Hayles relates three interwoven stories: how information lost its body, that is, how it came to be conceptualized as an entity separate from the material forms that carry it; the cultural and technological construction of the cyborg; and the dismantling of the liberal humanist "subject" in cybernetic discourse, along with the emergence of the "posthuman."
Ranging widely across the history of technology, cultural studies, and literary criticism, Hayles shows what had to be erased, forgotten, and elided to conceive of information as a disembodied entity. Thus she moves from the post-World War II Macy Conferences on cybernetics to the 1952 novel Limbo by cybernetics aficionado Bernard Wolfe; from the concept of self-making to Philip K. Dick's literary explorations of hallucination and reality; and from artificial life to postmodern novels exploring the implications of seeing humans as cybernetic systems.
Although becoming posthuman can be nightmarish, Hayles shows how it can also be liberating. From the birth of cybernetics to artificial life, How We Became Posthuman provides an indispensable account of how we arrived in our virtual age, and of where we might go from here.
Kline argues that, for about twenty years after 1950, the growth of cybernetics and information theory and ever-more-powerful computers produced a utopian information narrative—an enthusiasm for information science that influenced natural scientists, social scientists, engineers, humanists, policymakers, public intellectuals, and journalists, all of whom struggled to come to grips with new relationships between humans and intelligent machines.
Kline traces the relationship between the invention of computers and communication systems and the rise, decline, and transformation of cybernetics by analyzing the lives and work of such notables as Norbert Wiener, Claude Shannon, Warren McCulloch, Margaret Mead, Gregory Bateson, and Herbert Simon. Ultimately, he reveals the crucial role played by the cybernetics moment—when cybernetics and information theory were seen as universal sciences—in setting the stage for our current preoccupation with information technologies.-- Gregory J. Downey, University of Wisconsin'
A black swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. The astonishing success of Google was a black swan; so was 9/11. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions to events in our own personal lives.
Why do we not acknowledge the phenomenon of black swans until after they occur? Part of the answer, according to Taleb, is that humans are hardwired to learn specifics when they should be focused on generalities. We concentrate on things we already know and time and time again fail to take into consideration what we don’t know. We are, therefore, unable to truly estimate opportunities, too vulnerable to the impulse to simplify, narrate, and categorize, and not open enough to rewarding those who can imagine the “impossible.”
For years, Taleb has studied how we fool ourselves into thinking we know more than we actually do. We restrict our thinking to the irrelevant and inconsequential, while large events continue to surprise us and shape our world. In this revelatory book, Taleb explains everything we know about what we don’t know, and this second edition features a new philosophical and empirical essay, “On Robustness and Fragility,” which offers tools to navigate and exploit a Black Swan world.
Elegant, startling, and universal in its applications, The Black Swan will change the way you look at the world. Taleb is a vastly entertaining writer, with wit, irreverence, and unusual stories to tell. He has a polymathic command of subjects ranging from cognitive science to business to probability theory. The Black Swan is a landmark book—itself a black swan.
Praise for Nassim Nicholas Taleb
“The most prophetic voice of all.”—GQ
Praise for The Black Swan
“[A book] that altered modern thinking.”—The Times (London)
“A masterpiece.”—Chris Anderson, editor in chief of Wired, author of The Long Tail
“Idiosyncratically brilliant.”—Niall Ferguson, Los Angeles Times
“The Black Swan changed my view of how the world works.”—Daniel Kahneman, Nobel laureate
“[Taleb writes] in a style that owes as much to Stephen Colbert as it does to Michel de Montaigne. . . . We eagerly romp with him through the follies of confirmation bias [and] narrative fallacy.”—The Wall Street Journal
“Hugely enjoyable—compelling . . . easy to dip into.”—Financial Times
“Engaging . . . The Black Swan has appealing cheek and admirable ambition.”—The New York Times Book Review
Behind the familiar surfaces of the telephone, radio, and television lies a sophisticated and intriguing body of knowledge known as information theory. This is the theory that has permitted the rapid development of all sorts of communication, from color television to the clear transmission of photographs from the vicinity of Jupiter. Even more revolutionary progress is expected in the future.
To give a solid introduction to this burgeoning field, J. R. Pierce has revised his well-received 1961 study of information theory for a second edition. Beginning with the origins of the field, Dr. Pierce follows the brilliant formulations of Claude Shannon and describes such aspects of the subject as encoding and binary digits, entropy, language and meaning, efficient encoding, and the noisy channel. He then goes beyond the strict confines of the topic to explore the ways in which information theory relates to physics, cybernetics, psychology, and art. Mathematical formulas are introduced at the appropriate points for the benefit of serious students. A glossary of terms and an appendix on mathematical notation are proved to help the less mathematically sophisticated.
