DEEP is a machine learning toolchain by Fraunhofer IKS for the reliable AI-assisted automation of quality inspection systems. For this purpose, DEEP automates various specific Fraunhofer IKS technologies. This part of the series of blog posts focuses on the specific technological contents of modular concept learning.

Faced with cost pressures and a shortage of healthcare professionals, organizations are challenged to increase efficiency. The integration of artificial intelligence (AI) into workforce management offers promising approaches. In a joint project, Fraunhofer IKS and ATOSS Software have developed an AI-controlled shift planning agent that automates staff scheduling while demonstrating remarkable scalability.

What are the key ideas shaping the current automation landscape? Read on to learn about three insights that emerged from discussions between participants at Fraunhofer IKS’ Safe Intelligence Day 2025.

As the railway industry advances, integrating Artificial Intelligence (AI) systems has become essential to improve efficiency and enhance the attractiveness of rail transport. However, implementing AI in railway operations comes with several challenges. Ensuring safety is the top priority while simultaneously increasing throughput.

The United Nations has declared 2025 to be the International Year of Quantum Science and Technology. This is to recall and celebrate the fact that 100 years have now elapsed since the beginnings of quantum mechanics. It is an occasion for a critical review and an optimistic preview of quantum computing technology, including from the point of view of business.

DEEP, the machine learning toolchain of Fraunhofer IKS, enables the automation of quality control in production environments, even if the requirements are complex. The technologies FAST and Robuscope, likewise developed by Fraunhofer IKS, are essential components of this solution.

Quality assurance is one of the, if not the most crucial competitive factors for industrial companies. German companies have long since recognized this and generally do not have to contend with quality issues. But that comes at a cost. Artificial intelligence is now believed to become a game changer in quality assurance. Dr. Niels Syassen, Executive Board member with responsibility for SICK AG’s Technology & Digitalization portfolio, explains how this will work.

There are so many myths about autonomous driving and safety in the public debate. We're going to look at four of them here from a US perspective.

Machine learning (ML) is considered a promising technology for the automation of quality control in production environments, even if the requirements are complex. However, ML approaches require sufficiently large datasets for training of the system, which are often unavailable. The ML toolchain DEEP (Date Efficient Evaluation Platform) from Fraunhofer IKS addresses this challenge.

Autonomous driving can work well in precisely defined areas of use. Yet if driverless cars are let loose into the free-for-all of everyday road traffic unprepared, difficulties can crop up that these vehicles are not prepared to cope with. Three examples.