Ray Briggs

(B) (II)

This course meets in Autumn and Winter quarters.

This course meets in Autumn and Winter quarters.

Recent developments in artificial intelligence have brought about a radical reconceptualization of our idea of knowledge work. The model of the laboratory scientist, whose task is to conduct elaborate experiments that probe, in minute detail, the correctness of a theoretical hypothesis, is gradually giving way to that of the data scientist, whose concern is to wrangle massive datasets in an effort to extract from them reliable predictions with only a minimal theoretical guidance. In this course, we will explore some of the epistemological implications of this AI-driven shift in our conception of knowledge and the work that goes into acquiring it. Focusing on applications of artificial intelligence that utilize feed-forward deep neural networks for statistical inference, we will investigate what the shift to "big data" means for our philosophical theories of induction. Are the learning algorithms employed in the training of deep neural networks really "theory free"? If so, why should we trust that their predictions are reliable? How do neural networks purport to solve the curve-fitting problem and Goodman's new riddle of induction, without giving weight to theoretical virtues such as simplicity? Without a background of causal knowledge to structure their inferences, how do neural networks distinguish between causation and mere correlation, and if they cannot, why should we allow their predictions to serve as inputs to a theory of rational decision making? (B) (II)

Trans experience raises interesting philosophical questions about how people understand and misunderstand each other as gendered beings, how our internal senses of ourselves relate to the way society perceives us, and how to re-imagine our ideas of a good or normal body.  This graduate seminar explores some of these questions through readings in contemporary Anglo-American philosophy that center trans and feminist perspectives. (I)

Epistemology is the study of belief, and addresses questions like “what are we justified in believing?” and “when does a belief count as knowledge?”  This course will provide an overview of Bayesian epistemology, which treats belief as coming in degrees, and addresses questions like “when does rationality require us to be more confident of one proposition than another?", “how should we measure the amount of confirmation that a piece of evidence provides for a theory?”, and “which actions should we choose, based on our judgments about how probable various consequences are?” (B) (II)

This course introduces non-classical extensions and alternatives to classical logic, and the philosophical debates surrounding them. Topics include modal logic (the logic of possibility and necessity), intuitionistic and many-valued logics (in which sentences may be neither true nor false, or both true and false), and relevant logic (which tries to refine the classical concept of entailment to capture the idea that the premises of arguments should be relevant to their conclusions). 

Students will learn tableau-style proof theories and Kripke frame semantics for a variety of non-classical logics, and will discuss adjacent philosophical issues, including the nature of necessity and possibility, the metaphysics of ordinary objects and fictional characters, the nature of truth, and the relationship between the world and the logical theories we use to describe it. (B) (II)