Conférences  >  Mathématiques  >  Réseaux de neurones et intelligence artificielle, apprentissage automatique  >  Allemagne

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

Calcul scientifique et science des données,    Gestion des connaissances, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The rise of Machine Learning and Artificial Intelligence in nearly all aspects of society stands in stark contrast with the irreproducibility of the literature and its inaccessibility to the general public. Therefore, we have developed OpenML, an online collaborative science platform for Machine Learning, available at http://openml.org. The aim of this workshop is to bring together developers and users of the platform to stimulate further development.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The goal of this Dagstuhl Seminar is to provide an interdisciplinary forum to discuss the fundamental principles of fault management and diagnosis, bringing together international researchers from the fields of symbolic reasoning, machine learning, and control engineering. The seminar plans to identify an integrated framework to harmonize problems and algorithms from the different fields, as well as ideas for novel, integrated solutions; and to produce a comprehensive agenda for future research.

Démonstration automatique de théorèmes,    Soft computing, algorithmes évolutionnaires, domaine des métaheuristiques, logique floue

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Peer review is the best mechanism for assessing scientific validity of new research that we have so far. But this mechanism has many well-known issues, such as the different incentives of the authors and reviewers, difficulties with preserving reviewer and author anonymity, confirmation and other cognitive biases that even researchers fall prey to. These intrinsic problems are exacerbated in interdisciplinary fields like Natural Language Processing (NLP) and Machine Learning (ML), where groups of researchers may vary so much in their methodology, terminology, and research agendas, that sometimes they have trouble even recognizing each other's contributions as "research". This Dagstuhl Seminar will cover a range of topics related to organization of peer review in NLP and ML

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Graphs and knowledge bases have been around for many decades, and research outcomes have tremendously impacted areas like mathematics, artificial intelligence, and databases. However, despite being already coined by the scientific community, technological developments and astronomical data growth make knowledge graph management a fundamental topic nowadays in various computer science areas, supporting novel applications at the science (e.g., biomedicine) and industry (e.g., Google’s Knowledge Graph) level.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The past decade has seen a rapid growth in the ability to collect large-scale spatiotemporal data sets about sports. Ideally, such data should be used to inform strategic and tactical decision making. On the one hand, strategy is the long-term planning of training sessions, signing of coaches and athletes, rotation and the plan made before a match/race. On the other hand, tactics are short-term and involve the execution and adaptation to the match/race plan. Having insights into the efficacy and feasibility of strategies and tactics is particularly important and challenging within sports because effective and novel strategy & tactics allow weaker teams or athletes to win against stronger ones. Unfortunately, the size, richness, and complexity of modern spatiotemporal sports data means that automated analysis is essential. Alas, the nature of the data has posed a number of challenges for classic analysis techniques. This has spurred the development of novel statistical and machine learning techniques in order to perform more fine-grained analysis of every action and decision during a competitive event. In this Dagstuhl Seminar, we aim to bring together sports researchers in academia and industry to understand how they are using machine learning and statistical techniques to analyze strategy and tactics.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Artificial intelligence and machine learning have made impressive strides in the last decade and – especially in the eyes of the general public – even in the last months, with innovations that have entered the daily life of billions of people. Given the magnitude of the impact of AI, the social good should not be an afterthought: market forces alone may not guarantee that these technologies benefit everyone. Instead, we believe that AI should empower those already championing humanitarian and development causes. In order to accelerate adoption of AI methods where their impact on the social good is largest, we propose to bring together non-governmental organizations working in international development and on humanitarian issues, with AI technical experts (academics, researchers, data scientists, engineers). Primary objectives of this Dagstuhl Seminar are to establish partnerships and build trust, to iterate on concrete problems in a hands-on hackathon, and to demonstrate what is feasible today via case studies.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The purpose of the seminar will be to initiate a debate around both theoretical foundations and practical methodologies for a "Trustworthiness & Responsibility in AI" framework that integrates quantifiable responsibility and verifiable correctness into all stages of the software engineering process. Such a framework will allow governance and regulatory practises to be viewed not only as rules and regulations imposed from afar, but instead as an integrative process of dialogue and discovery to understand why an autonomous system might fail and how to help designers and regulators address these through proactive governance. In particular, we will consider how to reason about responsibility, blame, and causal factors affecting the trustworthiness of the system.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Autonomous systems are intended to operate without human intervention over prolonged time periods, perceive their operating environment, and adapt to changes – while pursuing defined goals or generating new ones. The perception functions process the inputs of various sensors and generate an internal model of the operating environment. By relying on this model, the decision functions plan and execute the actions required to achieve the goals of the mission. To achieve safety for an autonomous system, the engineers should ensure that the perception functions can sufficiently accurately build the model of the environment, i.e., perception and establishing a context for prediction are reliable. They also seek to ensure that the planned actions are safe, i.e., decisions do not result in actions that endanger humans or other agents in the operating environment.

