Keynote2: Ulrich Klauck

Prof. Ulrich Klauck holds a diploma degree in Medical Computer Science from Heidelberg University and Heilbronn University of Applied Sciences (1985) and a doctoral degree (Dr. sc. hum.) from Heidelberg University (1991). Before joining Aalen University of Applied Sciences he worked as a scientist for Bruker Analytical Instruments (Karlsruhe, Germany) in the area of pattern recognition and analysis of MR spectra.

He started his academic career at Aalen University in the Faculty of Electronics. From 1996 to 2001 he was vice rector for research and international affairs. In 2000 he founded the Department of Computer Science and was head of the department from 2000 to 2006 and from 2010 to 2014. From 2002 to 2006 he was member of the academic senate, since 2006 he is member and vice director of the board of directors.

Prof. Klauck is teaching in image processing, pattern recognition, autonomous systems and machine learning. He was leading several research projects and industrial projects in the area of image processing and pattern recognition. He is also head of a small company for technology transfer (STZBAI, Steinbeis Transferzentrum). He is currently a visiting professor, Central University of Technology Free State

Topic: Machine Learning – Chances and Challenges

In recent studies, Machine Learning is regarded to be one of the most disruptive technologies that will transform our future lives, businesses and the global economy. In their 2013 study, McKinsey identified 12 technology areas with the potential of a high impact on how people live and work and on industries and economies[1]. In most of these areas, Machine Learning is a key enabling technology.

Machine Learning is learning from data rather than programming hard coded decision rules. Taking this short definition alone, it highlights the central role of Machine Learning nowadays. The worldwide process of digitization produces data in all areas as e.g. production processes, Internet of Things, health care and even our daily life.

In this presentation, Machine Learning is defined a bit more precise. Going through the development of this rapidly emerging field, the different types of Machine Learning are explained and examples from different application areas are given. It will be shown, that computers are able (or will be able) to solve problems that were supposed to be dependent on human expertise in the past. Among many other benefits this can be economically advantageous in many areas. This will lead to a broad dissemination of Machine Learning applications.

The downside of this development is the fact that the future of our life and in particular of our work life will change dramatically. Some jobs – and in particular those requiring a low level of education and a high level of automation – are likely to disappear and on the other hand new job opportunities will open. The responsibility of our society, economy, politics and educational institutions will also be discussed shortly.

[1] Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global Institute, May 2013.