Lecture Series: Philosophy of Science and Machine Learning
Description
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, or the impact of AI on scientific understanding and explanation.
This lecture series is a special edition of the AI Colloquium at TU Dortmund University, coorganized 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”, and generously funded by the German Research Foundation (DFG; grant 508844757).
Time & Place
Thursdays, 4:15pm, room 303, Joseph-von-Frauenhofer-Str. 44227 Dortmund
Speakers
17.10.24 André Curtis Trudel (University of Cincinnati) „On finding what you’re (not) looking for: prospects and challenges for AI-driven discovery“
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 DL 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.
28.11.24 Tim Räz (University of Bern) „The Concept of Memorization in Machine Learning“
12.12.24 Stefan Buijsman (TU Delft) „The impact of epistemic dependence on AI for understanding“
23.01.25 Nina Poth (Radboud University Nijmegen) „Common Sense and the Limits of AI“
Participation
Participation is free, but places are limited. If you are interested in participating online, please register via the following form: https://forms.microsoft.com/r/W3whw0ac3B. If you would like to attend in person, please send an e-mail to udnn.ht@tu-dortmund.de.
We look forward to your participation and insightful discussions.
Kind regards,
The UDNN team (https://udnn.tu-dortmund.de/)
Main Organizers
Annika Schuster, Frauke Stoll, and Florian J. Boge