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UID:6135-409@gi.de
CLASS: PUBLIC
SUMMARY:Epistemological Issues of Machine Learning in Science
DESCRIPTION:The Emmy Noether Group UDNN: Scientific Understanding and Deep 
 Neural Networks (https://udnn.tu-dortmund.de/) invites participation in a t
 wo day workshop that marks the project’s kick-off.\n\n\n\nWorkshop on Epist
 emological Issues of Machine Learning in Science\n\n\n\n27.–28.02.2024\n\n\
 n\nChaudoire Pavillon, TU Dortmund, Germany\n\n\n\nRegistration: udnn.fk14@
 tu-dortmund.de\n\nWebsite: https://udnn.tu-dortmund.de/index.php/activities
 /ws-epi-issues/\n\n\n\n\n\nDescription:\n\nWith impressive advances in Mach
 ine Learning (ML) and particularly Deep Learning, Artificial Intelligence i
 s currently taking science by storm. This workshop brings together top scie
 ntists and philosophers working on fundamental issues connected to the use 
 of Machine Learning in science. The workshop marks the launch of the DFG-fu
 nded Emmy Noether Group UDNN: Scientific Understanding and Deep Neural Netw
 orks, and is co-organized with the Lamarr Institute for Machine Learning an
 d Artificial Intelligence and co-funded by the Department for Humanities an
 d Theology at TU Dortmund University.\n\nTopics include, but are not restri
 cted to:\n\n• The relation between prediction and discovery on the one hand
 , and explanation and understanding on the other, in fields of science that
  heavily rely on ML methods\n\n• The key issues in identifying genuine disc
 overies and stable predictions by ML systems\n\n• Core conceptions of “expl
 anation” involved in the field of eXplainable AI (XAI), and their relation 
 to philosophical theories of understanding and explanation\n\n• Present lim
 itations associated with ML’s predictive power and what may be needed to ov
 ercome them\n\n• The connection between ML and traditional scientific means
  for prediction and discovery, such as theories, models, and experiments\n\
 n• Our present understanding of ML itself and its limitations\n\n\n\nSpeake
 rs\n\n• Life Sciences\n\nJürgen Bajorath (University of Bonn)\n\nAxel Mosig
  (Ruhr University Bochum)\n\n\n\n• Machine Learning Theory\n\nM. Klopotek (
 University of Stuttgart)\n\nMarie-Jeanne Lesot (Sorbonne Université Paris)\
 n\nDavid Watson (King’s College London)\n\n\n\n• Philosophy\n\nKathleen A. 
 Creel (Northeastern University Boston, MA)\n\nBrigitte Falkenburg (TU Dortm
 und)\n\nKonstantin Genin (University of Tübingen)\n\nLena Kästner (Universi
 ty of Bayreuth)\n\nHenk de Regt (Radbout University Nijmegen)\n\nEva Schmid
 t (TU Dortmund)\n\nTom Sterkenburg (LMU Munich)\n\n\n\n• Physics / Astronom
 y\n\nDominik Elsässer (TU Dortmund)\n\nMichael Krämer (RWTH Aachen)\n\nMari
 o Krenn (Max Planck Institute for the Science of Light)\n\nWolfgang Rhode (
 TU Dortmund)\n\nChristian Zeitnitz (BU Wuppertal)\n\n\n\nRegistration is fr
 ee but places are limited. To register, please send an E-mail to udnn.fk14@
 tu-dortmund.de until January 15, 2024 including your name and institution. 
 A small number of attendees will be able to join the conference dinner on t
 he 27th on a dutch-treat basis. If you want to join the dinner, please indi
 cate this in your registration.\n\n\n\nOrganizers\n\nAnnika Schuster, Frauk
 e Stoll, and Florian J. Boge\n\n\n\nUDNN – Scientific Understanding and Dee
 p Neural Networks\n\nTU Dortmund\n\nEmil-Figge-Straße 50\n\n44227 Dortmund\
 n\nGERMANY
LOCATION:Chaudoire Pavillon, TU Dortmund
DTSTAMP:20231221T132802Z
DTSTART:20240227T080000Z
DTEND:20240228T160000Z
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