philosophical aspects of deep learning

Machine learning with deep neural networks has seen a tremendous rise in the last decade, with large language models like GTP only the most recent example. This seminar focuses on philosophical questions around the capacities of deep neural networks. In particular, we will read and discuss the novel book by Cameron Buckner, From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence.

Our primary reading is the book by Bucker, supplemented by some background reading. See the below schedule and material for details. (Further background reading will be added to the schedule as the course progresses, possibly also based on the participants' interests.)

This is a philosophy course, and our focus will be on conceptual issues. It will, of course, be helpful to already have some prior knowledge of machine learning and deep learning in particular, but none is required. Throughout the course I will also spend a little time on covering the essentials of techniques in deep learning, mostly based on the introductory textbook by Goodfellow, Bengio & Courville (2016).

The course is worth 9 ECTS. Your grade will be determined by a term paper at the end of the course. The term paper treats of a theme we have discussed in the course, and has a length of about 6000 words.

In addition, starting from the third meeting, you are required to submit at least two questions for discussion by the day prior to each meeting. A discussion question comes in the form of indicating some aspect of the text that you find problematic, implausible, and/or confusing; and a brief explanation why you think so.