Chomskyan (R)evolutions

It is not unusual for contemporary linguists to claim that “Modern Linguistics began in 1957” (with the publication of Noam Chomsky’s Syntactic Structures). Some of the essays in Chomskyan (R)evolutions examine the sources, the nature and the extent of the theoretical changes Chomsky introduced in the 1950s. Other contributions explore the key concepts and disciplinary alliances that have evolved considerably over the past sixty years, such as the meanings given for “Universal Grammar”, the relationship of Chomskyan linguistics to other disciplines (Cognitive Science, Psychology, Evolutionary Biology), and the interactions between mainstream Chomskyan linguistics and other linguistic theories active in the late 20th century: Functionalism, Generative Semantics and Relational Grammar. The broad understanding of the recent history of linguistics points the way towards new directions and methods that linguistics can pursue in the future

Oceanus.

v. 26, no. 4 (1983

Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century

In the mid-1950s, researchers in the United States melded formal theories of problem solving and intelligence with another powerful new tool for control: the electronic digital computer. Several branches of western mathematical science emerged from this nexus, including computer science (1960s–), data science (1990s–) and artificial intelligence (AI). This thesis offers an account of the origins and politics of AI in the mid-twentieth century United States, which focuses on its imbrications in systems of societal control. In an effort to denaturalize the power relations upon which the field came into being, I situate AI’s canonical origin story in relation to the structural and intellectual priorities of the U.S. military and American industry during the Cold War, circa 1952 to 1961. This thesis offers a detailed and comparative account of the early careers, research interests, and key outputs of four researchers often credited with laying the foundations for AI and machine learning—Herbert A. Simon, Frank Rosenblatt, John McCarthy and Marvin Minsky. It chronicles the distinct ways in which each sought to formalise and simulate human mental behaviour using digital electronic computers. Rather than assess their contributions as discontinuous with what came before, as in mythologies of AI's genesis, I establish continuities with, and borrowings from, management science and operations research (Simon), Hayekian economics and instrumentalist statistics (Rosenblatt), automatic coding techniques and pedagogy (McCarthy), and cybernetics (Minsky), along with the broadscale mobilization of Cold War-era civilian-led military science generally. I assess how Minsky’s 1961 paper 'Steps Toward Artificial Intelligence' simultaneously consolidated and obscured these entanglements as it set in motion an initial research agenda for AI in the following two decades. I argue that mind-computer metaphors, and research in complex information processing generally, played an important role in normalizing the small- and large-scale structuring of social behaviour using mathematics in the United States from the second half of the twentieth century onward

Data and Language in Organizations: Epistemological Aspects of Management Support Systems

This book contributes to the literature on management decision support systems (DSS). DSS research is motivated by the observation that much of what managers do involves unstructured problem solving. For the reason, the structured, procedural models implemented in management information systems (MIS) have had little impact on actual managerial practice. Actually, the terms "decision" and "problem solving" over-simplify the image of managerial activity, if what is meant is choosing from a set of well-defined alternatives. Management also includes such aspects as reality testing, problem finding, scenario generation, and just plain muddling through. A broader conception of management cognition -- of which decision making is only a part -- is therefore adopted. The challenge to technology development is to support these unstructured managerial activities. The emphasis is to amplify managerial cognition and to improve decision effectiveness. However, to achieve this we must go beyond platitudes and come to a better understanding of what managers actually do. The activity of managers is almost entirely linguistic. Computers, as symbolic processors, ought to be an effective complement. However, a fundamental problem, stressed repeatedly throughout the book, is semantic change. The context of managers is always changing, whereas computational inference depends on fixed semantics. Herein Lies the basis for a theory of management support systems. The theory takes the form of an applied epistemology: how do managers know their world and detect its changes? Thus, while this book is oriented towards improving information technology, its attention is primarily to the content of management information and only secondarily to technology. Technological innovations abound. What is needed now is a better understanding of what these technologies are to do