Safety is a property of a system and its environment. That's why predicting a system's context is so important to safety engineers. However, we must acknowledge that it is impossible to sufficiently predict and specify a system's context when it must operate outside of a controlled environment. In the previous parts of this series, we've seen how we can use self-adaptivity to build systems for uncertain, dynamic contexts. In this fourth and final part, we add the missing ingredient by taking safety engineering to the next level.

What are the basic principles of self-adaptive systems? This is the basic prerequisite for bringing the concept of resilience to life. Part 3 of our series.

Using artificial intelligence (AI) in the rail industry could potentially increase both efficiency and quality. At a workshop hosted by Fraunhofer IKS, experts from Deutsche Bahn, Siemens Mobility and the institute itself gathered to discuss the possible uses and challenges associated with AI in rail applications, as well as strategies for ensuring system safety.

If uncertainties in an autonomous system are not dealt with, they impair the system’s functionality and can lead to safety risks. In part 1 of our series on uncertainty, we presented a system for classifying uncertainties. In part 2, we will look at specific sources of uncertainty that have been noted in the relevant literature.

Resilience is the key ingredient for the next generation of cognitive cyber-physical systems. After all, many visions of the future can only become reality if the systems become aware of themselves, their goals and their context in order to be able to adapt to unknown and changing environments. And in doing so always optimize utility while preserving safety. But resilience can only be bought with increased complexity. In this article, part 2 of our series on resilience, we look at the character of complexity to better understand where to start engineering in order to manage this complexity.

Cognitive systems are often affected by the problem of uncertainty, particularly if those systems use machine learning to perceive their environments. A system’s sensors are not the only source of uncertainty, however. The life cycle of any highly complex system involves a large number of decisions that are vulnerable to the effects of uncertain knowledge. Part 1 of our series on uncertainty focuses on the latest developments in this technology.

The Operational Design Domain (ODD) precisely defines the area in which automated driving functions may be used. As such, they represent a fundamental prerequisite for safe automated driving. Part 1 of a three-part series on the importance of ODD.

Registration for this year's Safetronic (November 8 - 9, 2022) has already started. Save your ticket now and take advantage of the early bird rate until October 10. Safetronic will again be organized as a hybrid conference.

Resilience is a key ingredient for lifting cars, industrial manufacturing and medical devices to the next level. It is far more than a fancier synonym of dependability. And the aspect that resilience is standing for is essential for building cognitive systems. That's why resilience is the focus of a new series on the Safe Intelligence Blog. In the first part, we will look behind the term and clarify why resilience is so important and why it is more than just a synonym for dependability.

Software development is a comparatively young, but complex and quickly evolving field. One of the more recent additions to the common portfolio of software architects is cloud technologies. In general terms, cloud technologies decouple software and the resources required for running. This allows services provided by the software to be quickly scaled according to demand. Nevertheless, there are several pitfalls that can be encountered when integrating cloud technologies into your software architecture.