AI Keynote Series

We, the Institute of AI in Management at LMU Munich, are excited about AI in management and the dynamic developments in this field. That is why we would like to provide first-hand insights into the latest research work, granted by high profile scientists from all over the world. We are very honoured to be able to win great guest speakers for the keynotes every semester.

All session will be available via Zoom for everyone who is interested. We aim to provide an overview of current trends in AI research. The sessions, on Thursdays, consist of 45-60 minutes of presentation, followed by discussion, feedback and QA. We are looking forward to seeing you there.

All information on dates, times, speakers and their topics, incl. Zoom links will be published on this event page when we get closer to the dates.

You are invited to sign up for our newsletter, through which we communicate all upcoming events in this series. Please follow the link to our registration page.

The series is a joint initiative, led by LMU Munich (Prof. Stefan Feuerriegel) together with co-hosts from leading national universities:

Prof. Markus Weinmann, University of Cologne
Prof. Stefan Lessmann, Humboldt University Berlin
Prof. Mathias Kraus, Friedrich-Alexander University Erlangen-Nuremberg
Prof. Niklas Kühl, University of Bayreuth
Dr. Michael Vössing, Karlsruhe Institute of Technology
Prof. Oliver Müller, University of Paderborn
Prof. Nicolas Pröllochs, Justus-Liebig-University Gießen
Prof. Christian Janiesch, TU Dortmund
Prof. Gunther Gust, University of Würzburg
Prof. Tobias Brandt, University of Münster
Prof. Yash Raj Shrestha, University of Lausanne
Prof. Burkhardt Funk, Leuphana University Lüneburg
Prof. Nadja Klein, TU Dortmund
Prof. Martin Spindler, University of Hamburg
Prof. Niki Kilbertus, TU Munich
Prof. Stefan Bauer, TU Munich

Click here to see our past Speakers and Topics

Thursday 11.01.2024

Guest Speaker: Prof. Victor Veitch, University of Chicago

Presentation: Linear Structure of High-Level Concepts in Text-Controlled Generative Models

Abstract: Text controlled generative models (such as large language models or text-to-image diffusion models) operate by embedding natural language into a vector representation, then using this representation to sample from the model's output space. This talk concerns how high-level semantics are encoded in the algebraic structure of representations. In particular, we look at the idea that such representations are ''linear''---what this means, why such structure emerges, and how it can be used for precision understanding and control of generative models.

Time: 17:00 CET

Language: English