Safe intelligence is the central research topic of the Fraunhofer Institute for Cognitive Systems IKS. This topic is very important from a technology perspective as well as with regard to the economy and society. The two Chairs of the Advisory Board for the IKS development project see it the same way — each from their own perspective.

Fraunhofer IKS has established an advisory board to provide expert support to the IKS development project. The advisory board convened for its first regular meeting on April 14. The two Chairs, Thomas Gallner and Prof. Thomas Seidl, talk about their motivation for getting involved. And about the aspects on which they intend to focus.

Artificial intelligence (AI) has to be able to handle uncertainty before we can trust it to deliver in safety-critical use cases, for example, autonomous cars. The Fraunhofer Institute for Cognitive Systems IKS is investigating ways to help AI reason with uncertainty, one being the operational design domain.

The Fraunhofer Institute for Cognitive Systems IKS is now over a year old. To ensure its second year gets off to a good start, it has received further funding from the Bavarian Ministry of Economic Affairs to the tune of 20 million euros, which will be used to speed up the process of expanding into new research areas.

Cyber-physical systems (CPS) integrate software algorithms and physical structures. To exceed the simple embedded systems of today, advances in CPS require several properties, particularly adaptability, resilience, and safety. Lean management of CPS with resilient architectures satisfies these requirements by combining data distribution with service-oriented structures. A practical use case: a moving robot in a warehouse.

Reconciling safety, performance and reliability in cloud-based cognitive systems is no easy task. Especially in safety-critical autonomous systems such as driverless cars, expectations rightly assume that all three requirements are implemented at the highest level. However, this comes up against limited resources. The Fraunhofer IKS has developed an approach for optimising safety, performance and reliability depending on the current situation and concrete applications: flexilience.

Deep Neural Networks (DNNs) have proven their potential. This applies above all to their use in the laboratory. Their performance in real-world applications leaves a lot to be desired. One major problem is the estimation of uncertainty. A closer look at methods that meet this challenge.

Flexible service-oriented architectures (SOAs) in safety-critical applications have been virtually out of the question until now. This is because services need to be reliably available and safety requirements need to be met at all times. Fraunhofer IKS has developed resilientsoa, a software framework that meets these needs by ensuring that service orchestration is as safe as necessary while performing as well as possible.

Addressing the wide range of challenges in dependable cloud-based cyber-physical systems of systems (CPSoS) requires new approaches that are automated and efficient enough to shift some of the system’s elements to runtime. To design and evaluate the system architecture, Fraunhofer IKS has developed an iterative and automated process.

When a system malfunctions or a required service is not provisioned in a timely manner, this can lead to human injuries or fatalities. These situations are considered safety-critical, e.g. in automated driving and human robot interaction. For this reason, the systems that are involved must exhibit a high degree of dependability. To determine a system’s dependability, scientists at Fraunhofer IKS are developing an adaptive, dynamic and flexible safety analysis process that overcomes the limitations of current methods.