Fraunhofer Institute for Cognitive Systems IKS

This article highlights why it is so important to understand that safe AI has less to do with AI itself and much more to do with safety engineering.

A solution from Fraunhofer IKS is helping to uncover and predict difficult and unknown situations for autonomous vehicles:

How can we design conntected autonomous systems to simultaneously provide resilience, intelligence and flexibility?

Together with Continental, Fraunhofer IKS was able to create a concept for the dynamic distribution of vehicle functions and develop a technical safety concept that describes an implementation of the identified safety requirements.

Dr.-Ing. Henning Schepker will present his paper on software defined radios at the IEEE VTC-Fall 2020.

Together with Airbus, Fraunhofer IKS is testing an ear sensor for noncontact health monitoring of infectious patient groups to ensure that it is safe enough for use in hospitals.

Addressing the 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.

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.

The future will rely on widespread, massively-connected, highly-intelligent systems. To implement this vision, we need dependable cloud-based cyber-physical systems. Research in this direction encounters as many opportunities as challenges. Such systems exist in changing environments and interact with other systems and humans, which requires intelligence, autonomy, safety and adaptability – in fact, conflicting goals. An example of this is autonomous driving.

Machine learning means a disruptive challenge for safety assurance. It’s therefore no surprise that AI safety has gained much more attention over the past few months. This article focuses on the basic understanding of what safety actually means. It highlights why it is so important to understand that safe AI has less to do with AI itself and much more to do with safety engineering.