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Neural networks are not robust. The development of reliable predictors requires systematic methods to both assess their quality and to gain confidence in their correctness. Quantum computing can help here.

It takes time. And it will take time. But it will come: Fraunhofer IKS is making a decisive research contribution to autonomous driving, explains Dr. Reinhard Stolle, the new head of the Mobility business unit and deputy director of the institute. But there is still a lot to be done.

Regulations and standards for trustworthy AI are in place, and high-risk medical AI systems will be up for audits soon. But how exactly can we translate those high-level rules into technical measures for validating actual code and algorithms? Fraunhofer IKS’s AI verification framework provides a solution.

The new Fraunhofer IKS Safety Companion is an AI agent designed to support human developers with safety-critical tasks. It uses large language models (LLMs) to understand tasks and to identify and implement the appropriate safety concepts. Executive Director Mario Trapp and Florian Geissler, Senior Scientist at Fraunhofer IKS, introduce the Safety Companion in an interview.

As production technologies continue to evolve, it can feel like companies are permanently playing catch-up. Read on to understand the current key changes in industrial automation and why it is worthwhile to embrace them.

Quantum computing represents a frontier in computational technology, offering unparalleled potential for solving complex problems. However, this revolutionary field is not without its challenges. In this blog post, we will explore the vast potential of quantum optimization, identify the key challenges that lie ahead, and discuss strategies for navigating the path forward in this exciting domain.

Artificial Intelligence (AI) in Industry 4.0 still faces technological obstacles that make the use of AI difficult. Fraunhofer IKS is therefore developing a framework that supports and optimizes the data and AI lifecycle - and thus significantly increases the added value of AI in production.

Robots can relieve people of many tasks – but only as long as they work. And as soon as a fault occurs, the time-consuming search for its cause begins. In contrast to conventional software development, robots have even more areas in which bugs i.e., errors could be hidden. Especially once the development has progressed and the interaction between the robot and its environment is considered. A field report from the world of research.

Reinforcement learning is often the most suitable AI solution for a range of applications in the sectors of autonomous systems, healthcare, and communication, due to the training method. Nevertheless, the nature of these tasks makes data collection potentially resource intensive or in some cases even unachievable. This is where reinforcement learning could benefit from embedding quantum computing methods since hybrid quantum-classical reinforcement learning was empirically shown to need less training steps to reach convergence. Fraunhofer IKS is researching solutions that are also suitable for use in industry.

Deep Learning has increased interest in using artificial intelligence in more and more systems. Reinforcement Learning in Machine Learning has also experienced an upswing. However, despite these impressive recent successes, it still faces the challenge of being accepted and successfully deployed. This is especially true for safety-critical tasks such as robotics, autonomous driving, and industrial control. One approach that can help address this problem is Hierarchical Reinforcement Learning.