Instructor: Zachary Lipton (Director of ACMI lab)
Time: Fridays 1:25pm - 3:30pm
Location: GHC 5222 (in person)
TA: Ivan Stelmakh
Reading List: see schedule
What is this field? What are its normative aims? What are its modes of inquiry? What are (and have been) its intellectual and ideological commitments? What foundational questions is it in dialogue with, and what foundational obstacles obstruct its progress? Finally: What are our responsibilities as researchers & practitioners deploying this technology?
The pursuit of machine intelligence holds philosophical significance, both because of the field’s own philosophical commitments, because it seeks (knowingly or not) to resolve questions of longstanding philosophical interest, and because of its role as a longstanding object of interest to philosophers of science and mind. However, these matters are seldom addressed in machine learning curricula, seldom addressed in machine learning’s conference proceedings, and often visible to only a small subset of machine learning scientists. The disconnect between these conceptual questions and technical work in the field has consequences: too many papers purporting to address questions that they do not (or cannot) address, and a burgeoning subfield of AI ethics surprisingly out of touch with most basic concerns of ethics. This is an exploratory seminar-style course aimed at inlining the philosophical problems surrounding machine intelligence into the machine learning curriculum.
In this course, we will address the origins of the field through the foundational writings of (e.g.,) Turing, Weiner, McCarthy, Simon, Minsky, Vapnik, Rumelhart/McClelland/Hinton, Pearl, etc. We will address the fundamental problem of learning from observation, including both the problem of induction (setting Popper in dialogue with Vapnik and Wolpert) and the formation, evolution, and abandonment of concepts/kinds/theories (e.g.,) through the writings of (e.g.,) Kuhn, Hacking, Hofstadter. We will address the very technical language used to formulate our inquiry, including probability and causality through (e.g.,) Polya, Cox, Cartwright, Pearl, Halpern. And finally, we will discuss the ethical considerations (and epistemic limitations) associated with automating decisions with the current generation of technology.
There are no formal prerequisites for this class. It is open to PhD students and MS and undergraduate students may enroll with permission from the instructor. The course will not involve much theorem proving or engineering but will be considerably more reading-intensive than a typical computer science course. Students are expected to keep up with each week’s readings, write thoughtful short responses digesting the main points and relating them to modern practice in the field, and to lead or co-lead the presentation/discussion of at least one reading during the semester.