Diversity Reading List

Abstract:

John Searle, in his paper on 'Minds, Brains, and Programs' (1980), argues that computational theories in psychology are essentially worthless. He makes two main claims: that computational theories, being purely formal in nature, cannot possibly help us to understand mental processes; and that computer hardware-unlike neuroprotein-obviously lacks the right causal powers to generate mental processes. I shall argue that both these claims are mistaken.

Abstract:
During the 1970s and 1980s, a team of Automated Theorem Proving researchers at the Argonne National Laboratory near Chicago developed the Automated Reasoning Assistant, or AURA, to assist human users in the search for mathematical proofs. The resulting hybrid humans+AURA system developed the capacity to make novel contributions to pure mathematics by very untraditional means. This essay traces how these unconventional contributions were made and made possible through negotiations between the humans and the AURA at Argonne and the transformation in mathematical intuition they produced. At play in these negotiations were experimental practices, nonhumans, and nonmathematical modes of knowing. This story invites an earnest engagement between historians of mathematics and scholars in the history of science and science studies interested in experimental practice, material culture, and the roles of nonhumans in knowledge making.

Abstract:

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

Abstract: One of the most important aspects of Grice's theory of conversation is the drawing of a borderline between what is said and what is implicated. Grice's views concerning this borderline have been strongly and influentially criticised by relevance theorists. In particular, it has become increasingly widely accepted that Grice's notion of what is said is too limited, and that pragmatics has a far larger role to play in determining what is said than Grice would have allowed. (See for example Bezuidenhuit 1996; Blakemore 1987; Carston 1991; Recanati 1991, 1993, 2001; Sperber and Wilson 1986; Wilson and Sperber 1981.) In this paper, I argue that the rejection of Grice has moved too swiftly, as a key line of objection which has led to this rejection is flawed. The flaw, we will see, is that relevance theorists rely on a misunderstanding of Grice's project in his theory of conversation. I am not arguing that Grice's versions of saying and implicating are right in all details, but simply that certain widespread reasons for rejecting his theory are based on misconceptions.1Relevance theorists, I will suggest, systematically misunderstand Grice by taking him to be engaged in the same project that they are: making sense of the psychological processes by which we interpret utterances. Notions involved with this project will need to be ones that are relevant to the psychology of utterance interpretation. Thus, it is only reasonable that relevance theorists will require that what is said and what is implicated should be psychologically real to the audience. (We will see that this requirement plays a crucial role in their arguments against Grice.) Grice, I will argue, was not pursuing this project. Rather, I will suggest that he was trying to make sense of quite a different notion of what is said: one on which both speaker and audience may be wrong about what is said. On this sort of notion, psychological reality is not a requirement. So objections to Grice based on a requirement of psychological reality will fail. Once Grice's project and that of relevance theorists are seen as distinct, it will be clear that they can happily coexist.2They are simply discussing different subject matters. One may start to wonder, however, about who is really discussing what is said, a topic that both camps claim. I will not attempt a conclusive answer to this question. But I will suggest that Grice's view, despite certain shortcomings, has advantages which seem all too often to have gone unnoticed.