The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence

This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing

Behavioural Economics: Classical and Modern

In this paper, the origins and development of behavioural economics, beginning with the pioneering works of Herbert Simon (1953) and Ward Edwards (1954), is traced, described and (critically) discussed, in some detail. Two kinds of behavioural economics – classical and modern – are attributed, respectively, to the two pioneers. The mathematical foundations of classical behavioural economics is identified, largely, to be in the theory of computation and computational complexity; the corresponding mathematical basis for modern behavioural economics is, on the other hand, claimed to be a notion of subjective probability (at least at its origins in the works of Ward Edwards). The economic theories of behavior, challenging various aspects of 'orthodox' theory, were decisively influenced by these two mathematical underpinnings of the two theoriesClassical Behavioural Economics, Modern Behavioural Economics, Subjective Probability, Model of Computation, Computational Complexity. Subjective Expected Utility

Chiasmic Rhetoric: Alan Turing Between Bodies and Words

This Dissertation analyzes the life and writing of inventor and scientist Alan Turing in order to identify and theorize chiasmic relations between bodies and texts. Chiasmic rhetoric, as I develop throughout the Dissertation, is the dynamic processes between materials and discourses that interact to construct powerful rhetorical effect, shape bodies, and also compose new knowledges. My research here extends our knowledge of the rhetoric of science by demonstrating the ways that Alan Turing\u27s embodied experiences shape his rhetoric. Turing is an unusual figure for research on bodily rhetoric and embodied knowledge. He is often associated with disembodied knowledge and as his inventions are said to move intelligence towards greater abstraction and away from human bodies. However, this Dissertation exposes the many ways that bodies are active in shaping and producing knowledge even within Turing\u27s scientific and technical writing. I identify how, in every text that Turing produces, chiasmic interactions between bodies and texts actively compose Turing\u27s scientific knowledge and technical innovations towards digital computation and artificial intelligence. His knowledge, thus, is not composed out of abstract logic, or neutral technological advances. Rather, his knowledge and invention are composed and in through discourses and embodied experiences. Given that bodies and discourses are also composed within social and political power dynamics, then the political, social, and personal embodied experiences that compose Turing\u27s life and his embodiment also compose his texts, rhetoric, inventions, and science. Throughout the Dissertation, I develop chiasmic rhetoric as it develops in the rhetorical figure of chiasmus, as intersecting bodies and discourse, dynamic and productive, and potentially destabilizing. I conclude by proposing a pedagogy of care and disorientation that are attuned to the complex embodiment of students interacting with texts in our technical writing and composition classrooms

Social and epistemological bases of technology transfer: The case of artificial intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis addresses a problem in the literature on technology transfer of understanding the local appropriation of knowledge. Based on interpretive and analytic traditions developed in Science and Technology Studies (STS) and ethnomethodology, I conceptualise technology transfer as involving communication between discursive communities. I develop the idea of 'performance of community' to argue that explanations of research and technology, and readings of those explanations, are sites for the elaboration of the identity of a discursive community. I explore this approach through a case study in the field of artificial intelligence (AI). I focus on what I call 'explanatory practices', that is practices of describing, identifying and explaining Al, and trace the differences in these practices, according to location, context and audience. The novelty of my thesis is to show the pervasiveness of performance of community within these explanatory practices, through showing the differences in the claimed identity and significance of Al, associated with different locations, contexts and audiences. I draw out some of the implications of my approach by counterposing it to a theory of technology transfer as the passing of neutral units of information, which I argue is implicit in a complaint made by Al vendors that the Al marketplace had been damaged by overselling or hype. In particular, I show that disclaimers of hype (more than the perpetration of it) had always been associated with the marketing of Al. More generally, my claim is that it is politically important to understand that neutral information is not available even as an ultimate standard, and that the local appropriation of knowledge is not an aberration to be controlled, but a component of both successful and unsuccessful communication between discursive communities

Artificial intelligence : a heuristic search for commercial and management science applications

Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1984.MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY.Bibliography: leaves 185-188.by Philip A. Cooper.M.S

The errors, insights and lessons of famous AI predictions – and what they mean for the future

Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions already abound: but are they reliable? This paper will start by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: th