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

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?

Implementierung und Anwendungen von künstlicher Intelligenz,    Verarbeitung Natürlicher Sprache, Computerlinguistik

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.

Computergestütztes Lernen,    Didaktik in der Wissenschaft

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

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

Geometrie und Topologie,    Optimierungsrechnung, Entscheidungsunterstützung