Center for Advanced Studies (CAS) Research Focus Next Generation AI
Center for Advanced Studies (CAS)
7 episodes
3 months ago
We currently witness the impressive success of artificial intelligence (AI) in real-world applications, ranging from autonomous driving over speech recognition to the health care sector. At the same time, modern, typically data-driven AI methods have a similarly strong impact on science such as astronomy, physics, medicine – as well as humanities or social sciences, often replacing classical methods in the state of the art. In fact, at present, basically any research area is already impacted or starting to get involved in research questions in the realm of AI. However, despite this outstanding success, most of the research on AI is empirically driven and not only is a comprehensive theoretical foundation -- in particular, in the sense of explanations of decisions -- missing, but even the limitations of these methods are far from being well understood. It is also far from clear how AI-based methods can be optimally combined with classical methods based on physical models as domain knowledge.
At present, two general streams of research in artificial intelligence can be identified worldwide. On the one hand, existing methodologies are adapted and applied to diverse scientific areas, while on the other hand, researchers aim to tackle the aforementioned methodological/theoretical problems and initiate the next generation of artificial intelligence. At LMU Munich, those directions are also prominently represented and displayed at https://www.lmu.de/ai. It is important to also stress that in fact both directions require a highly interdisciplinary effort and have many interconnections.
The CAS Research Focus therefore aims to connect, in particular, more methodological/theoretical with more application-oriented researchers across all faculties of LMU Munich as well as existing research and teaching activities, focusing on the following key problem complexes at the verge of the next generation of AI:
AI and Uncertainty
AI and Domain Knowledge
Limitations of AI
Social Aspects of AI (Explainability, Fairness, etc.)
All content for Center for Advanced Studies (CAS) Research Focus Next Generation AI is the property of Center for Advanced Studies (CAS) and is served directly from their servers
with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
We currently witness the impressive success of artificial intelligence (AI) in real-world applications, ranging from autonomous driving over speech recognition to the health care sector. At the same time, modern, typically data-driven AI methods have a similarly strong impact on science such as astronomy, physics, medicine – as well as humanities or social sciences, often replacing classical methods in the state of the art. In fact, at present, basically any research area is already impacted or starting to get involved in research questions in the realm of AI. However, despite this outstanding success, most of the research on AI is empirically driven and not only is a comprehensive theoretical foundation -- in particular, in the sense of explanations of decisions -- missing, but even the limitations of these methods are far from being well understood. It is also far from clear how AI-based methods can be optimally combined with classical methods based on physical models as domain knowledge.
At present, two general streams of research in artificial intelligence can be identified worldwide. On the one hand, existing methodologies are adapted and applied to diverse scientific areas, while on the other hand, researchers aim to tackle the aforementioned methodological/theoretical problems and initiate the next generation of artificial intelligence. At LMU Munich, those directions are also prominently represented and displayed at https://www.lmu.de/ai. It is important to also stress that in fact both directions require a highly interdisciplinary effort and have many interconnections.
The CAS Research Focus therefore aims to connect, in particular, more methodological/theoretical with more application-oriented researchers across all faculties of LMU Munich as well as existing research and teaching activities, focusing on the following key problem complexes at the verge of the next generation of AI:
AI and Uncertainty
AI and Domain Knowledge
Limitations of AI
Social Aspects of AI (Explainability, Fairness, etc.)
Künstliche Intelligenz ist schon jetzt nicht mehr aus unserem Leben wegzudenken. Ihr Einfluss auf die Wissenschaften ist enorm. Die Vortragsreihe diskutiert die aktuellsten Entwicklungen in den jeweiligen Fachdisziplinen und ist Teil des gleichnamigen CAS-Schwerpunktes "Next Generation AI" unter Sprecherschaft von Prof. Dr. Gitta Kutyniok (LMU). | Barbara Plank ist Professorin für KI und Computerlinguistik an der LMU. | Mario Haim ist Professor für Kommunikationswissenschaften mit Fokus auf computergestützte Kommunikationsforschung an der LMU. | Uli Köppen leitet das AI und Automation Lab des BR. | Hinrich Schütze ist Professor für Computerlinguistik und Direktor des Centrums für Informations- und Sprachverarbeitung an der LMU.
