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

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

Scientific Computing and Data Science,    Information & Knowledge Management, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

This seminar aims at reasoning critically about how we build software and hardware for end-to-end machine learning. We hope that the discussions will lead to increased awareness for understanding the utilization of modern hardware and kickstart future developments to minimize hardware underutilization. We thus would like to bring together academics and industry across fields of data management, machine learning, systems, and computer architecture covering expertise of algorithmic optimizations in machine learning, job scheduling and resource management in distributed computing, parallel computing, and data management and processing. The outcome of the discussions in the seminar will therefore also positively impact the research groups and companies that rely on machine learning.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

AI is a disruptive force. With growing applications in fields like healthcare, transportation, game playing, finance, or robotics in general, AI systems and methods are entering our everyday lives. Such tight interaction with AI requires serious safety, correctness, and reliability considerations. Recently, the field of safety in AI has triggered a vast amount of research. Via a diverse program with ample space for open yet guided discussion, we aim to address a number of key challenges that range across all fields.

The Hausdorff Research Institute for Mathematics (HIM)

The interaction between learning theory and harmonic analysis was emphasized by mathematics of quantum computing. One of the outstanding open problems in this area concerns the sharp estimates in Bohnenblust-Hille inequality that generalizes a celebrated Littlewood’s lemma. How to learn (with small error and with large probability) a complicated function or a very large matrix in a relatively small number of random (quantum) queries? Of course, there should be some Fourier type restrictions on a function (a matrix) to have a reasonable answer to this. The “classical” way of learning (Boolean) functions comes from very sophisticated extensions of theorems of Kahn—Kalai—Linial type. In those results the interplay between maximal influence and heavy Fourier tails is the main technique. Maximal influence should be large if the `tail’ is small. However, recently another approach that is hinged on Bohnenblust—Hille inequality appeared. The school will cover the classical maximal influence approach to `probably approximately correct' (PAC) learning as well as the recent achievements using Bohnenblust—Hille inequality and its quantum counterpart.

Analysis,    Courses and Events for Math Students and Early Career Researchers

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Institute 2024 has an overarching focus on Next-Generation Human-Centred AI and Cognitive Technologies; its technical programme addresses the formal & cognitive foundations for human-centred computing (for AI), and the human-centred design, development, and usability of cognitive technologies aimed at human-in-the-loop assistance & empowerment in decision-making, planning, creative-technical problem-solving, and automation. Application areas to be addressed in the institute include autonomous vehicles, cognitive robotics, social robotics, cognitive/creative design assistance technologies (e.g., architecture and built environment design, visuo-auditory narrative media design), clinical diagnostics, technology-assisted education/learning.

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

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.

Information & Knowledge Management, Big Data Computing,    Scientific Computing and Data Science

Max Planck Institute for the Science of Light

In the "2nd Workshop on Machine Learning for Quantum Technology" we aim to attract researchers interested in the use of classical ML/AI techniques to advance the development of quantum technologies in a broad sense. Some examples would be analyzing quantum measurements, estimating the parameters of quantum devices, discovering new quantum experimental setups, protocols, and feedback strategies, and generally improving aspects of quantum computing, quantum communication, and quantum simulation. See the review paper here for more relevant topics. We will also cover some quantum machine learning topics and their interconnections with classical machine learning.

Quantum Mechanics and Quantum Technology,    Implementation and Applications of Artificial Intelligence

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Non-Bayesian approaches appear to solve many problems that Bayesians once dreamt of solving using Bayesian methods. We thus believe that it is timely and important to rethink and redefine the promises and challenges of Bayesian approaches; and also to elucidate which Bayesian methods might prevail against their non-Bayesian competitors; and finally identify key application areas where Bayes can shine.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

At the Dagstuhl Seminar on RNA Regulation and Multidimensional Transcriptomics, we will delve into the intersection of computational biology, deep learning, and RNA research. The event will explore how cutting-edge AI technologies are reshaping our understanding of RNA-based gene regulation and its role in health and disease.

Genomics and Bioinformatics,    Implementation and Applications of Artificial Intelligence

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Perspectives Workshop aims, first of all, to bring together the communities working on the related areas of learning through interaction, (conversational) agents in games, dialogue and interaction, and collecting judgments from crowds through games, to make each community aware of the most recent developments in the other areas. We also intend to discuss current challenges, and whether advances in one area (e.g., grounded interaction) can benefit other areas (e.g., interactive learning).

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Artificial Intelligence, Computers and Society, Databases, Trust, Transparency, Accountability, Knowledge Graphs, Web Data

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

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Logic and learning are central to Computer Science, and in particular to AI-related research. Already Alan Turing envisioned in his 1950 “Computing Machinery and Intelligence” paper a combination of statistical (ab initio) machine learning and an “unemotional” symbolic language such as logic. The combination of logic and learning has received new impetus from the spectacular success of deep learning systems. The goal of this Dagstuhl Seminar is to bring together researchers from various communities related to utilizing logical constraints in deep learning and to create bridges between them via the exchange of ideas.

Implementation and Applications of Artificial Intelligence,    Information Theory, Foundations of Computer Science

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

In our daily lives, we as humans perceive our surroundings in an inherently multimodal way. Nevertheless, the vast majority of data analysis idioms are exclusively visual, not using the apparent potential of combined designs. While the visualization field studies visual data analysis solutions, the sonification field investigates comparable solutions that convey data over non-speech audio. There are several similarities between the methods and design theories of both approaches, such as the use of perceptual variables to encode data attributes, the use of marks and substrates for conveying items, and the role of interaction in manipulating the data representations. Over the recent decades, both fields have established research communities, theoretical frameworks, and toolkit support.

Implementation and Applications of Artificial Intelligence,    Multimedia, Computer Graphics and Visualization, Games

Max Planck Institute for the Physics of Complex Systems (MPI-PKS)

Complex Systems, Chaos and Self-Organisation,    Quantum materials, Low Temperature Physics

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

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Seminar will focus on bridging the gap between deep learning and knowledge graphs, and will discuss their integration: neurosymbolic AI. The aim is to advance the understanding of how symbolic knowledge - in the form of KGs - can be used to improve the capabilities of DL systems, and how DL can advance KG construction and applications.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

In a little over a year since the release of ChatGPT, language models (LMs) have stirred concerns in government, over the possibility that citizens will come to believe the textual and spoken output of such models. Similarly, they have caused panic in education, forcing a rethink of what students are learning and how to assess it. Of concern to us here, is whether LMs mean the end of computational and/or cognitive models of human language learning and language use. Does the practical success of LMs mean that computational linguistics (and perhaps even linguistics itself) is no longer relevant? Or are we missing problems with LMs that computational linguistics (and linguistics more generally) could help us both recognize and surmount?

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

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Seminar aims to bring together experts and stakeholders in Generative AI and computing education to foster collaboration and to chart a way forward as Generative AI continues to improve and proliferate. It is the goal of this seminar to leverage the experience and knowledge of dozens of programming education experts from around the world to form an enduring community of practice. During the Dagstuhl Seminar, we intend to develop strategies for incorporating LLMs into programming education and to rigorously evaluate their use and impact. This seminar will explore the following topics in the context of Generative AI in programming education: accessibility; diversity, equality, and inclusion; resources; introductory programming for computer science majors and non-majors; advanced courses (that use programming); curriculum changes; novel pedagogies, approaches and tools; and industry use and changes that may lead to new learning outcomes.

e-Learning, Computers in Education,    Didactics of Scientific Disciplines

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