On June 26 and 27, the Max Planck Institute for Intelligent Systems in Tübingen will host a special symposium on the future directions in intelligent systems. Keynote lectures by leading international scientists will offer insights into groundbreaking research and emerging trends, giving attendees a forward-looking perspective on the global development of intelligent systems.
Register by June 23 to take part in the event and stay informed about all updates.
Date: Thursday, June 26, 2025 Time: 15:00-18:00 CEST Location: N0.002 MPI-IS Tübingen
Date: Friday, June 27, 2025 Time: 9:00-14:00 CEST Location: N0.002 MPI-IS Tübingen
If you plan to attend, please register online by 9:00 am on Monday, June 23.
Shuttle service (registration required)
Thursday, June 26
Friday, June 27
15:00 - 15:30
30min
15:30 - 16:30
1h
Open-Endedness, World Models, and the Automation of Innovation
The pursuit of Artificial Superintelligence (ASI) requires a shift from narrow objective optimization towards embracing Open-Endedness—a research paradigm, pioneered in AI by Stanley, Lehman and Clune, that is focused on systems that generate endless sequences of novel but learnable artifacts. In this talk, I will present our work on large-scale foundation world models that can generate a wide variety of diverse environments that can in turn be used to train more general and robust agents. Furthermore, I will argue that the connection between Open-Endedness and Foundation Models points towards automating innovation itself. This convergence is already yielding practical results, enabling self-referential self-improvement loops for automated prompt engineering, automated red-teaming, and AI debate in Large Language Models, and it hints at a future where AI drives its own discoveries.
Tim Rocktäschel is the Director, Principal Scientist, and the Open-Endedness Team Lead at Google DeepMind. He is also a Professor of Artificial Intelligence at the Centre for Artificial Intelligence in the Department of Computer Science at University College London (UCL), where he is the Principal Investigator of the UCL Deciding, Acting, and Reasoning with Knowledge (DARK) Lab. He is also a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. from UCL, receiving a Microsoft Research Ph.D. Scholarship and a Google Ph.D. Fellowship.
16:30 - 17:00
17:00 - 18:00
Talk Title TBC
09:00 - 10:00
Statistical Physics Perspective on Understanding Machine Learning
For over four decades, statistical physics has studied exactly solvable models of artificial neural networks. In this talk, we will explore how these models offer insights into deep learning and large language models. Specifically, we will examine a research strategy that trades distributional assumptions about data for precise control over learning behavior in high-dimensional settings. We will discuss several types of phase transitions that emerge in this limit, particularly as a function of data quantity. In particular, we will highlight how discontinuous phase transitions are linked to algorithmic hardness, impacting the behavior of gradient-based learning algorithms. Finally, we will cover recent progress in learning from sequences and advances in understanding generalization in modern architectures, including the role of dot-product attention layers in transformers.
Lenka Zdeborová is a Professor of Physics and Computer Science at École Polytechnique Fédérale de Lausanne, where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from the University of Paris-Sud and Charles University in Prague in 2008. Between 2010 and 2020, she was a researcher at CNRS, working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS and the Neuron Fund award. Lenka's expertise is in the application of concepts from statistical physics, such as advanced mean field methods, the replica method, and related message-passing algorithms, to problems in machine learning, signal processing, inference, and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.
10:00 - 10:30
10:30 - 11:30
Fact ‘n Fiction: How to Spot and Debunk Misleading Content?
Misleading content is especially dangerous to consumers of information since it is very hard to recognize. Examples of such content include false claims on social media backed up with credible (misused) scientific publications, images taken out of their original context with attached false claims or misleading charts encouraging the audience to believe false statements. How to spot and debunk misleading claims? The talk will expose the tricks of misleading content and present some answers.
Iryna Gurevych's many accolades include being a Fellow of the ACL, an ELLIS Fellow, an ERC Advanced Grant and the Milner Award of the Royal Society. She is Professor of Computer Science at the Technical University of Darmstadt in Germany, an adjunct professor at MBZUAI, UAE and INSAIT, Bulgaria. Her work in AI and natural language processing (NLP) combines deep understanding of human language with the latest paradigms in machine learning. She has made major contributions to establishing the field of argument mining and misinformation detection, among other topics.
11:30 - 12:00
For speakers.
12:00 - 13:00
For all attendees.
13:00 - 14:00
TBC
Zico Kolter is a Professor and Department Head of the Machine Learning Department at Carnegie Mellon University. Additionally, he serves on the Board of Directors at OpenAI, where he chairs the safety and security committee, is a co-founder and the Chief Technical Advisor of Gray Swan AI, an AI Security company, and is a Chief Expert at Robert Bosch, LLC. His work spans several topics in machine learning, including work in AI safety and robustness, LLM security, the impact of data on models, implicit models, and more. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), AISTATS (test of time), IJCAI, KDD, and PESGM.
15:00 - 19:00
4h
Separate registration required.
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