Welcome to relAI! – Konrad Zuse School of
Excellence in Reliable AI
The current technological revolution is largely driven by spectacular progress in artificial intelligence (AI). Yet, although the huge potential is widely recognized, the lack of reliability of AI technology is still considered a serious issue of concern, limiting its adoption both by industry and society at large. Indeed, aspects such as safety, security, and privacy-preservation are essential prerequisites for the use of AI in domains of public interest – e.g. ensuring that robots do not endanger life or respecting confidentiality of data.
The vision of the 'Konrad Zuse School of Excellence in Reliable AI' (relAI) is to train future generations of AI experts, who for the first time combine technical brilliance with awareness of the importance of AI’s reliability. Our novel, highly innovative AI program will educate top international candidates in the end-to-end development of reliable AI systems (including scientific knowledge, business expertise, and industrial exposure), both for industry and academia, and perform cutting-edge research to make AI ready for deployment in critical application domains.
relAI trains future generations of AI experts who combine technical brilliance with an eye on AI's implications for society.
NEWS
relAI second International Summer School
From July 28 to July 31, 2026, relAI hosted the second relAI International Summer School at LMU. This event focused on promoting educational exchange aimed at developing reliable AI and exploring current trends in the field. The summer school welcomed a group of Chinese students and provided them with comprehensive insights into four key research areas of … Read more
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Industry Partner QuantCo invites relAI for a Networking Event
Last week, our industry partner, QuantCo, hosted a networking event with relAI at their new Munich office. The relAI students had a wonderful evening filled with engaging conversations and delicious food. They had the opportunity to meet colleagues from QuantCo’s AI, and engineering teams and learned about how the company develops scalable systems to transform messy, real-world … Read more
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Can We Trust the Uncertainty of Causal Foundation Models?
Causal foundation models promise fast, flexible treatment-effect estimation from observational data, but can their uncertainty be trusted? The post of PhD Student Valentyn Melnychuk examines this question in light of the new class of foundation models for tabular and causal inference, called prior-data fitted networks (PFNs). He found that the answer is not always positive, but that … Read more
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