I begin by sketching a minimalist libertarian account of free will, one that is neutral as between so-called event-causal, agent-causal, and even certain noncausal accounts of the metaphysics of free choice and action. On this account, free choices are governed by objective probabilities. I then consider four recent control- or explanation-based objections to any such probabilistic account and argue that they do not succeed. I will end by arguing that, contrary to widespread assumption, the thicker metaphysical accounts that many deploy in developing distinctive responses to adequacy objections are not so much rival theories of freedom as one account of freedom developed within different theories of the root categories of substance, property, and causation, which theories are to be adjudicated on independent grounds.

In this talk, I explore the connection between understanding persons and respecting them. This connection is the focus of my current book project and the culmination of several years of work. But instead of delving into the nuances of the central theoretical claims of this project, in this talk I will explore what motivates it. In doing so, my goal will be to upset some longstanding philosophical biases towards rationalist and rights-based theories of respect, to help clear conceptual space for my alternative claim that understanding persons sometimes constitutes a way of respecting them. The upshot, I will argue, is a view about respect that is focused on persons in their individuality, and the promise of an ethics premised on difference.

As opposed to some of his predecessors (especially Locke) and contemporaries (especially Hume), Reid believes that the mind is active, by contrast to the body, which is fully passive. He claims that the mind is “properly active”. The main purpose of this paper is to understand what exactly the distinction between power, operation, and ability amounts to, according to Reid. 

In this talk, I outline a framework for thinking about foundational philosophical questions in deep learning as artificial intelligence by linking its research agenda to that of classical empiricist philosophy of mind. Critics of deep learning frequently paint it as beholden to a radical form of empiricism found in the radical behaviorists like John Watson and B. F. Skinner, but most research in deep learning fits better with a more moderate and historically grounded form of empiricism found in figures from the history of philosophy. I rebut the radical caricature by explicating the more moderate form that can be reverse engineered from most headline achievements and extracted from position papers by major figures in deep learning. What is missing from the radical caricature—but highlighted by both mainstream historical empiricism and deep learning—is the critical role played by interactions amongst active, general-purpose faculties (like perception, memory, imagination, attention, and empathy--which I illustrate by exploring the faculty theories of Aristotle, Ibn Sina, John Locke, David Hume, William James, Adam Smith, and Sophie de Grouchy). Tying deep learning to empiricist faculty theories offers benefits to both disciplines: computer scientists can continue to mine the history of philosophy for ideas and targets to hit in creating more robustly rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can be realized in specific computational systems.

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