Cutting-edge research in Machine Learning

The Fraunhofer CCIT Technology Hub Machine Learning develops new Informed Machine Learning solutions for intelligent systems. Our research activities focus on three main areas: Hybrid Learning, Simulation-based Learning, and Resource Aware Learning. With our basic and applied research, we improve the quality of products and optimize business and industrial processes. Our projects lay the foundations for innovative products, services, and forward-looking business models.

Do you have any questions concerning our research topics or are you interested in a collaboration? We are happy to hear from you.

What special role do the topics Machine Learning and Artificial Intelligence play in the cognitive internet?

Today, Artificial Intelligence and cognitive systems must be so efficient that they are no longer programmable but have to learn from data. With Machine Learning, we use the data and knowledge available for flexible and self-improving, intelligent systems.

What is the position of industry in this field, and how can Fraunhofer support enterprises?

The importance of Machine Learning is clearly recognized in the economy, but in many sectors on the one hand the appropriate data, and on the other hand the necessary specialists are lacking. We help enterprises assemble and create the right combinations of data, processes and business models. In our data science instruction courses at Fraunhofer, on site at the company or in interactive-online courses, we train the next generation of data scientists.

What are your long-term goals with the Technology Hub Machine Learning and its implications for the topic of »Cognitive Internet Technologies«?

With the Technology Hub Machine Learning, we want to establish »Informed Machine Learning« on a broad footing, by which we mean approaches that not only learn from data but can also utilize existing expert knowledge and models, as are often available in the economy, to improve performance. At the same time, the transparency and reliability of the results are assured, thereby bringing about the preconditions for trustworthy cognitive internet technologies.