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Understanding Computers and Cognition presents an important and controversial new approach to understanding what computers do and how their functioning is related to human language, thought, and action. While it is a book about computers, Understanding Computers and Cognition goes beyond the specific issues of what computers can or can't do. It is a broad-ranging discussion exploring the background of understanding in which the discourse about computers and technology takes place. Understanding Computers and Cognition is written for a wide audience, not just those professionals involved in computer design or artificial intelligence. It represents an important contribution to the ongoing discussion about what it means to be a machine, and what it means to be human. Book jacket.
This book presents a novel approach to neural nets and thus offers a genuine alternative to the hitherto known neuro-computers. The new edition includes a section on transformation properties of the equations of the synergetic computer and on the invariance properties of the order parameter equations. Further additions are a new section on stereopsis and recent developments in the use of pulse-coupled neural nets for pattern recognition.
An important collection of studies providing a fresh and original perspective on the nature of mind, including thoughtful and detailed arguments that explain why the prevailing paradigm - the computational conception of language and mentality - can no longer be sustained. An alternative approach is advanced, inspired by the work of Charles S. Peirce, according to which minds are sign-using (or `semiotic') systems, which in turn generates distinctions between different kinds of minds and overcomes problems that burden more familiar alternatives. Unlike conceptions of minds as machines, this novel approach has obvious evolutionary implications, where differences in semiotic abilities tend to distinguish the species. From this point of view, the scope and limits of computer and AI systems can be more adequately appraised and alternative accounts of consciousness and cognition can be more thoroughly criticised. Readership: Intermediate and advanced students of computer science, AI, cognitive science, and all students of the philosophy of the mind.
Computers, Chess, and Cognition presents an excellent up-to-date description of developments in computer chess, a rapidly advancing area in artificial intelligence research. This book is intended for an upper undergraduate and above level audience in the computer science (artificial intelligence) community. The chapters have been edited to present a uniform terminology and balanced writing style, to make the material understandable to a wider, less specialized audience. The book's primary strengths are the description of the workings of some major chess programs, an excellent review of tree searching methods, discussion of exciting new research ideas, a philosophical discussion of the relationship of computer game playing to artificial intelligence, and the treatment of computer Go as an important new research area. A complete index and extensive bibliography makes the book a valuable reference work. The book includes a special foreword by Ken Thompson, author of the UNIX operating system.
Highlighting and illustrating several important and interesting theoretical trends that have emerged in the continuing development of instructional technology, this book's organizational framework is based on the notion of two opposing camps. One evolves out of the intelligent tutoring movement, which employs artificial-intelligence technologies in the service of student modeling and precision diagnosis, and the other emerges from a constructivist/developmental perspective that promotes exploration and social interaction, but tends to reject the methods and goals of the student modelers. While the notion of opposing camps tends to create an artificial rift between groups of researchers, it represents a conceptual distinction that is inherently more interesting and informative than the relatively meaningless divide often drawn between "intelligent" and "unintelligent" instructional systems. An evident trend is that researchers in both "camps" view their computer learning environments as "cognitive tools" that can enhance learning, performance, and understanding. Cognitive tools are objects provided by the instructional environment that allow students to incorporate new auxiliary methods or symbols into their social problem solving which otherwise would be unavailable. A final section of the book represents researchers who are assimilating and accommodating the wisdom and creativity of their neighbors from both camps, perhaps forming the look of technology for the future. When the idea of model tracing in a computer-based environment is combined with appreciation for creative mind-extension cognitive tools and for how a community of learners can facilitate learning, a camp is created where AI technologists and social constructivist learning theorists can feel equally at home.
The basic questions addressed in this book are: what is the computational nature of cognition, and what role does it play in language and other mental processes?; What are the main characteristics of contemporary computational paradigms for describing cognition and how do they differ from each other?; What are the prospects for building cognition and how do they differ from each other?; and what are the prospects for building an artificial intelligence?
In a field choked with seemingly impenetrable jargon, Philip N. Johnson-Laird has done the impossible: written a book about how the mind works that requires no advance knowledge of artificial intelligence, neurophysiology, or psychology. The mind, he says, depends on the brain in the same way as the execution of a program of symbolic instructions depends on a computer, and can thus be understood by anyone willing to start with basic principles of computation and follow his step-by-step explanations. The author begins with a brief account of the history of psychology and the birth of cognitive science after World War II. He then describes clearly and simply the nature of symbols and the theory of computation, and follows with sections devoted to current computational models of how the mind carries out all its major tasks, including visual perception, learning, memory, the planning and control of actions, deductive and inductive reasoning, and the formation of new concepts and new ideas. Other sections discuss human communication, meaning, the progress that has been made in enabling computers to understand natural language, and finally the difficult problems of the conscious and unconscious mind, free will, needs and emotions, and self-awareness. In an envoi, the author responds to the critics of cognitive science and defends the computational view of the mind as an alternative to traditional dualism: cognitive science integrates mind and matter within the same explanatory framework. This first single-authored introduction to cognitive science will command the attention of students of cognitive science at all levels including psychologists, linguists, computer scientists, philosophers, and neuroscientists--as well as all readers curious about recent knowledge on how the mind works.
This book considers computer vision to be an integral part of the artificial intelligence system. The core of the book is an analysis of possible approaches to the creation of artificial vision systems, which simulate human visual perception. Much attention is paid to the latest achievements in visual psychology and physiology, the description of the functional and structural organization of the human perception mechanism, the peculiarities of artistic perception and the expression of reality. Computer vision models based on these data are investigated. They include the processes of external data analysis, internal environmental model synthesis, and the generating of behavioristic responses based on external and internal models comparison. Computer vision system evolution resulting from environmental effects is also considered. A unique feature of this book is the authors' use of black and white, and colour prints of traditional and contemporary Russian art to illustrate their principal theses. In doing so, they introduce the reader to a particularly Russian view of the world.
An argument for a non-Cartesian philosophical foundation for cognitive science that combines elements of Heideggerian phenomenology, a dynamical systems approach to cognition, and insights from artificial intelligence-related robotics.
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks