Picking up the pieces…
My flight out of Pittsburgh has been cancelled, and as I may be stuck in the airport for some time, I will try to make a virtue of it by jotting down some of my promised reflections on the “simplicity and truth” conference at Carnegie Mellon (organized by Kevin Kelly). My remarks concern only the explicit philosophical connections drawn by (4 of) the seven non-philosophers who spoke. For more general remarks, see blogs of: Larry Wasserman (Normal Deviate) and Cosma Shalizi (Three-Toed Sloth). (The following, based on my notes and memory, may include errors/gaps, but I trust that my fellow bloggers and sloggers, will correct me.)
First to speak were Vladimir Vapnik and Vladimir Cherkassky, from the field of machine learning, a discipline I know of only formally. Vapnik, of the Vapnik Chervonenkis (VC) theory, is known for his seminal work here. Their papers, both of which addressed directly the philosophical implications of their work, share enough themes to merit being taken up together.
Vapnik and Cherkassky find a number of striking dichotomies in the standard practice of both philosophy and statistics. They contrast the “classical” conception of scientific knowledge as essentially rational with the more modern, “data-driven” empirical view:
The former depicts knowledge as objective, deterministic, rational. Ockham’s razor is a kind of synthetic a priori statement that warrants our rational intuitions as the foundation of truth with a capital T, as well as a naïve realism (we may rely on Cartesian “clear and distinct” ideas; God does not deceive; and so on). The latter empirical view, illustrated by machine learning, is enlightened. It settles for predictive successes and instrumentalism, views models as mental constructs (in here, not out there), and exhorts scientists to restrict themselves to problems deemed “well posed” by machine-learning criteria.
But why suppose the choice is between assuming “a single best (true) theory or model” and the extreme empiricism of their instrumental machine learner? Continue reading
Error Statistics Philosophy 