Bøker av Clayton Lewis

The success of predictive large language models (PLLMs) like GPT3 and ChatGPT has created both enthusiasts and skeptics of their widespread practical applications, but this book argues that the larger significance of such models is contained in what they suggest about human cognition. To explore this potential, the book develops a thought experiment called the Prediction Room, a reference to John Searle's influential Chinese Room argument, in which a human agent processes language by following a set of opaque written rules without possessing an inherent understanding of the language. The book proposes a new Room model-the Prediction Room with its resident Prediction Agent-generalizing the working of large language models. Working through a wide range of topics in cognitive science, the book challenges the conclusion of Searle's thought experiment, that discredited contemporary artificial intelligences (AI), through the suggestion that the Prediction Room offers a means of exploring how new ideas in AI can provide productive alternatives to traditional understandings of human cognition. In considering the implications of this, the book reviews an array of topics and issues in cognitive science to uncover new ideas and reinforce older ideas about the mental mechanisms involved in both sides. The discussion of these topics in the book serves two purposes. First, it aims to stimulate new thinking about familiar topics like language acquisition or the nature and acquisition of concepts. Second, by contrasting human psychology with the form of artificial psychology these models exhibit, it uncovers how new directions in the development of these systems can be better explored.In addition, this book: Evaluates PLLMs using cognitive science to reflect on implications for human cognition Explores how PLLMs reinforce and suggest new ideas about mental mechanisms of cognition Utilizes cognitive model theories to test limits of understanding large language models

A representation is a thing that can be interpreted as providing information about something: a map, or a graph, for example. This book is about the expanding world of computational representations, representations that use the power of computation to provide information in new forms, and in new ways. Unlike printed maps or graphs, computational representations can be dynamic, and even interactive, so that what is represented, and how, can be shaped by user actions. Exploring these new possibilities can be guided by an emerging theory of representation, that clarifies what characteristics representations must have to express the meaning being represented, and to enable users to discern that meaning easily and accurately. The theory also shows the way to inclusive design, for example using sounds to represent information commonly presented visually, so that people who cannot see can understand what is being presented. Because representations must be shaped by the abilities of their users, and by the nature of the meanings they convey, creating them requires perspectives from multiple disciplines, including psychology, as well as computer science, and the sciences appropriate to the content being expressed. The book presents a series of explorations of this large and complicated space, as invitations to further study, and to innovation.