Chair of Artificial Intelligence and Machine Learning

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AIM@LMU Ringvorlesung

The interdisciplinary lecture series "AI in Science and Society" („KI in Wissenschaft und Gesellschaft“), in which the social relevance of AI and the importance of AI in different scientific fields are to be examined, is one component of the planned new Minor "AI as a major Minor." Although the range of courses is still being prepared, we would like to offer this lecture as a trial in the coming summer semester. The lecture series is open to all members of the LMU community. Stay tuned to this website for up-to-date information.

When: SoSe 2022, Wednesdays 16 to 18h

Where: Geschw.-Scholl-Pl. 1 (A) - A 120

(tentative schedule)

27.04.2022 no lecture

04.05.2022 no lecture

11.05.2022 Prof. Dr. Rudolf Seising (Forschungsinstitut für Technik- und Wissenschaftsgeschichte Teilprojekt "Automatisches Beweisen", Deutsches Museum, München) 
"Geschichten der Künstlichen Intelligenz"

Der schillernde Begriff der Künstlichen Intelligenz (KI) steht für ein wissenschaftliches Fachgebiet. Als solches wurzelt es in etablierten wissenschaftlichen Disziplinen, mit je nach Perspektive unterschiedlicher Gewichtung, u.a. Logik, Elektrotechnik, Informatik, Mathematik, Hirnforschung, Psychologie. Dementsprechend können sowohl die Anfänge als auch die Entwicklungspfade der KI sehr unterschiedlich erzählt werden. In diesem Vortrag werden einige dieser Erzählungen präsentiert.

18.05.2022 Prof. Dr. Nicola Lercari (Institute for Digital Cultural Heritage Studies, LMU Munich) 
"AI for the Study of Ancient Cultures"

AI is gaining prominence in the study of ancient civilizations by providing new automated methods for analyzing large corpora of geospatial, material, textual, natural, and artistic data. For instance, Machine Learning algorithms are now widely used for the identification and classification of objects in archaeological collections and ancient features in the contemporary landscape. However, archaeologists using these techniques also face ethical and intellectual challenges. This lecture reviews recent AI applications in archaeology and intends to spark a debate on their role in studying and interpreting the human past.

25.05.2022 Prof. Dr. Frauke Kreuter (Chair of Statistics and Data Science in Social Sciences and the Humanities (SODA), LMU Munich)
"AI Fairness and Social Inequality: Why having the right data matters"

Artificial intelligence (AI) and Big Data offer enormous potential to explore and solve complex societal challenges. In the labor market context, for instance, AI is used to optimize bureaucratic procedures and minimize potential errors in human decisions. AI is also applied to identify patterns in digital data trails. Data trails are created when people use smartphones and IoT devices or browse the internet, for example. Unfortunately, the fact that all of this is dependent on social and economic contexts is often ignored when AI is used, and the importance of high-quality data is frequently overlooked. There is growing concern about the lack of fairness—an essential criterion for making good use of AI, and growing concern about the lack of diversity in data. This lecture introduces a framework that allows students to understand what to look out for when evaluating the quality of data streams and outlines the latest developments in the use of AI and Big Data with applications in economics, social research, and policymaking.

01.06.2022 Prof. Dr. Daniel Gruen (Lehrstuhl für Astrophysik, Kosmologie und Künstliche Intelligenz, LMU Munich)
"Artificial Intelligence for Cosmological Insights"

Die vielleicht drängendsten Probleme der modernen Physik sind unser fehlendes Verständnis der so genannten Dunklen Materie und Dunklen Energie, die zusammengenommen etwa 95% des Energie-Budgets des Universums ausmachen. Hierdurch motiviert sammeln globale Kollaborationen mit exponentiell wachsender Geschwindigkeit Teleskopdaten vom Himmel, inzwischen mit hunderten Millionen vermessener Galaxien. Künstliche Intelligenz ist unverzichtbar, diese Datenflut zu verarbeiten, vor allem aber die Daten genügend genau zu verstehen, komplexe astrophysikalische Systeme wie Galaxienhaufen vollständig modellieren zu können, und am Ende Einsichten über die wahre Natur des "dunklen" Universums zu gewinnen. In der Vorlesung stelle ich Beispiele für solche Anwendungsfälle, insbesondere aber auch die besonderen Anforderungen an künstliche Intelligenz für verlässliche statistische Analysen vor.

