We are excited to announce the next Munich AI Lecture featuring Prof. Guido Montúfar, Professor of Mathematics and of Statistics & Data Science at the University of California, Los Angeles, and leader of the Mathematical Machine Learning research group at the Max Planck Institute for Mathematics in the Sciences, MPI MiS. He serves as a core Principal Investigator in the SECAI Zuse School of Excellence in AI (Leipzig–Dresden).

 Event Details:

🎤 Title: Deep Learning Theory: What we know, what we are learning, and what remains unclear

📅 Date and Time: July 17, 2025 at 5 pm CET

📍 Location: Room B006, Main LMU Building, Geschwister-Scholl-Platz 01, 80539 Munich

Abstract

Deep learning has revolutionized artificial intelligence and a wide range of applied domains, driving transformative progress in computer vision, language processing, and scientific discovery. This talk surveys the vibrant and rapidly evolving landscape of deep learning theory—an effort to uncover the mathematical foundations of learning with neural networks. We will review key theoretical insights into optimization dynamics, implicit biases of learning algorithms, and the generalization behavior of deep models—highlighting connections to classical learning theory, high dimensional statistics, and approximation theory. Along the way, we will discuss some of the major successes in analyzing overparameterized regimes, as well as open challenges in understanding feature learning and generalization under moderate overparameterization. The talk will also spotlight emerging phenomena such as benign overfitting, grokking, and delayed generalization, illustrating the depth and complexity of ongoing research questions that challenge traditional notions.

More information is available at the website of Munich AI Lectures

We are excited to announce the next Munich AI Lecture featuring Prof. Virginia Dignum, a member of the relAI Scientific Advisory Board. She is Professor of Responsible Artificial Intelligence at Umeå University, Sweden, where she leads the AI Policy Lab. She is also senior advisor on AI policy to the Wallenberg Foundations and chair of the ACM’s Technology Policy Council.

 Event Details:

🔹Title: Responsible AI: Governance, Ethics, and Sustainable Innovation

🔹Date and Time: July 9, 2025 6:30 pm

🔹Location: Plenarsaal of the Bavarian Academy of Sciences and Humanities (BAdW), Alfons-Goppel-Straße 11, 80539 Munich

Abstract

As AI systems become increasingly autonomous and embedded in socio-technical environments, balancing innovation with social responsibility grows increasingly urgent. Multi-agent systems and autonomous agents offer valuable insights into decision-making, coordination, and adaptability, yet their deployment raises critical ethical and governance challenges. How can we ensure that AI aligns with human values, operates transparently, and remains accountable within complex social and economic ecosystems? This talk explores the intersection of AI ethics, governance, and agent-based perspectives, drawing on my work in AI policy and governance, as well as prior research on agents, agent organizations, formal models, and decision-making frameworks. Recent advancements are reshaping AI not just as a technology but as a socio-technical process that functions in dynamic, multi-stakeholder environments. As such, addressing accountability, normative reasoning, and value alignment requires a multidisciplinary approach. A central focus of this talk is the role of governance structures, regulatory mechanisms, and institutional oversight in ensuring AI remains both trustworthy and adaptable. Drawing on recent AI policy research, I will examine strategies for embedding ethical constraints in AI design, the role of explainability in agent decision-making, and how multi-agent coordination informs regulatory compliance. Rather than viewing regulation as a barrier, will show that responsible governance is an enabler of sustainable innovation, driving public trust, business differentiation, and long-term technological progress. By integrating insights from agent-based modeling, AI policy frameworks, and governance strategies, this talk underscores the importance of designing AI systems that are both socially responsible and technically robust. Ultimately, ensuring AI serves the common good requires a multidisciplinary approach—one that combines formal models, ethical considerations, and adaptive policy mechanisms to create AI systems that are accountable, fair, and aligned with human values.

More information is available on the website of the Munich AI Lecture. This is the flagship speaker series about AI in Munich, co-organized by relAI