Another major challenge is related to the growing interest in integrating artificial intelligence algorithms into mission-critical applications in order to achieve increasing levels of autonomy, flexibility and adaptation to changes and unforseen events. Although machine learning algorithms have demonstrated high accuracy for specific tasks such as object recognition and classification, the current state of knowledge does not allow yet to provide the minimum levels of guarantees and justified confidence required for the certification of systems integrating such algorithms for mission critical domains. Indeed, machine learning (ML) algorithms are typically based on the assumption that the training dataset is representative of the inputs that the system will face during its deployment. However, in practice there are a wide variety of unexpected accidental, as well as adversarially-crafted, perturbations on the ML inputs that might lead to violations of this assumption. Furthermore, ML algorithms are often run on special-purpose hardware accelerators, which may themselves be subject to faults. A major goal of current research is to develop innovative solutions for the problem of establishing guarantees of reliability, security, safety, robustness and better explainability for systems that incorporate increasingly complex ML models, and for the challenge of determining whether such systems can comply with requirements for safety-critical systems. This includes for example defining new paradigms for the design, verification and systems level assurance of embeddable and certifiable AI architectures as well as the design of new processor chips to be used for AI critical applications. The use of AI techniques such as generative models  and virtual assistants to support the efficient design and optimization of critical embedded systems requires also careful investigation especially when the implemented systems need to be certified or to comply with stringent development assurance and regulation constraints.

Cybersecurity is another challenging area where significant scientific advances are needed to cope with the increase, in number and sophistication, of malicious threats targeting critical infrastructures and systems. Vulnerabilities span all system layers, and increasingly the lower layers and the hardware. Hardware assisted protection techniques and trustworthy embedded components are among the promising solutions being explored, together with new paradigms that consider security and safety holistically.  In addition, personal data protection and privacy have also become a real industrial and societal concern due to the massive collection of data and the requirement to demonstrate compliance with the GDPR.

Finally, developing sustainable and energy efficient solutions to reduce the environmental impact of future generations of critical systems and ensuring their social acceptability is nowadays a major concern and an important objective to achieve in all application sectors.

We are looking forward to welcoming you in Toulouse in June 2024 for the 12^th edition of the ERTS Congress to exchange about these topics and your recent contributions!