08.06.2022 - fällt aus Prof. Dr. Gitta Kutyniok (Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU Munich)
"Towards Reliable AI: The Impact of Mathematics " 

During the last years, artificial intelligence has changed our society in an unprecedented manner. The success stories range from public life such as autonomous driving, robots, or speech recognition up to the sciences, with significant impact on areas like astronomy, biology, humanities, and medicine, to name a few. However, there were also incidents, where AI-technology caused dangerous situations, behaved in an unexplainable manner, or led to unfair decisions. In addition, it is known that, for instance, deep neural networks encounter problems with robustness. Thus, also due to the AI Act of the European Union, there is a current major trend in both academia and industry to develop methodologies which ensure reliability of AI. In this lecture, we will provide an introduction into this exciting research field, discuss current problems with reliability of AI, and show the necessity for a mathematical approach, in particular, when aiming for certifications of AI-technologies. We will finish with several examples, which also include results concerning profound limitations of current AI-based approaches, which could only be revealed by such a mathematical perspective.

15.06.2022 no lecture

22.06.2022 Prof. Dr. Mario Haim (Chair of the Department of Media and Communication, LMU Munich)
"AI and News Online: Production, Distribution and Use"

Journalism is a central pillar for democratic societies. Over the last several years, however, it has faced severe challenges, not least due to a "platformization" of news, competition from "fake news," and a widespread unwillingness to pay for news online. Importantly, not only are many of these challenges driven by AI, but also journalism‘s tools to fight these challenges are largely AI-driven. This lecture provides insights and overview of the latest developments and applications of AI for journalism and news online.

29.06.2022 Dr. Chiara Gianoli  (Chair of Experimental Physics and Medical Physics)
"AI in Medical Physics"

The most remarkable developments in modern medicine have been related to the advances in medical imaging. An unprecedented number of digital images has been made available to the extent that several clinical data repositories have been also made “open access”. In this scenario, the interest in artificial intelligence in medical imaging, with particular reference to machine learning and deep learning, is growing enormously. In the near future, artificial intelligence is expected to change profoundly healthcare and the application of physics to healthcare, referred to as medical physics.
In this lecture, the role of imaging in medical physics, with particular reference to radiation oncology, will be presented. An introduction to artificial intelligence, machine learning, and deep learning in medical physics will be given to provide a common level of understanding. Fundamentals of model-based tomographic image reconstruction will be mentioned in function of the data-driven counterparts. Treatment planning, verification ad adaptation in radiotherapy will be explained. An overview of pertinent problems solved by means of artificial intelligence will be outlined.
Finally, exemplary current projects involving artificial intelligence at the Chair of Experimental Physics – Medical Physics in the Faculty for Physics of the Ludwig-Maximilians-Universität München will be illustrated.

06.07.2022 Prof. Dr. Stefan Feuerriegel (Chair of the Institute of AI in Management)
"Leveraging AI to reach the Sustainable Development Goals"

Leveraging artificial intelligence (AI) allows for unprecedented opportunities to make important progress towards reaching the Sustainable Development Goals (SDGs) of the United Nations by 2030. In this talk, we present several AI-powered tool from our research to support decision-makers in this objective. Key challenges include the use of novel (often unstructured) datasets, scaling analyses to large-scale, population-wide datasets, and using AI to improve decision-making for the better of society. Among others, we will answer specific questions of immediate impact: How can AI help in monitoring global development aid? How can AI support the global allocation of development aid to countries in need and thereby optimize progress towards reaching the SDGs? Our results are of direct relevance for decision-makers and policy institutions to promote evidence-based decisions targeting the SDGs?

13.07.2022 Prof. Dr. Sahana Udupa (Professor of Media Anthropology, Institut für Ethnologie) - cancelled
"AI and Extreme Speech Online "

20.07.2022 Prof. Dr. Michael Ingrisch (Head of Clinical Data Science at the LMU Klinikum)
"AI in Radiology"

Deep learning and computer vision have been tremendously successful for image analysis and image classification. This success has raised considerable discussion about the potential role of AI applications in the radiological workflow. In today’s lecture, we will showcase several applications of AI for radiological imaging data, and we will demonstrate the importance of not only asking the right questions, but also answering them with the right data.

27.07.2022 Prof. Dr. Ines Helm (Professor of AI in Economics)
"AI and the Labor Market"

The last two decades have witnessed major advances in artificial intelligence and robotics. While there is the promise of productivity gains and growth, there exists also fear that AI will replace workers and lead to widespread unemployment. This lecture will discuss the potential impact of AI on the labor market, and show up similarities and differences to past episodes of technological change.