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Author: Michael A. Arbib
Publisher: Springer Science & Business Media
ISBN: 1461247829
Category : Computers
Languages : en
Pages : 215
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Book Description
This is a book whose time has come-again. The first edition (published by McGraw-Hill in 1964) was written in 1962, and it celebrated a number of approaches to developing an automata theory that could provide insights into the processing of information in brainlike machines, making it accessible to readers with no more than a college freshman's knowledge of mathematics. The book introduced many readers to aspects of cybernetics-the study of computation and control in animal and machine. But by the mid-1960s, many workers abandoned the integrated study of brains and machines to pursue artificial intelligence (AI) as an end in itself-the programming of computers to exhibit some aspects of human intelligence, but with the emphasis on achieving some benchmark of performance rather than on capturing the mechanisms by which humans were themselves intelligent. Some workers tried to use concepts from AI to model human cognition using computer programs, but were so dominated by the metaphor "the mind is a computer" that many argued that the mind must share with the computers of the 1960s the property of being serial, of executing a series of operations one at a time. As the 1960s became the 1970s, this trend continued. Meanwhile, experi mental neuroscience saw an exploration of new data on the anatomy and physiology of neural circuitry, but little of this research placed these circuits in the context of overall behavior, and little was informed by theoretical con cepts beyond feedback mechanisms and feature detectors.
Author: Michael A. Arbib
Publisher: Springer Science & Business Media
ISBN: 1461247829
Category : Computers
Languages : en
Pages : 215
Get Book Here
Book Description
This is a book whose time has come-again. The first edition (published by McGraw-Hill in 1964) was written in 1962, and it celebrated a number of approaches to developing an automata theory that could provide insights into the processing of information in brainlike machines, making it accessible to readers with no more than a college freshman's knowledge of mathematics. The book introduced many readers to aspects of cybernetics-the study of computation and control in animal and machine. But by the mid-1960s, many workers abandoned the integrated study of brains and machines to pursue artificial intelligence (AI) as an end in itself-the programming of computers to exhibit some aspects of human intelligence, but with the emphasis on achieving some benchmark of performance rather than on capturing the mechanisms by which humans were themselves intelligent. Some workers tried to use concepts from AI to model human cognition using computer programs, but were so dominated by the metaphor "the mind is a computer" that many argued that the mind must share with the computers of the 1960s the property of being serial, of executing a series of operations one at a time. As the 1960s became the 1970s, this trend continued. Meanwhile, experi mental neuroscience saw an exploration of new data on the anatomy and physiology of neural circuitry, but little of this research placed these circuits in the context of overall behavior, and little was informed by theoretical con cepts beyond feedback mechanisms and feature detectors.
Author: Michael Anthony Arbib
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Michael A. Arbib
Publisher: New York : McGraw-Hill
ISBN:
Category : Brain
Languages : en
Pages : 172
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Book Description
Author: Edmund C. Berkeley
Publisher:
ISBN:
Category :
Languages : en
Pages : 290
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Book Description
Author: James A. Anderson
Publisher: Oxford University Press
ISBN: 019935779X
Category : Psychology
Languages : en
Pages : 401
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Book Description
Current computer technology doubles in in power roughly every two years, an increase called "Moore's Law." This constant increase is predicted to come to an end soon. Digital technology will change. Although digital computers dominate today's world, there are alternative ways to "compute" which might be better and more efficient than digital computation. After Digital looks at where the field of computation began and where it might be headed, and offers predictions about a collaborative future relationship between human cognition and mechanical computation. James A. Anderson, a pioneer of biologically inspired neural nets, presents two different kinds of computation-digital and analog--and gives examples of their history, function, and limitations. A third, the brain, falls somewhere in between these two forms, and is suggested as a computer architecture that is more capable of performing some specific important cognitive tasks-perception, reasoning, and intuition, for example- than a digital computer, even though the digital computer