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<span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">We are delighted to invite you to our upcoming lecture series, Philosophy of Science and Machine Learning.</span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<p style="mso-margin-top-alt:12.0pt;margin-right:0cm;margin-bottom:12.0pt;margin-left:0cm">
<b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">Lecture Series Overview:</span></b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black"> In an age where artificial intelligence (AI) is transforming science,
it becomes increasingly important to reflect critically on the foundations, methodologies, and implications of these advancements. This lecture series will investigate fundamental issues in AI from the vantage point of philosophy of science, which includes
topics such as the transparency and interpretability of AI within scientific research, as well as the impact of AI on scientific understanding and explanation. </span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<p style="mso-margin-top-alt:12.0pt;margin-right:0cm;margin-bottom:12.0pt;margin-left:0cm">
<span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">More information:
</span><span style="font-family:"Calibri Light",sans-serif"><a href="https://udnn.tu-dortmund.de/index.php/activities/lecture-series-philosophy-of-science-and-machine-learning/"><span lang="EN-GB" style="color:#1155CC">https://udnn.tu-dortmund.de/index.php/activities/lecture-series-philosophy-of-science-and-machine-learning/</span></a></span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black"> </span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
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<b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">Upcoming Talk:</span></b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black"> We are excited to announce the next talk in the series:</span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<p style="mso-margin-top-alt:12.0pt;margin-right:0cm;margin-bottom:12.0pt;margin-left:0cm">
<b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">Title:</span></b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black"> On finding what you’re (not) looking for: prospects and challenges for AI-driven
discovery</span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><br>
<b>Speaker:</b> Andre Curtis Trudel, University of Cincinnati <br>
<b>Date & Time:</b> October 17th, 2024, 4:15 PM CET<br>
<b>Location:</b> TU Dortmund University<o:p></o:p></span></p>
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<b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">Talk Abstract:</span></b><b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><br>
</span></b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif">Recent high-profile scientific achievements by machine learning (ML) and especially deep learning (DL) systems have reinvigorated interest in ML for automated scientific discovery
(e.g., Wang et al. 2023). Much of this work is motivated by the thought that methods might facilitate the efficient discovery of phenomena, hypotheses, or even models or theories more efficiently than traditional, theory-driven approaches to discovery. This
talk considers some of the more specific obstacles to automated, DL-driven discovery in frontier science, focusing on gravitational-wave astrophysics (GWA) as a representative case study. In the first part of the talk, we argue that despite these efforts prospects
for DL-driven discovery in GWA remain uncertain. In the second part, we advocate a shift in focus towards the ways DL can be used to augment or enhance existing discovery methods, and the epistemic virtues and vices associated with these uses. We argue that
the primary epistemic virtue of many such uses is to decrease opportunity costs associated with investigating puzzling or anomalous signals, and that the right framework for evaluating these uses comes from philosophical work on pursuitworthiness.<o:p></o:p></span></p>
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<b><span style="font-family:"Calibri Light",sans-serif;color:black">How to Attend:</span></b><span style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<ul style="margin-top:0cm" type="disc">
<li class="MsoNormal" style="margin-top:12.0pt;margin-bottom:0cm;line-height:normal;mso-list:l0 level1 lfo1;vertical-align:baseline">
<b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black;mso-fareast-language:DE">In-Person:
</span></b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black;mso-fareast-language:DE">Please send an e-mail to
</span><span style="font-family:"Calibri Light",sans-serif;mso-fareast-language:DE"><a href="mailto:udnn.ht@tu-dortmund.de"><span lang="EN-GB" style="color:#1155CC">udnn.ht@tu-dortmund.de</span></a></span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;mso-fareast-language:DE">.
If you would like to join us for dinner afterwards, please let us know so we can make a reservation.<o:p></o:p></span></li><li class="MsoNormal" style="margin-bottom:12.0pt;line-height:normal;mso-list:l0 level1 lfo1;vertical-align:baseline">
<b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black;mso-fareast-language:DE">Online:
</span></b><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black;mso-fareast-language:DE">Please register via the following form:</span><span lang="EN-GB">
</span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;mso-fareast-language:DE"><a href="https://forms.microsoft.com/r/W3whw0ac3B">https://forms.microsoft.com/r/W3whw0ac3B</a>. We will send you a Zoom link before the talk.<o:p></o:p></span></li></ul>
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<span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">We look forward to your participation and insightful discussions.</span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<p style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm">
<span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">This lecture series is a special edition of the AI Colloquium at TU Dortmund University, co-organized by the Lamarr Institute for Machine Learning and Artificial Intelligence, the
Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas). The Lecture Series is organized by the Emmy Noether Group "UDNN: Scientific Understanding and Deep Neural Networks”
(</span><span style="font-family:"Calibri Light",sans-serif"><a href="https://udnn.tu-dortmund.de/"><span lang="EN-GB" style="color:#1155CC">https://udnn.tu-dortmund.de/</span></a></span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif;color:black">) </span><span lang="EN-GB" style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<p style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm">
<span style="font-family:"Calibri Light",sans-serif;color:black">Kind regards,</span><span style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
<p style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm">
<span style="font-family:"Calibri Light",sans-serif;color:black">The UDNN team</span><span style="font-family:"Calibri Light",sans-serif"><o:p></o:p></span></p>
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