50 years after the perceptron, 25 years after PDP: Neural computation in language sciences


Book Description

This Research Topic aims to showcase the state of the art in language research while celebrating the 25th anniversary of the tremendously influential work of the PDP group, and the 50th anniversary of the perceptron. Although PDP models are often the gold standard to which new models are compared, the scope of this Research Topic is not constrained to connectionist models. Instead, we aimed to create a landmark forum in which experts in the field define the state of the art and future directions of the psychological processes underlying language learning and use, broadly defined. We thus called for papers involving computational modeling and original research as well as technical, philosophical, or historical discussions pertaining to models of cognition. We especially encouraged submissions aimed at contrasting different computational frameworks, and their relationship to imaging and behavioral data.




Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society


Book Description

This volume features the complete text of all regular papers, posters, and summaries of symposia presented at the 18th annual meeting of the Cognitive Science Society. Papers have been loosely grouped by topic, and an author index is provided in the back. In hopes of facilitating searches of this work, an electronic index on the Internet's World Wide Web is provided. Titles, authors, and summaries of all the papers published here have been placed in an online database which may be freely searched by anyone. You can reach the Web site at: http://www.cse.ucsd.edu/events/cogsci96/proceedings. You may view the table of contents for this volume on the LEA Web site at: http://www.erlbaum.com.




Speech & Language Processing


Book Description




Neurocomputing


Book Description

The areas covered here are those which are commonly managed by the generalist. The four contributions discuss: the autopsy in fatal non- missile head injuries; viral encephalitis and its pathology; a general approach to neuropathological problems; and dementia in middle and late life. Gives an overview of the network theory, including background review, basic concepts, associative networks, mapping networks, spatiotemporal networks, and adaptive resonance networks. Explores the principles of fuzzy logic. Annotation copyrighted by Book News, Inc., Portland, OR




Speaking Minds


Book Description

Few developments in the intellectual life of the past quarter-century have provoked more controversy than the attempt to engineer human-like intelligence by artificial means. Born of computer science, this effort has sparked a continuing debate among the psychologists, neuroscientists, philosophers,and linguists who have pioneered--and criticized--artificial intelligence. Are there general principles, as some computer scientists had originally hoped, that would fully describe the activity of both animal and machine minds, just as aerodynamics accounts for the flight of birds and airplanes? In the twenty substantial interviews published here, leading researchers address this and other vexing questions in the field of cognitive science. The interviewees include Patricia Smith Churchland (Take It Apart and See How It Runs), Paul M. Churchland (Neural Networks and Commonsense), Aaron V. Cicourel (Cognition and Cultural Belief), Daniel C. Dennett (In Defense of AI), Hubert L. Dreyfus (Cognitivism Abandoned), Jerry A. Fodor (The Folly of Simulation), John Haugeland (Farewell to GOFAI?), George Lakoff (Embodied Minds and Meanings), James L. McClelland (Toward a Pragmatic Connectionism), Allen Newell (The Serial Imperative), Stephen E. Palmer (Gestalt Psychology Redux), Hilary Putnam (Against the New Associationism), David E. Rumelhart (From Searching to Seeing), John R. Searle (Ontology Is the Question), Terrence J. Sejnowski (The Hardware Really Matters), Herbert A. Simon (Technology Is Not the Problem), Joseph Weizenbaum (The Myth of the Last Metaphor), Robert Wilensky (Why Play the Philosophy Game?), Terry A.Winograd (Computers and Social Values), and Lotfi A. Zadeh (The Albatross of Classical Logic). Speaking Minds can complement more traditional textbooks but can also stand alone as an introduction to the field. Originally published in 1995. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.




Introduction To The Theory Of Neural Computation


Book Description

Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.




Patterns, Predictions, and Actions: Foundations of Machine Learning


Book Description

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers




The Cambridge Handbook of Computational Psychology


Book Description

A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.




The Deep Learning Revolution


Book Description

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.




An Introduction to Neural Network Methods for Differential Equations


Book Description

This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.