Génie logiciel,    Robotique, automation & mécatronique

From April 8 to 10, 2024 the Heinz Maier-Leibnitz Zentrum (MLZ) located in Garching near Munich will host the Machine Learning Conference for X-Ray and Neutron-Based Experiments. The first two days (April 8/9) will take place at the Community Center (Bürgerhaus) Garching with all participants. The third day (April 10) offers optional hands-on workshops and reactor tours at the site of research neutron source FRM II for all interested participants.

Mécanique, tribologie et rhéologie,    Physique appliquée: Diffraction de neutrons

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

This Dagstuhl Seminar provides a unique opportunity for discussing the discrepancy between human and AI generalization mechanisms and crafting a vision on how to align the two streams in a compelling and promising way that combines the strengths of both. To ensure an effective seminar, we aim to bring together cross-disciplinary perspectives across computer and cognitive science fields. Our participants will include experts in Interpretable Machine Learning, Neuro-Symbolic Reasoning, Explainable AI, Commonsense Reasoning, Case-based Reasoning, Analogy, Cognitive Science, and Human-Computer Interaction. Specifically, the seminar will focus on the following questions: How can cognitive mechanisms in people be used to inspire generalization in AI? What Machine Learning methods hold the promise to enable such reasoning mechanisms? What is the role of data and knowledge engineering for AI and human generalization? How can we design and model human-AI teams that can benefit from their complementary generalization capabilities? How can we evaluate generalization in humans and AI in a satisfactory manner?

Interpretable Machine Learning, Human-AI Collaboration, Cognitive Science, Neuro-Symbolic Reasoning, Explainability

Mise en œuvre et applications de l'intelligence artificielle,    Philosophie universitaire

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The fundamental and simple model of Stochastic Games was introduced in the fifties by prominent mathematicians (Shapley, Bellman) and form today a well adopted and thoroughly studied notion, with applications in computer science, mathematics, economics, and beyond. This wide adoption implies that many different research communities are involved in the study of stochastic games, oftentimes with different but related goals. Each community develops different tools towards understanding stochastic games, leading to a very broad and diverse literature with a variety of techniques and approaches. The goal of this Dagstuhl Seminar is to bring together researchers interested in the algorithmic aspects of Stochastic Games, from a theoretical as well as practical perspective.

This symposium will treat the impact of Artificial intelligence (AI) and Machine Learning (ML) on chemistry and biology, including drug design. The underlying theme of the discussions will be what we can now do with AI and ML that we couldn’t do before. For instance, is AI simply a new interpolation method in the old traditional computer-aided drug design (CADD)? Are the methods applied nowadays intransparent in contrast to the those applied in the early days? How can we combine the development and application of new materials with AI-generated knowledebases? How much can AI help in the prediction and retro-synthesis of new chemical compounds, and how useful are these compounds? Still, the quality of published data matters as meaningful AI applications depend on the availability of high-quality test datasets.

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

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Game design and implementation are tasks which require a high amount of creativity, and which must lead to products which require a high amount of fine-tuned functionality. For example, a game “level” should not only look appealing, it should also be playable and it should be interesting to play. These are not features which can be acquired simply by “training on big data”, which is what most developments in modern artificial intelligence are based on. The goal of this Dagstuhl Seminar is to investigate to what extent modern artificial intelligence techniques can produce meaningful and functional game content, and what changes to or extensions of these techniques can improve this AI-driven creative process.

Interface homme-machine, expérience utilisateur,    Multimédia et infographie, Jeu vidéo

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The workshop addresses all application aspects of artificial intelligence from symbolic methods of semantic technologies to sub-symbolic methods of machine learning, neuro-symbolic and hybrid approaches combining symbolic and non-symbolic methods. The focus of this workshop series is on the practical use of these methods to solve real-world application problems.

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.

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.

Gestion des connaissances, Big Data Computing,    Calcul scientifique et science des données

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.

Génomique et bio-informatique,    Mise en œuvre et applications de l'intelligence artificielle

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

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