is constructed from far faster and more reliable basic elements. Anderson discusses the essentials of brain hardware, in particular, the cerebral cortex, and how cortical structure can influence the form taken by the computational operations underlying cognition. Topics include association, understanding complex systems through analogy, formation of abstractions, the biology of number and its use in arithmetic and mathematics, and computing across scales of organization. These applications, of great human interest, also form the goals of genuine artificial intelligence. After Digital will appeal to a broad cognitive science community, including computer scientists, philosophers, psychologists, and neuroscientists, as well as the curious science layreader, and will help to understand and shape future developments in computation.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Stanislas Dehaene
Publisher: Penguin
ISBN: 0525559906
Category : Science
Languages : en
Pages : 369
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Book Description
“There are words that are so familiar they obscure rather than illuminate the thing they mean, and ‘learning’ is such a word. It seems so ordinary, everyone does it. Actually it’s more of a black box, which Dehaene cracks open to reveal the awesome secrets within.”--The New York Times Book Review An illuminating dive into the latest science on our brain's remarkable learning abilities and the potential of the machines we program to imitate them The human brain is an extraordinary learning machine. Its ability to reprogram itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. But how do we learn? What innate biological foundations underlie our ability to acquire new information, and what principles modulate their efficiency? In How We Learn, Stanislas Dehaene finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain’s learning algorithms in our schools and universities, as well as in everyday life and at any age.
Author: Michael A. Arbib
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 488
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Book Description
This was begun as a revision of the 1972 classic The Metaphorical Brain, but quickly grew into an independent work. This new volume offers readers a timely, in-depth exploration of exciting research into the interplay between brain modeling, computer design and artificial intelligence.
Author: David Ruelle
Publisher: Princeton University Press
ISBN: 9780691129822
Category : Mathematics
Languages : en
Pages : 180
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Book Description
Examines mathematical ideas and the visionary minds behind them. This book provides an account of celebrated mathematicians and their quirks, oddities, personal tragedies, bad behavior, descents into madness, tragic ends, and the beauty of their mathematical discoveries.
Author: Michael A. Arbib
Publisher: MIT Press
ISBN: 0262018101
Category : Science
Languages : en
Pages : 677
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Book Description
A presentation of music and language within an integrative, embodied perspective of brain mechanisms for action, emotion, and social coordination. This book explores the relationships between language, music, and the brain by pursuing four key themes and the crosstalk among them: song and dance as a bridge between music and language; multiple levels of structure from brain to behavior to culture; the semantics of internal and external worlds and the role of emotion; and the evolution and development of language. The book offers specially commissioned expositions of current research accessible both to experts across disciplines and to non-experts. These chapters provide the background for reports by groups of specialists that chart current controversies and future directions of research on each theme. The book looks beyond mere auditory experience, probing the embodiment that links speech to gesture and music to dance. The study of the brains of monkeys and songbirds illuminates hypotheses on the evolution of brain mechanisms that support music and language, while the study of infants calibrates the developmental timetable of their capacities. The result is a unique book that will interest any reader seeking to learn more about language or music and will appeal especially to readers intrigued by the relationships of language and music with each other and with the brain. Contributors Francisco Aboitiz, Michael A. Arbib, Annabel J. Cohen, Ian Cross, Peter Ford Dominey, W. Tecumseh Fitch, Leonardo Fogassi, Jonathan Fritz, Thomas Fritz, Peter Hagoort, John Halle, Henkjan Honing, Atsushi Iriki, Petr Janata, Erich Jarvis, Stefan Koelsch, Gina Kuperberg, D. Robert Ladd, Fred Lerdahl, Stephen C. Levinson, Jerome Lewis, Katja Liebal, Jônatas Manzolli, Bjorn Merker, Lawrence M. Parsons, Aniruddh D. Patel, Isabelle Peretz, David Poeppel, Josef P. Rauschecker, Nikki Rickard, Klaus Scherer, Gottfried Schlaug, Uwe Seifert, Mark Steedman, Dietrich Stout, Francesca Stregapede, Sharon Thompson-Schill, Laurel Trainor, Sandra E. Trehub, Paul Verschure
