As artificial intelligence (AI) systems become increasingly integrated into safety-critical domains—such as healthcare, transportation, and industrial automation—their dependability and safety have emerged as urgent priorities. Unlike traditional deterministic software, AI-driven systems introduce new complexities and unpredictable behaviors, making it challenging to guarantee their reliability and trustworthiness in scenarios where failures can have catastrophic consequences. There is a growing need for engineers and practitioners who not only understand the foundational principles of AI and deep learning but are also equipped to address the unique safety and dependability challenges these technologies present. This includes navigating the evolving landscape of AI standardization and regulation, such as the EU AI Act, and mastering state-of-the-art techniques for detecting and mitigating hardware-induced faults in AI systems.
This course provides a comprehensive introduction to the dependability and safety of artificial intelligence (AI) systems, with a special focus on their application in safety-critical domains. Students will explore the foundational principles of AI, including deep learning and the hardware architectures that support modern algorithms. The module covers the evolving landscape of AI standardization and industry regulations, such as the EU AI Act. Then, students will learn state-of-the-art solutions to assess, detect, and mitigate hardware-induced faults in AI systems. The course concludes with a discussion of future trends and challenges in the AI safety field.
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