Artificial intelligence (AI) offers a lot of potential for Industrie 4.0, but there are technological barriers that make it more difficult to implement. Researchers at Fraunhofer IKS are developing a framework to support and optimize the data and AI life cycle. This will significantly increase the scope of value creation provided by AI.

Can deep neural networks in image processing reliably recognize new, unknown test patterns? First considerations for different methods — especially for safety-critical applications.

The international conference Safetronic holistically addresses the safety of road vehicles. Abstracts for conference papers may be submitted until May 9, 2022.

Theresa Wirth translates medical problems into the language of computer science to find AI-based solutions. In part 16 of our series of employee portraits, she describes her motivations and how she found her way to applied research.

Deep learning has boosted the interest in deploying artificial intelligence (AI) to more systems. Reinforcement learning (RL) has also been through a revival, with Deep RL models designed to play Atari games and even beat grandmasters at the game of Go. However, despite these recently obtained impressive achievements, RL is still struggling to be accepted and successfully deployed in some applications, especially in the case of safety-critical tasks such as robotics, autonomous driving and industrial control.

At Fraunhofer IKS, people from a wide variety of countries work on solving complex tasks: Currently we have colleagues from more than 20 different nations. We asked three of them about their experiences of living and working in Germany.

The “QuaST” (quantum-enabling services and tools for industrial applications) project, led by Fraunhofer IKS, is now underway. Jeanette Lorenz, Senior Scientist at the Quantum Computing Department of Fraunhofer IKS, heads the project. In this interview, she explains what QuaST is all about.

As farmers step up their use of information and communication technology, agriculture is not only becoming more efficient, but also more sustainable and resilient. A Fraunhofer IKS webinar explored some different solutions and strategies in the field of smart farming.

In the near future, medical engineering will be shaped by artificial intelligence and machine learning. However, the complex processes involved in machine learning create a problem for researchers — it is costly and challenging to eliminate errors before autonomous systems are put into use, and to reliably detect errors during operation.

The Fraunhofer IKS Safe Intelligence blog starts the new year with a big change: From now on, there is also an English version of the blog.