J. R. Pierce worked for many years at the Bell Telephone Laboratories, where he became Director of Research in Communications Principles. His Introduction to Information Theory continues to be the most impressive nontechnical account available and a fascinating introduction to the subject for lay readers.
Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.
According to an article in The New York Times , the course on artificial intelligence is “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom.
Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®.
To learn more about the course on artificial intelligence, visit http://www.ai-class.com. To read the full New York Times article, click here.
The book begins with a discussion of the nature of cybernetics and its methods. Separate chapters cover the use of cybernetics in the field of biological development; previous work in the area of cybernetics related to automata theory; and the application of information theory to development. Subsequent chapters present models of development. These include computer programs which continually replicate themselves and control the resulting development; growing automata nets as models of development; and a method that allows a system to control the relative sizes of its parts during development and afterwards during regeneration.
This book provides enough background material to make it understandable both to the biologist with little knowledge of cybernetics and the cybernetician with no great knowledge of developmental biology.
This book constitutes the refereed proceedings of the Cybernetics and Mathematics Applications in Intelligent Systems Section of the 6th Computer Science On-line Conference 2017 (CSOC 2017), held in April 2017.
The Cybernetic Brain explores a largely forgotten group of British thinkers, including Grey Walter, Ross Ashby, Gregory Bateson, R. D. Laing, Stafford Beer, and Gordon Pask, and their singular work in a dazzling array of fields. Psychiatry, engineering, management, politics, music, architecture, education, tantric yoga, the Beats, and the sixties counterculture all come into play as Pickering follows the history of cybernetics’ impact on the world, from contemporary robotics and complexity theory to the Chilean economy under Salvador Allende. What underpins this fascinating history, Pickering contends, is a shared but unconventional vision of the world as ultimately unknowable, a place where genuine novelty is always emerging. And thus, Pickering avers, the history of cybernetics provides us with an imaginative model of open-ended experimentation in stark opposition to the modern urge to achieve domination over nature and each other.
This profound and elegant book derives both its title and its outlook from Samuel Butler’s 1863 essay, “Darwin Among the Machines.” Observing the beginnings of miniaturization, self-reproduction, and telecommunication among machines, Butler predicted that nature’s intelligence, only temporarily subservient to technology, would resurface to claim our creations as her own. Updating Butler’s arguments, Dyson distills the historical record to chronicle the origins of digital telecommunications and the evolution of digital computers, beginning long before the time of Darwin and exploring the limits of Darwinian evolution to suggest what lies beyond. Weaving a cohesive narrative of his brilliant predecessors, Dyson constructs a straightforward, convincing, and occasionally frightening view of the evolution of mind in the global network, on a level transcending our own. Dyson concludes that we are in the midst of an experiment that echoes the prehistory of human intelligence and the origins of life.
Just as the exchange of coded molecular instructions brought life as we know it to the early earth’s primordial soup, and as language and mind combined to form the culture in which we live, so, in the digital universe, are computer programs and worldwide networks combining to produce an evolutionary theater in which the distinctions between nature and technology are increasingly obscured. Nature, argues Dyson, is on the side of the machines.
It brings new approaches and methods to real-world problems and exploratory research that describes novel approaches in the cybernetics, algorithms and software engineering in the scope of the intelligent systems.
This book constitutes the refereed proceedings of the Computational Methods in Systems and Software 2017, a conference that provided an international forum for the discussion of the latest high-quality research results in all areas related to computational methods, statistics, cybernetics and software engineering.
This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.What You Will LearnImplement a wide range of algorithms and techniques for tackling complex dataGet to grips with some of the most powerful languages in data science, including R, Python, and JuliaHarness the capabilities of Spark and Hadoop to manage and process data successfullyApply the appropriate machine learning technique to address real-world problemsGet acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learningExplore the future of machine learning and dive deeper into polyglot persistence, semantic data, and moreIn Detail
Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development.
This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data.
This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application.
With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data.
