Abstract In October 1950, Alan Turing proposed that a machine’s success in an imitation game provides a criterion for thinking. Turing’s test became one of the philosophical foundations of AI, but three-quarters of a century later, it is widely claimed that generative AI models pass the test, rendering it obsolete. Our aim here is to establish definitively Turing’s conceptual design for his game. We argue that influential interpretations of Turing’s design are mistaken, with the consequence that his test has not been passed. We also present the results of carrying out, for the first time, Turing’s experiment as he proposed it.
Abstract The goal of this paper is to argue for a systematic set of connections between the philosophy literature on the empirical significance of symmetries and the physics literature on generating effective field theories using symmetry-breaking data via the coset construction. A salient case for exhibiting these connections is the symmetry principles exhibited by Newton’s Corollaries V and VI. In the following, we (i) argue for an interpretation of symmetry breaking in terms of subsystem–environment reasoning, and (ii) display a novel derivation of Newtonian gravitational dynamics from Newton’s Corollaries V and VI using this interpretation.
Abstract AI provides a rich source of examples for theories of agency because it allows relevant features to be relatively freely combined. This paper considers agency in sequence models: models that have been trained to predict the next item in a sequence. Training models on sequences of observations and actions can yield systems that use reinforcement learning in context, as well as ones that merely imitate agents. Investigating systems from this group suggests a requirement that agents must learn about the environment in part from their own interaction with it. It also suggests a distinction between two approaches to understanding agency.
Abstract Novel tools have allowed researchers to intervene into circuits at the mesoscale. The results of these interventions are often explained by appeal to functions. How are functions ascribed to circuit parts experimentally? I identify two kinds of function ascription practices in circuit interventions. Analysis of these practices shows us that what makes function ascriptions challenging is a lack of interventive control and insufficient constraints on the class of candidate functions to discriminate in practice. One kind of function ascription practice—subtractive analysis—fares better at addressing these challenges.
Abstract This article applies the Copernican principle and Gott’s delta- t method to the infamous Humean problem of induction. Copernican reasoning uses only the observed duration of a phenomenon to predict its future duration, so this reasoning is noncircular. It also does not involve positing necessary connections, as do many other attempts to answer Hume’s challenge. Copernican reasoning is controversial, as are many of its applications, but the application to Hume’s problem is one that even most critics of the Copernican principle should accept. If Copernican reasoning is ever applicable, it is applicable here. Copernicus might have an answer for Hume.
