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

Artificial intelligence: reflecting on the past and looking towards the next paradigm shift

Artificial intelligence (AI) has undergone major advances over the past decades, propelled by key innovations in machine learning and the availability of big data and computing power. This paper surveys the historical progress of AI from its origins in logic-based systems like the Logic Theorist to recent deep learning breakthroughs like Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer 3 (GPT-3) and Large Language Model Meta AI (LLaMA). The early rule-based systems using handcrafted expertise gave way to statistical learning techniques and neural networks trained on large datasets. Milestones like AlexNet and AlphaGo established deep learning as a dominant AI approach. Transfer learning enabled models pre-trained on diverse corpora to excel at specialised downstream tasks. The scope of AI expanded from niche applications like playing chess to multifaceted capabilities in computer vision, natural language processing and dialogue agents. However, current AI still needs to catch up to human intelligence in aspects like reasoning, creativity, and empathy. Addressing limitations around real-world knowledge, biases, and transparency remains vital for further progress and aligning AI with human values. This survey provides a comprehensive overview of the evolution of AI and documents innovations that shaped its advancement over the past six decades

Artificial Intelligence and Machine Learning: A Perspective on Integrated Systems Opportunities and Challenges for Multi-Domain Operations

This paper provides a perspective on historical background, innovation and applications of Artificial Intelligence (AI) and Machine Learning (ML), data successes and systems challenges, national security interests, and mission opportunities for system problems. AI and ML today are used interchangeably, or together as AI/ML, and are ubiquitous among many industries and applications. The recent explosion, based on a confluence of new ML algorithms, large data sets, and fast and cheap computing, has demonstrated impressive results in classification and regression and used for prediction, and decision-making. Yet, AI/ML today lacks a precise definition, and as a technical discipline, it has grown beyond its origins in computer science. Even though there are impressive feats, primarily of ML, there still is much work needed in order to see the systems benefits of AI, such as perception, reasoning, planning, acting, learning, communicating, and abstraction. Recent national security interests in AI/ML have focused on problems including multidomain operations (MDO), and this has renewed the focus on a systems view of AI/ML. This paper will address the solutions for systems from an AI/ML perspective and that these solutions will draw from methods in AI and ML, as well as computational methods in control, estimation, communication, and information theory, as in the early days of cybernetics. Along with the focus on developing technology, this paper will also address the challenges of integrating these AI/ML systems for warfare

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

The expected AI as a sociocultural construct and its impact on the discourse on technology

The thesis introduces and criticizes the discourse on technology, with a specific reference to the concept of AI. The discourse on AI is particularly saturated with reified metaphors which drive connotations and delimit understandings of technology in society. To better analyse the discourse on AI, the thesis proposes the concept of “Expected AI”, a composite signifier filled with historical and sociocultural connotations, and numerous referent objects. Relying on cultural semiotics, science and technology studies, and a diverse selection of heuristic concepts, the thesis delves beneath the surface of AI discourse and demonstrates the hidden political, social, cultural, and ecological dangers of AI. The entanglement of the discourse(s) with (science) fiction, folklore, myth, and religion impacts how AI is perceived and received, as well as the expectations to AI-enabled technologies now and in the future. The thesis also proposes a more ethical and comprehensive ontological model for AI systems. The model describes AI systems as complex figurations, considering their socio-material organisation, global economic-material becoming, and impact on the environment, social institutions, and the semiosphere. The dissertation argues that AI should be understood not just as an object or sociotechnical system, but as its entire product chain encompassing people and cultures, as well as the used resources and impact (both material-ecological and semiotic) on a planetary scale

Intelligenza artificiale: i primi 50 anni

La tesi tratta dell'origine e dello sviluppo dell'intelligenza artificiale. Il primo capitolo si sofferma sulla cibernetica, disciplina scientifica nata negli anni Quaranta che può essere vista come la matrice originale da cui nacque l'intelligenza artificiale. Nel secondo capitolo, viene presa in esame la collaborazione tra John McCarthy e Claude Shannon per la stesura degli Automata Studies, una raccolta di articoli aventi come tema le macchine intelligenti; la delusione per il risultato finale avrebbe spinto McCarthy a organizzare la Conferenza di Dartmouth del 1956, argomento principale del terzo capitolo. Il quarto capitolo si sofferma invece sugli effetti di queste nuove scoperte in Italia durante gli anni Cinquanta-Sessanta. Infine, nell'ultimo capitolo viene presa in esame la conferenza del 2006 AI@50, nata per celebrare la Conferenza di Dartmouth originale e per analizzare gli sviluppi dell'intelligenza artificiale nell'intervallo di tempo intercorso tra i due eventi

A Short History of Cybernetics in the United States: The Origin of Cybernetics

Key events in the history of cybernetics and the American Society for Cybernetics are discussed, among them the origin of cybernetics in the Macy Foundation conferences in the late 1940s and early 1950s; different interpretations of cybernetics by several professional societies; reasons why the U.S. government did or did not support cybernetics in the 1950s, 1960s, and 1970s; early experiments in cyberspace in the 1970s; conversations with Soviet scientists in the 1980s; the development of „second order“ cybernetics; and increased interest in cybernetics in Europe and the United States in the 2000s, due at least in part to improved understanding of the assumptions underlying the cybernetics movement. The history of cybernetics in the United States is viewed from the perspective of the American Society for Cybernetics (ASC) and several questions are addressed as to its future.Key events in the history of cybernetics and the American Society for Cybernetics are discussed, among them the origin of cybernetics in the Macy Foundation conferences in the late 1940s and early 1950s; different interpretations of cybernetics by several professional societies; reasons why the U.S. government did or did not support cybernetics in the 1950s, 1960s, and 1970s; early experiments in cyberspace in the 1970s; conversations with Soviet scientists in the 1980s; the development of „second order“ cybernetics; and increased interest in cybernetics in Europe and the United States in the 2000s, due at least in part to improved understanding of the assumptions underlying the cybernetics movement. The history of cybernetics in the United States is viewed from the perspective of the American Society for Cybernetics (ASC) and several questions are addressed as to its future

The Influence of German-Speaking Émigrés on the Emergence of Cognitive Science as a New Interdisciplinary Field

During the 1950s, the scientific world experienced a shift in the study of the mind in what is now called the cognitive revolution. While common belief claims a rise of novel approaches, this is only partially true. A number of notions which built the foundation for cognitive studies were already present in the prior century in German schools. Research of developments of these traditions and concepts leading up to the cognitive revolution also showed that certain key figures in psychology and mathematics taught in Germany and by means of often forced emigration carried over the ideas that sparked in early German research centres. This article gives an overview of the development leading up to the cognitive revolution and the émigrés involved

Imagining the thinking machine: Technological myths and the rise of artificial intelligence

This article discusses the role of technological myths in the development of Artificial Intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: the creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on Artificial Intelligence published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers, and technologists, three dominant patterns in the construction of the AI myth are identified: (1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies; (2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome; (3) the relevance of controversies around the claims of AI, which we argue should be considered as an integral part of the discourse surrounding the AI myth