You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.Style and approach
A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
IMA - Institute of Information Management in Mechanical Engineering
ZLW - Center for Learning and Knowledge Management
IfU - Associated Institute for Management Cybernetics e.V. Faculty of Mechanical Engineering, RWTH Aachen University
The book presents a range of innovative fields of application, including: cognitive systems, cyber-physical production systems, robotics, automation technology, machine learning, natural language processing, data mining, predictive data analytics, visual analytics, innovation and diversity management, demographic models, virtual and remote laboratories, virtual and augmented realities, multimedia learning environments, organizational development and management cybernetics. The contributions selected reflect the fundamental paradigm shift toward an increasingly interdisciplinary research world – which has always been both the basis and spirit of the institute cluster IMA/ZLW & IfU.
Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
Since its first publication in 1960, Maltz’s landmark bestseller has inspired and enhanced the lives of more than 30 million readers. In this updated edition, with a new introduction and editorial commentary by Matt Furey, president of the Psycho-Cybernetics Foundation, the original text has been annotated and amplified to make Maltz’s message even more relevant for the contemporary reader.
“Before the mind can work efficiently, we must develop our perception of the outcomes we expect to reach. Maxwell Maltz calls this Psycho-Cybernetics; when the mind has a defined target it can focus and direct and refocus and redirect until it reaches its intended goal.” —Tony Robbins (from Unlimited Power)
Maltz was the first researcher and author to explain how the self-image (a term he popularized) has complete control over an individual’s ability to achieve (or fail to achieve) any goal. And he developed techniques for improving and managing self-image—visualization, mental rehearsal, relaxation—which have informed and inspired countless motivational gurus, sports psychologists, and self-help practitioners for more than fifty years.
The teachings of Psycho-Cybernetics are timeless because they are based on solid science and provide a prescription for thinking and acting that lead to quantifiable results.
A Huffington Post Definitive Tech Book of 2013
Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence.
In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
Designing Freedom ponders the possibilities of liberty in a cybernetic world.
As lives offline and online merge even more, it is easy to forget how we got here. Rise of the Machines reclaims the spectacular story of cybernetics, one of the twentieth century’s pivotal ideas.
Springing from the mind of mathematician Norbert Wiener amid the devastation of World War II, the cybernetic vision underpinned a host of seductive myths about the future of machines. Cybernetics triggered blissful cults and military gizmos, the Whole Earth Catalog and the air force’s foray into virtual space, as well as crypto-anarchists fighting for internet freedom.
In Rise of the Machines, Thomas Rid draws on unpublished sources—including interviews with hippies, anarchists, sleuths, and spies—to offer an unparalleled perspective into our anxious embrace of technology.
• How does a machine learn a new concept on the basis of examples?
• How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input?
• How much training is required to achieve a specified level of accuracy in the prediction?
• How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time?
In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics.
This second edition extends and improves upon this material, covering new areas including:
• Support vector machines.
• Fat-shattering dimensions and applications to neural network learning.
• Learning with dependent samples generated by a beta-mixing process.
• Connections between system identification and learning theory.
• Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm.
Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added.
Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist.
The book presents a detailed analysis focusing on the modern trends of research in cybernetics. A new development stage of cybernetics (the so-called cybernetics 2.0) is discussed as a science on general regularities of systems organization and control. The author substantiates the topicality of elaborating a new branch of cybernetics, i.e. organization theory which studies an organization as a property, process and system.
The book is intended for theoreticians and practitioners, as well as for students, postgraduates and doctoral candidates. In the first place, the target audience includes tutors and lecturers preparing courses on cybernetics, control theory and systems science.
Three groups of problems of the new cybernetics are considered in the book:
(a) Systems that can be calculated based on known physics of subsystems. This includes the external observer influence calculated from basic physical laws (ideal dynamics) and dynamics of a physical system influenced even by low noise.
(b) Emergent systems. This includes external noise from the observer by using the black box model (complex dynamics), external noise from the observer by using the observer’s intuition (unpredictable dynamics), defining boundaries of application of scientific methods for system behavior prediction, and the role of the observer’s intuition for unpredictable systems.
