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.)
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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.)
Center for Advanced Studies (CAS) Research Focus Next Generation AI
1 hour 28 minutes 36 seconds
3 years ago
Content Algorithm Validation for Data Science
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.
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.)
