managing IBM's SMT department from 2001-2006. He was a founding Editor-in-Chief of ACM Transactions on Speech and Language Processing from 2003-2007. In 2006, he joined Yahoo! Research where he is the head of Machine Learning. His interests include optimization, estimation, control, and computational advertising. 6

13:45

Learning to get smart –how to cope with the challenge of interaction within a natural environment Edgar Körner Honda Research Institute Europe GmbH

Progress in machine learning has enabled technical artifacts to perform quite well for well specified tasks for which the necessary knowledge can be either provided by preprogramming a rule base or supervised learning. How far we are still apart from the smart behavior of living creatures becomes apparent when dealing with interaction in an ever changing and uncertain natural environment. It is not learning algorithms per se, but how to organize processing and learning at a systems level is the essential problem to be solved for coping with the tremendous complexity and dynamics of environmental situations.

We argue that the control to detect, localize and internally preserve behaviorally relevant situations for learning during behavior and from few experiences–or even one–defines the degree of intelligent behavior a system can develop. Hence, understanding the control architecture for the organization of autonomous incremental learning and utilization of the acquired information to configure the processing architecture on the fly may be the key to achieve autonomous interaction comparable to living beings.

The talk outlines first steps towards semi-autonomous learning with examples from visual category learning, learning of association of multi-sensory events with behavior, and learning to imitate observed behavior on a humanoid platform.

Edgar Körner received his Dr.-Ing. in biomedical cybernetics in 1977 and the Dr. Sci. in biocybernetics in 1984, both from Ilmenau Institute of Technology. In 1988, Dr. Körner was appointed full professor for biocybernetics and head of the Department of Neurocomputing and Cognitive Systems at the Technical University Ilmenau. In 1992 he moved to Japan to join Honda R&D’s Fundamental Research Center, focusing as a chief scientist on the brain-like intelligence research. In 1997 he started research in computational neuroscience, evolutionary technology, and 7 cognitive robotics at Honda R&D Europe, where he served as an executive vice president and head of the Future Technology Research Divison. From 2003 to 2010, Dr. Körner served as the president, and since April 2010 as the chairman of the Honda Research Institute Europe GmbH. Since October 2007, he additionally serves as a co-director of the Research Institute for Cognition and Robotics at the University Bielefeld.

14:30 Postersession

15:30

What is intelligence? Nello Cristianini, University of Bristol, UK

While the question in the title has remained unanswered for thousands of years, it is perhaps easier to address the apparently similar question: "What is intelligence for?" We take a pragmatic approach to intelligent behavior, and we examine systems that can pursue goals in their environment, using information gathered from it in order to make useful decisions, autonomously and robustly. We review the fundamental aspects of their behavior, methods to model it and architectures to realize it. The discussion will cover both natural and artificial systems, ranging from single cells to software agents.

Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol since March 2006, and a holder of the Royal Society Wolfson Merit Award. He has wide research interests in the area of computational pattern analysis and its application to problems ranging from genomics, to computational linguistics and artificial intelligence systems. He has contributed extensively to the field of kernel methods. He has a PhD from the University of Bristol, a MSc from Royal Holloway, University of London, and a Degree in Physics from University of Trieste. Since 2001 has been Action Editor of the Journal of Machine Learning Research (JMLR), and since 2005 also Associate Editor of the Journal of Artificial Intelligence Research (JAIR). He is co-author of the books 'An Introduction to Support Vector Machines' and 'Kernel Methods for Pattern Analysis' with John Shawe-Taylor, and "Introduction to Computational Genomics" with Matt Hahn (all published by Cambridge University Press).

16:30 Panel discussion moderated by Bas Haring

17:00 Drinks and Posters

18:00 Closing

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Posters

Those who wish to present a poster at the symposium should submit an abstract before 15 October 2010. The abstract should clearly state the problem and the results and should use the following format:

You are encouraged to use at least one and maximum two illustrations. The length of the abstract is restricted to 400 words or 1 A4 page. The abstract should be uploaded as a PDF file on the symposium website. The abstracts will be judged by the organizing committee for quality and relevance. The authors will be notified by November 1st 2010. The accepted abstracts will be presented on the symposium website.

Stands and company presentations

Companies who want to present their products / services which are relevant for this symposium are invited to contact the organizing committee before the 1st of October, 2010.

Time schedule

Deadline Poster abstracts submission 15 October 2010 Notification of acceptance posters 1 November Deadline registration stands /demo 1 October 2010 Deadline registration 1 November Symposium Machine Learning 17 November Contactaddress