Over the past 30 years, artificial intelligence (AI) has evolved from an exotic research topic to a driving force behind the development of IT applications. In the early days of AI, the focus was on solving combinatorial and logical problems. A classic example is computer chess, the mechanisation of which has been studied since the 1940s.

Rapid advances in computing speed finally made it possible to solve such combinatorial problems better and faster than humans – the world champion in computer chess was defeated by a computer in 1997. Since then, research has focused on machine learning. Instead of laboriously programming solution algorithms, the aim is to enable computers to discover the solution strategy for a wide variety of problems from large amounts of data on their own. For this reason, today’s AI goes hand in hand with strategies for storing and quickly making available huge amounts of data.

Today, this is known as big data, i.e. data that is no longer collected locally, but in the cloud around the world. Deep learning is therefore much more than the use of multi-layer neural networks. Deep learning requires the combination of all these factors in adaptive systems that can understand and translate language or even drive autonomous vehicles.

The Research Center for Artificial Intelligence will take up this challenge and work on the following topics:

• Machine learning with classical statistical and modern deep learning methods
• Investigating systems that process different forms of logic for tasks such as system verification, theorem proving and explanatory components of AI systems
• Combination of sub-symbolic and symbolic AI systems
• Numerical algorithms for machine learning based on massively parallel computing
• Strategies for interaction between AI algorithms in the cloud and local algorithms (edge computing)
• AI applications: language understanding, language translation, bioinformatics, robotics, pattern recognition, etc.
• Explainability of AI systems and ethical issues

The Research Center for Artificial Intelligence will work in a highly interdisciplinary way, involving colleagues from different disciplines (databases, distributed systems, statistics, logic, philosophy). This can be organized through collaboration with local universities as part of the funded projects, but also through direct collaboration with industry via external PhD students. The external PhD students can be located anywhere in the world. They will be in regular contact with their supervisors and will attend a weekly research seminar where Masters and PhD students will interact.

Topics for Master’s theses and doctoral dissertations will be derived from the focus areas and projects being worked on. This will enable the Research Center for Artificial Intelligence to establish a unique research network with global reach in this dynamic field of research.

Research Groups:

• Statistical and Symbolic Artificial Intelligence
• Digital Education and Internet Technologies
• Operations Research
• Industrial AI