AI Colloquium
The AI Colloquium is a series of lectures dedicated to cutting-edge research in the field of machine learning and artificial intelligence, coorganized by the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr Institute), the Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas).
Programme
Distinguished researchers deliver captivating lectures followed by vibrant discussions. However, unlike traditional colloquia, the AI Colloquium prioritizes interactive dialogue, fostering international collaboration. Conducted primarily in English, these 90-minute sessions feature hour-long lectures and 30-minute Q&A sessions. Join every Thursday at 10 AM c.t. for a stimulating exploration of cutting-edge topics. Whether in-person at our Lecture Room on Fraunhofer Strasse 25 or via Zoom, our hybrid format ensures accessibility for all.
| Day (usually) | Thursday |
| Start and end time | 10 AM c.t. - 12 AM |
| Duration of Presentation | 60 Minutes |
| Location (usually) | Lecture Room 303 3. Floor Fraunhofer Strasse 25 Dortmund |
Upcomming Events
Is Information Digital? A Defense of Reality
- AI Colloquium
Abstract: Data-driven techniques, such as large-language models, have proven astonishingly powerful in recent years, but progress has been much slower with cyber-physical systems such as robots. Many people assume this is because there is not enough training data. In this talk, I explore a possible explanation that is much more fundamental. Specifically, I ask the question of whether there is a fundamental difference between acquisition of knowledge through observation and acquisition of knowledge through embodied interaction. Can you learn to ride a bicycle by watching others ride a bicycle? In previous work, I have used concepts from computer science (zero-knowledge proofs, bisimulation, etc.) to show that there are things you can learn from embodied interaction that cannot be learned by objective observation. In this talk, I use Shannon information theory to argue that objective observation falls far short of revealing everything about physical reality. There is information in the real world that cannot be represented digitally, and objective observation can never acquire more than a small subset of this information. In short, learning to ride a bicycle may require getting on a bicycle, even for a robot.
Prof. Edward A. Lee
Bio: Edward A. Lee is Professor of the Graduate School and Distinguished Professor Emeritus in Electrical Engineering and Computer Sciences (EECS) at the University of California at Berkeley, where he has been on the faculty since 1986. He is also co-founder of Xronos Inc. and BDTI, Inc. He is the author of seven books, some with several editions and translations, including two for a general audience, and hundreds of papers and technical reports. Lee has delivered hundreds of keynotes and other invited talks at venues worldwide and has graduated 40 PhD students.
Professor Lee's research studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center. From 2005-2008, he served as Chair of the EE Division and then Chair of the EECS Department at UC Berkeley. He has led the development of several influential open-source software packages, notably Ptolemy and Lingua Franca.
Past Events
Is Information Digital? A Defense of Reality
- AI Colloquium
Abstract: Data-driven techniques, such as large-language models, have proven astonishingly powerful in recent years, but progress has been much slower with cyber-physical systems such as robots. Many people assume this is because there is not enough training data. In this talk, I explore a possible explanation that is much more fundamental. Specifically, I ask the question of whether there is a fundamental difference between acquisition of knowledge through observation and acquisition of knowledge through embodied interaction. Can you learn to ride a bicycle by watching others ride a bicycle? In previous work, I have used concepts from computer science (zero-knowledge proofs, bisimulation, etc.) to show that there are things you can learn from embodied interaction that cannot be learned by objective observation. In this talk, I use Shannon information theory to argue that objective observation falls far short of revealing everything about physical reality. There is information in the real world that cannot be represented digitally, and objective observation can never acquire more than a small subset of this information. In short, learning to ride a bicycle may require getting on a bicycle, even for a robot.
Prof. Edward A. Lee
Bio: Edward A. Lee is Professor of the Graduate School and Distinguished Professor Emeritus in Electrical Engineering and Computer Sciences (EECS) at the University of California at Berkeley, where he has been on the faculty since 1986. He is also co-founder of Xronos Inc. and BDTI, Inc. He is the author of seven books, some with several editions and translations, including two for a general audience, and hundreds of papers and technical reports. Lee has delivered hundreds of keynotes and other invited talks at venues worldwide and has graduated 40 PhD students.
Professor Lee's research studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center. From 2005-2008, he served as Chair of the EE Division and then Chair of the EECS Department at UC Berkeley. He has led the development of several influential open-source software packages, notably Ptolemy and Lingua Franca.
Is Information Digital? A Defense of Reality
- AI Colloquium
Abstract: Data-driven techniques, such as large-language models, have proven astonishingly powerful in recent years, but progress has been much slower with cyber-physical systems such as robots. Many people assume this is because there is not enough training data. In this talk, I explore a possible explanation that is much more fundamental. Specifically, I ask the question of whether there is a fundamental difference between acquisition of knowledge through observation and acquisition of knowledge through embodied interaction. Can you learn to ride a bicycle by watching others ride a bicycle? In previous work, I have used concepts from computer science (zero-knowledge proofs, bisimulation, etc.) to show that there are things you can learn from embodied interaction that cannot be learned by objective observation. In this talk, I use Shannon information theory to argue that objective observation falls far short of revealing everything about physical reality. There is information in the real world that cannot be represented digitally, and objective observation can never acquire more than a small subset of this information. In short, learning to ride a bicycle may require getting on a bicycle, even for a robot.
Prof. Edward A. Lee
Bio: Edward A. Lee is Professor of the Graduate School and Distinguished Professor Emeritus in Electrical Engineering and Computer Sciences (EECS) at the University of California at Berkeley, where he has been on the faculty since 1986. He is also co-founder of Xronos Inc. and BDTI, Inc. He is the author of seven books, some with several editions and translations, including two for a general audience, and hundreds of papers and technical reports. Lee has delivered hundreds of keynotes and other invited talks at venues worldwide and has graduated 40 PhD students.
Professor Lee's research studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center. From 2005-2008, he served as Chair of the EE Division and then Chair of the EECS Department at UC Berkeley. He has led the development of several influential open-source software packages, notably Ptolemy and Lingua Franca.