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Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Neural Networks and Artificial Intelligence, Machine Learning,    Scientific Computing and Data Science

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Analysis,    Calculus, Differential Equations and Integration

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The central topic of our Dagstuhl Seminar is the development, implementation, and evaluation of new AI technologies to support decision-making in management. A distinguishing feature is therefore innovative algorithms from the field of AI (e.g. explainable AI, generative AI, large language models, probabilistic ML, causal ML, etc.) that enable new insights in practice and beyond. This holds great potential for informing and improving decision-making.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Conversational Agents (CA) as frontends to Information Retrieval (IR) and Recommender Systems (RS) become more popular in everyday life, with a wider range of users and usages. The latest developments in Large Language Models (LLMs) will have tremendous consequences, especially for the workplace and education. In this Dagstuhl Perspectives Workshop, we want to focus on the evaluation of these conversational systems, as appropriate methods are still missing. The quality of these systems is limited in terms of personalization, veracity and correctness, bias, transparency, trustworthiness, and understandability. Thus, evaluation methods must address these shortcomings. Furthermore, user- and usage-oriented aspects should become a more prominent and integral component in evaluations, as the user population as well as the tasks these systems are used for become more heterogeneous. For this reason, the topic-centric view of relevance has to be extended to a broad range of facets which are important for the different usage scenarios. Therefore, suitable evaluation criteria have to be specified, which form the basis for defining appropriate measures. Most importantly, the range of evaluation methods must be revisited and extended, as popular methods like the Cranfield approach or crowdsourcing must be complemented by new evaluation methods and strategies specifically tailored to this new type of system.

Natural Language Processing, Computational Linguistics,    Implementation and Applications of Artificial Intelligence

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve desirable scalability and accuracy guarantees. At the same time, modern data-analysis tasks require the use of advanced statistics to handle challenges such as testing for multiple hypotheses or identifying dependencies among data points, such as in time series or graphs. Probabilistic methods are also at the core theoretical computer-science areas, such as sublinear algorithms and average-case analysis. To obtain efficient data-analysis algorithms, probabilistic methods require careful balancing of theoretical and practical aspects. This Dagstuhl Seminar brings together researchers interested in statistical and probabilistic methods to design and analyze scalable algorithms for discovering knowledge in large and rich datasets. We plan to cover the following topics, among others.

Probability and Statistics, Game Theory,    Scientific Computing and Data Science

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Data-sharing across domain and infrastructure boundaries is becoming more common, and significant work has been done in aligning many of the standard model describing the information and data being exchanged. In order to guarantee the scalability and practicality of data-sharing implementations, these standard models need to be aligned with the emerging technology architectures designed to support exchange networks at this scale. This workshop will look at how the standard metadata models for cross-domain use - including DDI-CDI and related models - and architectural approaches can best be used to leverage the strengths of both.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Through a series of Dagstuhl hosted workshops, and the CODATA-DDI collaboration, we have developed a framework for cross-domain interoperability, published by the EC-funded WorldFAIR project. This framework is being employed both within data intensive science and and between the scientific world and the world of official data, collected to inform policy making, including in relation to the Sustainable Development Goals and other components of the UN Agenda. The effective use and uptake of such a framework will require methodologies to evaluate implementations. This workshop will explore such approaches, building on current work for FAIR assessment, and the experience gained from implementation in a range of case studies, including those in WorldFAIR. The workshop will focus on improvements to evaluation metrics, and to the metadata exchange frameworks themselves.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

Neural Networks and Artificial Intelligence, Machine Learning,    Scientific Computing and Data Science