KI Lectures

Artificial intelligence powered methods are currently finding their way into almost every sphere of public life, where they often replace long-established traditional methods with great success. We are also seeing artificial intelligence exert a similarly strong influence in many scientific fields, such as astronomy, medicine and even the humanities. Despite this impressive rise in the technology’s impact, there is still a lack of understanding around the processes by which artificial intelligence actually makes decisions. Moreover, undesirable effects such as unforeseen wrong decisions remain a problem.

In her lecture, Gitta Kutyniok, Professor of Mathematics at LMU, starts by giving an introduction to artificial intelligence and explaining why these new methodologies are so extremely successful. She then goes into the extent to which artificial intelligence decisions are currently understood and can be explained to users. To finish, Gitta Kutyniok makes some suggestions as to how transparency, explainability and security around artificial intelligence can be achieved from a mathematical perspective.

Professor Gitta Kutyniok holds the Chair for Mathematical Foundations of Artificial Intelligence in the Faculty of Mathematics, Computer Science and Statistics, which is one of the AI professorships funded under the State of Bavaria’s High-Tech Agenda.

Video (in German, optionally with English subtitles)

The development of machine learning has changed the face of medical research in recent years. New AI-based analytics give us an increasingly better grasp of the complexity of genetic predisposition, social environmental factors and biological disease processes. At the same time, AI technology is acting as a catalyst for translating research into clinical application: more and more computer models are being developed that should be able to predict and prevent severe disease progression with the use of medical Big Data and enable individualized therapies to be planned. However, in many cases it is proving difficult to implement such tools owing to barriers around methodology and infrastructure, coupled with high regulatory and ethical requirements. AI-based personalized medicine must find answers to these challenges if the current hype around AI is really to lead to a Copernican revolution in the care of patients with chronic systemic diseases.

Nikolaos Koutsouleris, Professor of Precision Psychiatry at LMU Munich and King’s College London, will cover the latest developments in AI techniques in clinical neuroscience in this lecture and take a critical look at the application of these tools in clinical practice.

Professor Nikolaos Koutsouleris is Professor of Precision Psychiatry in the Department of Psychiatry at LMU and practices as a specialist in Psychiatry and Psychotherapy at the University of Munich Hospital.

Video (in German, optionally with English subtitles)

Artificial intelligence (AI) and Big Data offer enormous potential for exploring and solving complex societal challenges. In the labor market context, for example, AI is being used to optimize bureaucratic processes and reduce errors in human decision-making. AI is also being used to recognize patterns in digital data traces. Data traces are created, for example, when people use smartphones or IoT devices to browse the internet.

Unfortunately, that all of this is deeply interwoven into its surrounding social and economic context is often ignored in the application of AI, and the importance of high-quality data is often overlooked. There is growing concern about the lack of fairness—an essential criterion for making good use of AI. Fairness in this context means the adequate consideration of different social groups in the database and in pattern recognition.

This talk outlines recent developments in the use of AI and Big Data in economic and social research. Frauke Kreuter explains the shortcomings in their application and how science can come to grips with issues of ethics and privacy without compromising the ability to reproduce and reuse the data. The talk also outlines the essential conditions for a successful and fair use of AI.

Professor Frauke Kreuter holds the chair of Statistics and Data Science in the Social Sciences and Humanities and is co-director of the Data Science Centers at the University of Maryland and the University of Mannheim.

Video (in German, optionally with English subtitles)

The masterpieces of ancient Near Eastern literature are full of gaps. Cuneiform texts, written on clay tablets in the last three millennia BC, have come down to us in a fragmentary state. Piecing the fragments together is a lengthy process. Despite the groundbreaking discoveries made by a handful of specialists, there are still thousands of incomplete tablets in museum cabinets, which cannot yet be assigned to any composition.

This is the situation which the project "Electronic Babylonian Literature" (eBL), funded by the Humboldt Foundation, seeks to address. The aim of the project is to develop digital tools that automate, and thus dramatically accelerate, the process of reconstruction. In his lecture, Enrique Jiménez will showcase various methods for automating the reconstruction of ancient Near Eastern literature on the basis of artificial intelligence.

