Conferences  >  Informatics  >  Implementation and Applications of Artificial Intelligence  >  Germany

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The timeliness of this seminar is critical: in the last twelve months, LLMs have revolutionized many commercial fields and their use in education continues to grow. This use is mostly by students who use LLMs to generate answers to school work, while universities are grappling with the impact of LLMs on assessment policies and pedagogy.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Automated machine learning (AutoML) aims to reduce the need for experts to obtain effective ML pipelines. It offers off-the-shelf solutions that can be applied without prior knowledge in ML, allowing engineers to dedicate more time to domain-specific tasks. Regrettably, AutoML is underutilized in computational mechanics. There is almost no communication between the two communities, and engineers spend unnecessary effort selecting and configuring ML algorithms. In light of this, the current Dagstuhl Seminar is dedicated to fostering cross-pollination between the fields of AutoML and Computational Mechanics. It aims to convene a curated selection of international experts, each hailing from diverse backgrounds and various application domains, encompassing computer science, machine learning, engineering, mathematics, operations research, and industrial applications.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Robotic Process Automation (RPA) is an emerging technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI). RPA allows organizations to automate high-volume and repetitive tasks – also referred to as routines – performed by human users. The enactment of these routines is emulated by means of a software (SW) robot that works on the applications' user interfaces (UIs) in the same way as the original human operators did. The overall goal is to explore the scientific and technological foundations to pioneer new trust-aware RPA solutions that work in partnership with the human workforce, to enhance human capabilities rather than replace human intelligence and break through the barriers to human trust using RPA.

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,    Information & Knowledge Management, Big Data Computing

Software & Support Media GmbH

Whether you're a seasoned ML practitioner or just starting your journey, MLCon New York 2024 offers valuable insights, networking opportunities, and a chance to stay at the forefront of machine learning and artificial intelligence.

Phone: [017693102012];     Email: marketing@sandsmedia.com

ML Business & Strategy, Advanced ML Development, ML Tools, APIs & Frameworks, ML Basics & Principles

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Automated programming refers to techniques that suggest newly written code, e.g., in the form of code completion tools. Techniques for automated programming include large language models that predict code based on natural language specifications of the intended behavior. The recent development of technologies like Codex and ChatGPT have made us examine the possibility of automated programming in the future. Automated program repair refers to a suite of techniques for automated rectification of errors or vulnerabilities in programs. Automated program repair technologies were originally developed for reducing the debugging effort for manually written code. However, interestingly these techniques can be adapted to improve automatically generated code. This Dagstuhl Seminar will explore the intersection of these two fields.

Software Engineering,    Natural Language Processing, Computational Linguistics

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,    Neural Networks and Artificial Intelligence, Machine Learning

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,    Neural Networks and Artificial Intelligence, Machine Learning

WEKA FACHMEDIEN GMBH

Der zweite »Medical Solution Day« widmet sich 2024 der »KI für die Medizingeräte-Entwicklung« und den drängendsten Entwickler-Fragen zur KI-Integration: Was können KI-Algorithmen in medizinischen Anwendungen leisten, und wie lassen sie sich dort implementieren, aktualisieren und skalieren? Wie sollten MedTech-Entwickler – auch unter regulatorischen Aspekten – dabei vorgehen? Führende Technologie- und Medizin-Experten teilen Ihre KI-Expertise, um komplexe Medizin-KI-Anwendungen zu verstehen und zu entwickeln. Sie geben Hilfestellungen an die Hand und berichten über praktische und klinische Anwendungen in der Medizintechnik.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Generative AI systems are rapidly being integrated into global systems of cultural communication, consumption, and production. As these technologies shape our cultures, we urgently need conceptual foundations for investigating the cultural inclusivity of generative AI pipelines (from data collection, to model development and deployment, to evaluation), as well as methods to study the varying societal and cultural impacts of generative AI. This Dagstuhl Seminar wants to bring together scholars and practitioners from computer science, social sciences, the tech industry, and creative industries to discuss the cultural implications of generative AI and find paths toward building generative AI that can be responsive to the diverse needs of individuals, groups, and societies around the world. Together, seminar participants will build shared language and frameworks for reshaping the technical and social architectures of generative AI.

Natural Language Processing, Computational Linguistics,    Social Networks and Social Aspects of Information Technology

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Seminar Task and Situation-Aware Evaluation of Speech and Speech Synthesis is an opportunity to help redefine the metrics and methods traditionally used to evaluate speech synthesis and human speech as they are used across disciplines, tasks, and applications. The seminar is designed as a collaborative platform where experts from engineering, the humanities, social sciences, and more come together to challenge the status quo and drive innovation. Through the combination of perspectives and the bridging of gaps between scientific disciplines we hope to uncover and develop evaluation techniques that are not only scientifically rigorous but also contextually relevant to the diverse uses of speech technology today. We seek the collective expertise of participants from diverse areas of science and technology – ranging from phonetics to machine learning, from rhetorical analysis to practical applications in assistive technologies. This interdisciplinary approach is essential for crafting evaluation standards that are as dynamic and nuanced as the technologies and applications they aim to assess. To this end, we encourage you to share, debate, and refine ideas, methods, and tools that can contribute to a transformative discussion on the evaluation of speech synthesis.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

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

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Artificial intelligence has made tremendous strides across many spheres of life, however deploying this technology in safety critical domains remains challenging. This seminar focuses on clinical practice where data-driven models can streamline the work of healthcare professionals and democratise access to personalised medicine, thus have lasting positive impact on society, but also where deploying such tools without adequate foresight and safeguards can be perilous. This duality – anticipated benefits that may come along with unintended consequences – requires new technologies to be thoroughly vetted, e.g., with clinical trials and medical certification processes, before they can be deployed to avoid any harmful fallout. However, fulfilling such regulatory requirements is a lengthy and complex process plagued with many challenges, hence while prototype systems are becoming increasingly ubiquitous, they often remain indefinitely designated as research tools that can be used exclusively for research purposes. Their lacklustre adoption is compounded by pervasive reproducibility issues; history of unsafe systems being deployed prematurely; scarce data that are inherently private, difficult to collect or share, and often riddled with numerous biases; and prevalence of automation promises that never come to fruition. Such hurdles result in healthcare remaining one of the least digitised spheres of life.

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.

Neural Networks and Artificial Intelligence, Machine Learning,    Information Theory, Foundations of Computer Science

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

The first goal of this Dagstuhl Seminar is to survey existing uses of any kind of XR technologies in the OR. More importantly, we will seek to foster and create ideas for novel research on using XR in the OR. This should lead to novel use cases of the technology, novel research questions, and an agenda for research on XR in the OR for the next 5 years. As one of the tangible outcomes, we plan to develop a position paper outlining this agenda during the seminar, and submit it to a major journal. These goals will be achieved by bringing together researchers from all sub-fields of XR (VR, AR, sound and haptic rendering, new materials, and other kinds of mixed reality), along with practitioners (surgeons) from different medical areas, in order to bring the practical perspective of surgery to the seminar.

Healthcare Technologies,    Human-Computer Interaction, User Experience, Virtual Reality

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

There are many connections between market design with and without money, and this Dagstuhl Seminar aims to provide a platform for scholars in auctions, matching, and learning in games to work together and develop new ideas on how to deal with the complexities in designing and analyzing real-world markets.

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.

Neural Networks and Artificial Intelligence, Machine Learning,    Multimedia, Computer Graphics and Visualization, Games

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH