Connectionists: Lecture Series Philosophy of Science and Machine Learning: October 17th, 2024, André Curtis Trudel

[HT, UDNN]

We are delighted to invite you to our upcoming lecture series, Philosophy of Science and Machine Learning.

Lecture Series Overview: 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.

More information: https://udnn.tu-dortmund.de/index.php/activities/lecture-series-philosophy-of-science-and-machine-learning/

Upcoming Talk: We are excited to announce the next talk in the series:

Title: On finding what you're (not) looking for: prospects and challenges for AI-driven discovery
Speaker: Andre Curtis Trudel, University of Cincinnati
Date & Time: October 17th, 2024, 4:15 PM CET
Location: TU Dortmund University

Talk Abstract:
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.

How to Attend:

  *   In-Person: Please send an e-mail to udnn.ht at tu-dortmund.de<mailto:udnn.ht at tu-dortmund.de>. If you would like to join us for dinner afterwards, please let us know so we can make a reservation.
  *   Online: Please register via the following form: https://forms.microsoft.com/r/W3whw0ac3B. We will send you a Zoom link before the talk.

We look forward to your participation and insightful discussions.

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" (https://udnn.tu-dortmund.de/)

Kind regards,

The UDNN team

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