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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.

Autonomous systems are increasingly being used in various safety-critical applications, such as autonomous driving. Especially here, the environment and operating conditions are of crucial importance. Machine Learning components (e.g., Deep Neural Network (DNN) serve as enablers and therefore themselves must continue to operate reliably despite changing environmental conditions. Fraunhofer IKS provides a solution that handles such diverse and continuously changing environmental conditions.

Assuming we knew how to properly assess the safety of an autonomous driving application, what would be the consequences? A preview of my presentation at the Safetronic conference (November 15th and 16th 2023).

Holistic safety for road vehicles is the focus of Safetronic conference (15-16 November 2023 in Leinfelden-Echterdingen). In the video interview, Fraunhofer IKS Institute Director Prof. Dr. Mario Trapp and program committee member Hans-Leo Ross from Vay Technologies discuss safety challenges in the automotive sector.

Reinforcement learning (RL) is one of three main machine learning (ML) paradigms. Unlike the other two, supervised learning and unsupervised learning, in reinforcement learning the learning takes place in direct interaction with the surroundings. A brief introduction follows.

AI has paved the way for better and faster diagnostics in the realm of medical imaging. However, challenges specific to the field of medicine, notably the scarcity of data, can impede its performance. This article looks into the enhancement of AI algorithms using quantum technology for the improvement of medical diagnostics, showcasing how quantum computing can tackle existing challenges in AI and offer unique opportunities for healthcare.

A straightforward online tool makes it possible to test the robustness of an AI model – and does so within a matter of seconds. The tool is called Robuscope, and has been developed by the Fraunhofer IKS.

Leaving cooperative, heterogeneous robots alone in unstructured environments isn’t such a good idea. After all, only with an AI-powered management solution will they be able to solve complex tasks without causing any damage. The Fraunhofer IKS is working on developing such a system.

From medical diagnosis to quality control, visual information is often used to make important decisions. Automating these decisions can relieve the burden on experts and avoid errors. This requires complex AI systems whose development involves large amounts of data, and which are generally unable to make reliable predictions. FAST attempts to provide an answer to these problems: a framework for developing reliable AI systems with little data.