Professor Enrique Jiménez is Professor of Ancient Near Eastern Languages at the Institute for Assyriology and Hethitology in the Faculty for the Study of Culture at LMU.

Video (in German, optionally with English subtitles)

Artificial intelligence (AI) is increasingly influencing our everyday actions – which means it is becoming a key subject of ethical debate. Questions include: Are the decisions generated by AI free of discrimination? Is the use of AI-controlled robots in areas like nursing legitimate, sensible or perhaps even ethically imperative? Who or what entity takes responsibility when a fully autonomous AI system performs the wrong or even a prohibited action? When we say an AI “decides” or “acts”, what does that mean – and is an AI even capable of doing this? And finally, is it at all possible to design a “moral AI” and what theoretical approaches are being discussed in the research into this?

LMU asked two of its scholars and one graduate to share with the public their insights and their approaches to the ethical challenges of artificial intelligence as part of the AI Lectures.

The topic will be discussed by Fiorella Battaglia, visiting lecturer at the Chair in Philosophy and Political Theory, Timo Greger, scientific coordinator and joint project leader of “AI and Ethics” in the Faculty of Philosophy, Philosophy of Science and Religious Studies, and graduate Felicia Kuckertz, who was awarded a research prize for her bachelor’s thesis on “AI-powered military robots and moral responsibility”. Moderating the discussion will be Martin Wirsing, Professor of Computer Science at LMU and a sought-after expert in the field of programming, software engineering and development.

Video (in German, optionally with English subtitles)

Telescopes are collecting ever-increasing amounts of data about the Universe, enabling a better understanding of the most fundamental properties of our cosmos, its constituents and physical laws. The requirements of analyses used in this process differ significantly from traditional artificial intelligence use cases. The difficulty in observational cosmology lies in reconstructing with great accuracy almost invisible signals from the observation of large sections of the Universe.

Professor Daniel Grün, Chair of Astrophysics, Cosmology and Artificial Intelligence in the Faculty of Physics at LMU, will use his AI Lecture to explain the importance of special architectures and training methods that already support cutting-edge cosmological measurements in key ways today. In addition, the astrophysicist will offer an outlook on the use of so-called generative models, which enable the machine to “learn” from the data available what the elusive structures of the Universe look like in reality.

Professor Daniel Grün holds the Chair of Astrophysics, Cosmology and Artificial Intelligence in the Faculty of Physics at LMU.

Video (in German, optionally with English subtitles)

In a 2021 survey by the Center for the Governance of Change, more than 50 percent of respondents in Europe supported the concept of replacing some of their elected officials with algorithms. Younger people in particular supported this idea of artificial intelligence (AI) taking over power: 60 percent of 25-to-34-year-olds were in favor.

In data-based decision-making in the field of statistics, methods classified as artificial intelligence play an important role. In his lecture, Helmut Küchenhoff presents examples of data analyses for environmental, climate and electoral research, discusses how they can be implemented in politics and also draws on the COVID-19 pandemic as an example.

Professor Helmut Küchenhoff is Professor of Statistics at the Institute of Statistics and Head of the Statistical Consulting Unit (StaBLab) at LMU.

Video (in German, optionally with English subtitles)

Having featured in many fictional contributions to world literature, the colorful concept of artificial intelligence began to be implemented in a diverse array of technologies from the middle of the 20th century onward. This is now giving rise to impressive developments across all areas of science and civil society in the 21st century.

In his lecture, Thomas Seidl, Professor of Computer Science at LMU, will talk about the latest developments in artificial intelligence. Besides reflecting on the terminology and limitations of artificial intelligence, the lecture will present selected practical issues related to machine learning, particularly around the preparation and analysis of data necessary for automated learning.

In addition, Professor Seidl will offer insights into research at the Munich Center for Machine Learning. He will discuss the methodological foundations and the development of new technologies in image and speech recognition. He will also present examples of the challenges involved in applying artificial intelligence methods.

Professor Thomas Seidl is Professor of Computer Science. He is Chair of Database Systems and Data Mining at LMU’s Faculty of Mathematics, Informatics and Statistics and Director of the Munich Center for Machine Learning.

Video (in German, optionally with English subtitles)