Künstliche Intelligenz ist schon jetzt nicht mehr aus unserem Leben wegzudenken. Ihr Einfluss auf die Wissenschaften ist enorm. Die Vortragsreihe diskutiert die aktuellsten Entwicklungen in den jeweiligen Fachdisziplinen und ist Teil des gleichnamigen CAS-Schwerpunktes "Next Generation AI" unter Sprecherschaft von Prof. Dr. Gitta Kutyniok (LMU). | Nils B. Weidmann ist Professor für Politikwissenschaft und Co-Sprecher des Exzellenzclusters „The Politics of Inequality” an der Universität Konstanz. Derzeit ist er Fellow am CAS
Künstliche Intelligenz ist schon jetzt nicht mehr aus unserem Leben wegzudenken. Ihr Einfluss auf die Wissenschaften ist enorm. Die Vortragsreihe diskutiert die aktuellsten Entwicklungen in den jeweiligen Fachdisziplinen und ist Teil des gleichnamigen CAS-Schwerpunktes "Next Generation AI" unter Sprecherschaft von Prof. Dr. Gitta Kutyniok (LMU). | Daniel Gruen ist Professor für Astrophysik, Kosmologie und KI an der LMU | Lukas Heinrich ist Professor für Data Science in Physik an der TUM | Kevin Heng ist Professor für theoretische Astrophysik extrasolarer Planeten an der LMU | Jenny Sorce ist Postdoc am Institut d'Astrophysique Spatiale (IAS) der Université Paris-Saclay und derzeit Fellow am CAS.
Data Science (DS) algorithms interpret outcomes of empirical experiments with random influences. Often, such algorithms are cascaded to long processing pipelines especially in biomedical applications. The validation of such pipelines poses an open question since data compression of the input should preserve as much information as possible to distinguish between possible outputs. Starting with a minimum description length argument for model selection Joachim Buhmann motivates a localization criterion as a lower bound that achieves information theoretical optimality for sufficient statistics. Uncertainty in the input causes a rate distortion tradeoff in the output when the DS algorithm is adapted by learning. Joachim Buhmann presents design choices for algorithm selection and sketches a theory of validation.
Karl-Peter Hopfner ist Professor für Biochemie am Genzentrum der LMU mit dem Fokus auf Strukturbiologie. | Julia Merlot ist Redakteurin im Wissenschaftsressort des SPIEGEL. | Alexander Pritzel ist wissenschaftlicher Mitarbeiter bei DeepMind und Mitentwickler des AlphaFold Programms
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, humanfocused datasets, and in data-analytic contexts such as classifiers and recommendation systems. Michael Jordan will discuss several recent projects that aim to explore the interface between machine learning and microeconomics, including the study of explorationexploitation trade-offs for bandit learning algorithms that compete over a scarce resource, leader/follower dynamics in strategic classification, and the robust learning of optimal auctions.
Center for Advanced Studies (CAS) Research Focus Next Generation AI
We currently witness the impressive success of artificial intelligence (AI) in real-world applications, ranging from autonomous driving over speech recognition to the health care sector. At the same time, modern, typically data-driven AI methods have a similarly strong impact on science such as astronomy, physics, medicine – as well as humanities or social sciences, often replacing classical methods in the state of the art. In fact, at present, basically any research area is already impacted or starting to get involved in research questions in the realm of AI. However, despite this outstanding success, most of the research on AI is empirically driven and not only is a comprehensive theoretical foundation -- in particular, in the sense of explanations of decisions -- missing, but even the limitations of these methods are far from being well understood. It is also far from clear how AI-based methods can be optimally combined with classical methods based on physical models as domain knowledge.
At present, two general streams of research in artificial intelligence can be identified worldwide. On the one hand, existing methodologies are adapted and applied to diverse scientific areas, while on the other hand, researchers aim to tackle the aforementioned methodological/theoretical problems and initiate the next generation of artificial intelligence. At LMU Munich, those directions are also prominently represented and displayed at https://www.lmu.de/ai. It is important to also stress that in fact both directions require a highly interdisciplinary effort and have many interconnections.
The CAS Research Focus therefore aims to connect, in particular, more methodological/theoretical with more application-oriented researchers across all faculties of LMU Munich as well as existing research and teaching activities, focusing on the following key problem complexes at the verge of the next generation of AI:
AI and Uncertainty
AI and Domain Knowledge
Limitations of AI
Social Aspects of AI (Explainability, Fairness, etc.)
