Conferences  >  Mathematics  >  Neural Networks and Artificial Intelligence, Machine Learning  >  Germany

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

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

Geometry and Topology,    Optimization Theory, Operational Research

entwickler.de Akademie

Das neue GenAI Bootcamp bei den JavaScript & Angular Days vom 23. - 24. Oktober 2025 in Berlin ist deine praxisnahe Einführung in die Welt der Generativen KI - speziell für Angular-Entwickler:innen. In dieser kompakten Schulung mit Marco Frodl lernst du, wie du moderne GenAI-Technologien im Enterprise-Umfeld verstehst, anwendest und nahtlos in deine Angular-Projekte integrierst.

Was dich erwartet:

- Grundlagen von Generative AI, Prompt Engineering, RAG & Agenten

- Entwicklung von PoCs mit Python, LangChain & LangGraph

- Robuste AI-Schnittstellen für den Zugriff aus dem Frontend

- Integration von AI-Backends in Angular-Anwendungen

- Streaming von Antworten & visuelles Reasoning in der UI

Email: info@entwickler-akademie.de

Generative AI, Angular, Prompt Engineering, RAG, LangChain, LangGraph, TypeScript, AI-Integration, AI-UX, Streaming APIs, Obsidian, Frontend-Entwicklung, Enterprise AI, KI im Frontend, Developer-Portal, AI-Prototyping, Reasoning-UI, MCP-Server, Wissensmanagement, LLM-Anbindung, KI-gestützte Anwendungen

Implementation and Applications of Artificial Intelligence,    Training in Computer Science, Courses

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Currently, a paradigm shift is taking place from human interpretation of results towards machine interpretation. The long-term vision here is to automate the benchmarking process further to enable machines to automatically learn the properties of algorithms. This requires a more rigid benchmarking process with all assumptions and design decisions made explicit and accessible for machine interpretation.

Implementation and Applications of Artificial Intelligence,    Scientific Computing and Data Science

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The seminar aims to bring together researchers and practitioners from diverse backgrounds – including those working in robotics, healthcare, natural sciences, machine learning, and knowledge representation – to develop a concrete action plan for advancing practical Neurosymbolic AI applications over the next three to five years.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Research in Bayesian optimisation spans multiple domains, including Machine Learning, Statistics, Engineering, and Operational Research. This makes it a multi-disciplinary field that benefits from a wide range of perspectives and methodologies, but also means the community is dispersed. As a result, there is a need for a forum where researchers from different disciplines can share insights, compare approaches, and collaborate on standardizing tools and benchmarks.

Optimization Theory, Operational Research,    Algorithms and Data Structures

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

This seminar brings together researchers from diverse backgrounds in order to study these questions. Which applications are most likely for an adversary to gain from exploiting machine learning models? Are there particular attacks that will be more prevalent than others? What defense strategies are possible—and practical? What is the cost of these defenses to utility?

Information Security and Information Privacy,    Implementation and Applications of Artificial Intelligence

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In this meeting of the Research Training Group "Neuroexplicit Models of Language, Vision, and Action", we will discuss current research and trends in combining neural and human-interpretable models. These model architectures have complementary strengths with respect to generalization, robustness, and interpretability. The overall goal of the RTG is to develop novel neuroexplicit models that accurately solve tasks in natural language processing, computer vision, and action-decision making, and to investigate the theoretical and practical principles of designing effective neuroexplicit models.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Inductive programming (IP) research – also called inductive program synthesis – is concerned with learning computer programs from data, typically input/output examples. It incorporates many areas of computer science, especially machine learning, automated reasoning, program verification, programming language theory, and software engineering. IP also has strong relations to research outside computer science, notably in cognitive science, where it can help build models of human inductive learning and intelligent tutoring systems for programming education. In industry, IP supports tools for end-user programming such as the Microsoft Excel plug-in FlashFill.

Software Engineering,    Implementation and Applications of Artificial Intelligence

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

Implementation and Applications of Artificial Intelligence,    Human-Computer Interaction, User Experience, Virtual Reality

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

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

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