(c) Methods for solution of basic physical paradoxes by using methods of the new cybernetics: the entropy increase paradox, Schrödinger’s cat paradox (wave package reduction in quantum mechanics), the black holes information paradox, and the time wormholes grandfather paradox. All of the above paradoxes have the same resolution based on the principles of new cybernetics. Indeed, even a small interaction of an observer with an observed system results in their time arrows’ alignment (synchronization) and results in the paradox resolution and appearance of the universal time arrow.Provides solutions to the basic physical paradoxes and demonstrates their practical actuality for modern physicsDescribes a wide class of molecular physics and kinetic problems to present semi-analytical and semi-qualitative calculations of solvation, flame propagation, and high-molecular formationDemonstrates the effectiveness in application to complex molecular systems and other many-component objectsIncludes numerous illustrations to support the text
This monograph is the second edition in the series, providing the reader with a selection of high-quality papers dedicated to current progress, new developments and research trends in man-machine interactions area. In particular, the topical subdivisions of this volume include human-computer interfaces, robot control and navigation systems, bio-data analysis and mining, pattern recognition for medical applications, sound, text and image processing, design and decision support, rough and fuzzy systems, crisp and fuzzy clustering, prediction and regression, algorithms and optimisation, and data management systems.
Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include:how intelligence can be defined whether machines can 'think' sensory input in machine systems the nature of consciousness the controversial culturing of human neurons.
Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.
In response to the apparent dissolution of boundaries at work in the contemporary technosciences of emergence, neocybernetics observes that cognitive systems are operationally bounded, semi-autonomous entities coupled with their environments and other systems. Second-order systems theory stresses the recursive complexities of observation, mediation, and communication. Focused on the neocybernetic contributions of von Foerster, Francisco Varela, and Niklas Luhmann, this collection advances theoretical debates about the cultural, philosophical, and literary uses of their ideas. In addition to the interview with von Foerster, Emergence and Embodiment includes essays by Varela and Luhmann. It engages with Humberto Maturana’s and Varela’s creation of the concept of autopoiesis, Varela’s later work on neurophenomenology, and Luhmann’s adaptations of autopoiesis to social systems theory. Taken together, these essays illuminate the shared commitments uniting the broader discourse of neocybernetics.
Contributors. Linda Brigham, Bruce Clarke, Mark B. N. Hansen, Edgar Landgraf, Ira Livingston, Niklas Luhmann, Hans-Georg Moeller, John Protevi, Michael Schiltz, Evan Thompson, Francisco J. Varela, Cary Wolfe
Mindell examines four different arenas of control systems research in the United States between the world wars: naval fire control, the Sperry Gyroscope Company, the Bell Telephone Laboratories, and Vannevar Bush's laboratory at MIT. Each of these institutional sites had unique technical problems, organizational imperatives, and working environments, and each fostered a distinct engineering culture. Each also developed technologies to represent the world in a machine.
At the beginning of World War II, President Roosevelt established the National Defense Research Committee, one division of which was devoted to control systems. Mindell shows how the NDRC brought together representatives from the four pre-war engineering cultures, and how its projects synthesized conceptions of control, communications, and computing. By the time Wiener articulated his vision, these ideas were already suffusing through engineering. They would profoundly influence the digital world.
As a new way to conceptualize the history of computing, this book will be of great interest to historians of science, technology, and culture, as well as computer scientists and theorists. Between Human and Machine: Feedback, Control, and Computing before Cybernetics
This wickedly inventive guide offers 19 build-it-yourself projects featuring high-tech devices that can map, manipulate, and even improve the greatest computer on earth-the human brain. Every project inside Mind Performance Projects for the Evil Genius is perfectly safe and explores cutting-edge concepts, such as brain wave mapping, lucid dream control, and hypnosis.
Using easy-to-find parts and tools, this do-it-yourself book offers a wide variety of brain-bending bio hacks you can accomplish on your own. You'll find detailed guidelines, parameters, schematics, code, and customization tips for each project in the book. The only limit is your imagination!
Mind Performance Projects for the Evil Genius:Features step-by-step instructions, complete with helpful illustrations Allows you to customize each project for your purposes Discusses the underlying principles behind the projects Removes the frustration factor-all required parts are listed, along with sources
Build these and other lid-flipping gadgets:Biofeedback device Reaction speedometer Body temperature monitor Heart rate monitor Lie detector White noise generator Waking reality tester Audio dream director Lucid dream mask Alpha meditation goggles Clairvoyance tester Visual hypnosis aid Color therapy device Synchro brain machine
Author Leon Brillouin begins by defining and applying the term "information" and proceeds to explorations of the principles of coding, coding problems and solutions, the analysis of signals, a summary of thermodynamics, thermal agitation and Brownian motion, and thermal noise in an electric circuit. A discussion of the negentropy principle of information introduces Brillouin's renowned examination of Maxwell's demon. Concluding chapters explore the associations between information theory, the uncertainty principle, and physical limits of observation, in addition to problems related to computing, organizing information, and inevitable errors.