Artificial Intelligence

Artificial intelligence (AI), one of the most important aspects of our future digital age, is currently experiencing a major boom in science, the economy and the media and has already become an everyday technology. Thanks to SIRI we can now speak to our smartphones, the first self-driving cars have started to appear on the roads of California and logistics providers are already testing autonomous flying drones. However, if machines are to be used safely in factories, hospitals and households they must – just like us humans – be able to act, react and learn by observation and experience and not simply operate in accordance with pre-programmed data.
 

Hybrid research methods:

Combining knowledge-driven and data-driven approaches to close the »semantic gap«

»Multimedia Pattern Recognition« and »Data Science« are the Fraunhofer IAIS’s core areas of research and most important areas of expertise in relation to artificial intelligence and its applications in robotics, image and speech processing and process optimization. Central to the research being done at the Fraunhofer IAIS are hybrid artificial intelligence solutions: This is where we combine the knowledge based research methods of professors Dr. Sören Auer and Dr. Jens Lehmann with the data-driven methods by professors Dr. Stefan Wrobel and Dr. Christian Bauckhage.

The knowledge-based approach is founded on people’s predetermined empirical knowledge and the conclusions drawn from it – and has recently been encapsulated in the phrase »Semantic Web«. The data-based approach on the other hand uses »Machine Learning« methods to analyze statistical correlations. The aim of combining both approaches is to close the »semantic gap«. This is where intuitive empirical knowledge and statistical knowledge meet and need to be put into context if they are to replicate the human ability to understand meaning from a given context.

 

Leading research work about theory and ethics of artificial intelligence

 

Our research contributes significantly to the theory and ethics of artificial intelligence and machine learning whilst still being closely guided by the practical needs of the economy and our customers in industry. For many years the Fraunhofer IAIS has been one of the leading European centers for research and development in machine learning with responsibility for such ground-breaking developments as »Echo State Networks« and »Graph Cores«. Machine learning algorithms and systems and solutions developed by the Fraunhofer IAIS are now being successfully applied in many commercial areas including advertising, the automotive industry and the financial sector.

Selected publications on artificial intelligence

  • Christian Bauckhage, Kristian Kersting, Fabian Hadiji: Parameterizing the Distance Distribution of Undirected Networks. UAI 2015
  • Fabian Hadiji, Martin Mladenov, Christian Bauckhage, Kristian Kersting: Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation. IJCAI 2015
  • Christian Bauckhage, Kristian Kersting: Can Computers Learn from the Aesthetic Wisdom of the Crowd? KI 27(1), 2013
  • Christian Bauckhage, Kristian Kersting: Data Mining and Pattern Recognition in Agriculture. KI 27(4), 2013
     

Selected publications on machine learning

  • M. Neumann, R. Garnett, C. Bauckhage, K. Kersting, „Propagation Kernels: Efficient Graph Kernels from Propagated Information“, Machine Learning, 102(2), 2016
  • T. von Landesberger, F. Brodkorb, P. Roskosch, N. Andrienko, G. Andrienko, A. Kerren, „Mobility Graphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering “, IEEE Transactions on Visualization and Computer Graphics, 22(1), 2016
  • J.E. Labra Gayo, D. Kontokostas, S. Auer, „Multilingual linked data patterns “, Semantic Web, 6(4), 2015
  • C. Bauckhage, A. Drachen, R. Sifa, „Clustering Game Behavior Data “, IEEE Transactions on Computational Intelligence and AI in Games, 7(3), 2015
  •  D. Oglic, D. Paurat, T. Gärtner, „Interactive Knowledge-based Kernel PCA “, Machine Learning and Knowledge Discovery in Databases, Springer, 2015.