Konferenzen  >  Mathematik  >  Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen  >  Deutschland

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Seminar addresses research gaps in the continual assessment of AI systems amid post-deployment changes in requirements, user-specific objectives, deployment environments, and the AI systems themselves.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In this Dagstuhl Seminar, we will explore the particularly important human domain of visual art, in order to obtain further insights into the cognitive ability of abstraction. Art is a culturally old and worldwide established means for humans to express their thoughts, emotions and views about the world, often as an expression of non-expressible or verbalizable information, episodes or experiences. We argue that understanding human reasoning, especially the ability to abstract, art is a valuable and rich candidate to be systematically analyzed. The seminar offers a unique opportunity to gather computer scientists, psychologists, cognitive scientists, artists, and art historians to explore the art domain toward understanding the cognitive tools needed for building AI systems with better abstraction abilities to be used for more understandable representations. The seminar will focus on how AI and art conceptualize abstraction by identifying the similarities and the differences, the role of abstraction in understandability, and how it can contribute to XAI.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Recent years have seen the emergence of a number of research groups that aim to advance quantum computing and to develop a skilled quantum software workforce. A particular challenge that must be overcome for safe quantum computing at scale is the creation of robust, expressive, efficient, and tractable formal verification tools. Since there is currently no event dedicated to this pursuit, the first goal of this Dagstuhl Seminar is to bring together a dedicated community of researchers in a single venue. This will give participants an opportunity to identify and discuss the most pressing challenges and promising directions for quantum programming and verification.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Implementierung und Anwendungen von künstlicher Intelligenz,    Benutzerschnittstellen, User Experience, Virtuelle Realität

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In addition to code and language reasoning, recent work shows that large language models (LLMs) can achieve promising results on causal reasoning tasks, e.g. in determining cause-effect relationships. This points to a new paradigm for addressing real-world causal tasks by integrating LLMs in the causal inference workflow, but also raises fundamental questions on causal reasoning capabilities of LLMs. That is, given that LLMs are trained on observational data, do LLMs’ capabilities correspond to causal reasoning or are simply a result of dataset memorization; and how reliable are the reasoning outputs? And if not, how can we use causal techniques to improve LLMs’ reasoning capabilities? To address these questions, this Dagstuhl Seminar would bring together experts from causal machine learning and language models to foster collaboration and identify the key research questions at the intersection of the fields.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model (LLM), so it references an authoritative knowledge base outside of its training data sources before generating a response. RAG and VectorDBs are two important concepts in natural language processing (NLP) and multi-modal data management that are pushing the boundaries of what AI systems can achieve. This Dagstuhl Seminar aims to bring together researchers from the emerging areas of RAG, VectorDBs, systems, and applications – providing opportunities for interdisciplinary progress.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Autonomous AI agents, empowered by recent advances in Large Language Models (LLMs) and generative AI, are rapidly changing how we interact with, structure, and reason about information. These "agentic" systems go beyond static analysis – they act: interpreting their environment, interacting with humans and other agents, and autonomously executing tasks. This evolution opens new doors for knowledge engineering, especially in the context of increasingly complex, dynamic, and human-centric information spaces. While traditional knowledge engineering emphasized structured symbolic representations (e.g., ontologies, knowledge graphs) and formal reasoning, modern generative agents leverage machine-learned representations and natural language interfaces to emulate intelligent behavior. However, this comes with new challenges: How can we ensure such agents are interpretable, controllable, and aligned with human expectations? What is the role of hybrid systems that combine symbolic reasoning and statistical learning? This meeting will explore agentic AI as a foundation for the next generation of knowledge engineering systems – those that not only understand and transform data, but can also autonomously interact, reason, and collaborate.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

spatial AI; probabilistic and statistical learning; generative deep learning; simulation-based inference; climate change

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The aim of this Dagstuhl Seminar is to bring together researchers from different subfields of computer science that study or use techniques in knowledge compilation.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Social Inteliigence in AI Theory of Mind Large Language Models Social Arificial Intelligence Social Agents

European Association for the Study of the Liver (EASL)

Artificial intelligence is sweeping all over medicine. But where is it useful, and where should clinicians be cautious? How can we shape the path towards equitable and explainable algorithms while mitigating biases and vulnerabilities for hepatology? In this interdisciplinary EASL school, you will learn key aspects of clinical artificial intelligence, all the way from understanding other people’s publications and claims towards your own kick-off for AI-enhanced research and clinical practice.

General hepatology; EASL Schools & Masterclasses

Hepatologie,    Medizintechnik

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Explainable AI; Knowledge Representation; Neuro-Symbolic AI; Machine Learning