In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

Established approaches to assuring safety-critical systems and software are difficult to apply
to systems employing ML where there is no clear, pre-defined specification against which to …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Deployment of modern data-driven machine learning methods, most often realized by deep
neural networks (DNNs), in safety-critical applications such as health care, industrial plant …

Deep neural networks (DNNs) are widely used in various application domains such as
image processing, speech recognition, and natural language processing. However, testing …

We present Adapt, a new white-box testing technique for deep neural networks. As deep
neural networks are increasingly used in safety-first applications, testing their behavior …

The advent of sophisticated robotics and AI technology makes sending humans into
hazardous and distant environments to carry out inspections increasingly avoidable. Being …