In this age of DNA computers and artificial intelligence, information is becoming disembodied even as the "bodies" that once carried it vanish into virtuality. While some marvel at these changes, envisioning consciousness downloaded into a computer or humans "beamed" Star Trek-style, others view them with horror, seeing monsters brooding in the machines. In How We Became Posthuman, N. Katherine Hayles separates hype from fact, investigating the fate of embodiment in an information age.

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.
How did cybernetics and information theory arise, and how did they come to dominate fields as diverse as engineering, biology, and the social sciences?

Winner of the CHOICE Outstanding Academic Title of the Choice ACRL

Outstanding Academic Title, Choice

Cybernetics—the science of communication and control as it applies to machines and to humans—originates from efforts during World War II to build automatic antiaircraft systems. Following the war, this science extended beyond military needs to examine all systems that rely on information and feedback, from the level of the cell to that of society. In The Cybernetics Moment, Ronald R. Kline, a senior historian of technology, examines the intellectual and cultural history of cybernetics and information theory, whose language of “information,” “feedback,” and “control” transformed the idiom of the sciences, hastened the development of information technologies, and laid the conceptual foundation for what we now call the Information Age.

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.

The most influential book of the past seventy-five years: a groundbreaking exploration of everything we know about what we don’t know, now with a new section called “On Robustness and Fragility.”

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 will change the way you look at the world, 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.

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. Elegant, startling, and universal in its applications, The Black Swan is a landmark book—itself a black swan.
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.
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence. This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.
"Uncommonly good...the most satisfying discussion to be found." — Scientific American.
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.

Technological systems become organized by commands from outside, as when human intentions lead to the building of structures or machines. But many nat ural systems become structured by their own internal processes: these are the self organizing systems, and the emergence of order within them is a complex phe nomenon that intrigues scientists from all disciplines. Unfortunately, complexity is ill-defined. Global explanatory constructs, such as cybernetics or general sys tems theory, which were intended to cope with complexity, produced instead a grandiosity that has now, mercifully, run its course and died. Most of us have become wary of proposals for an "integrated, systems approach" to complex matters; yet we must come to grips with complexity some how. Now is a good time to reexamine complex systems to determine whether or not various scientific specialties can discover common principles or properties in them. If they do, then a fresh, multidisciplinary attack on the difficulties would be a valid scientific task. Believing that complexity is a proper scientific issue, and that self-organizing systems are the foremost example, R. Tomovic, Z. Damjanovic, and I arranged a conference (August 26-September 1, 1979) in Dubrovnik, Yugoslavia, to address self-organizing systems. We invited 30 participants from seven countries. Included were biologists, geologists, physicists, chemists, mathematicians, bio physicists, and control engineers. Participants were asked not to bring manu scripts, but, rather, to present positions on an assigned topic. Any writing would be done after the conference, when the writers could benefit from their experi ences there.
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This Book
  • Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
  • Comprehensive practical solutions taking you into the future of machine learning
  • Go a step further and integrate your machine learning projects with Hadoop
Who This Book Is For

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 Learn
  • Implement a wide range of algorithms and techniques for tackling complex data
  • Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
  • Harness the capabilities of Spark and Hadoop to manage and process data successfully
  • Apply the appropriate machine learning technique to address real-world problems
  • Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning
  • Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more
In 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.

Cybernetics (loosely translated from the Greek): “a helmsman who steers his ship to port.” Psycho-Cybernetics is a term coined by Dr. Maxwell Maltz, which means, “steering your mind to a productive, useful goal so you can reach the greatest port in the world, peace of mind.”

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.
Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as:

• 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.

Application of New Cybernetics in Physics describes the application of new cybernetics to physical problems and the resolution of basic physical paradoxes by considering external observer influence. This aids the reader in solving problems that were solved incorrectly or have not been solved.

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 (observable dynamics).

(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 physics
  • Describes a wide class of molecular physics and kinetic problems to present semi-analytical and semi-qualitative calculations of solvation, flame propagation, and high-molecular formation
  • Demonstrates the effectiveness in application to complex molecular systems and other many-component objects
  • Includes numerous illustrations to support the text
Man-machine interaction is the interdisciplinary field, focused on a human and a machine in conjunction. It is the intersection of computer science, behavioural sciences, social psychology, ergonomics, security. It encompasses study, design, implementation, and evaluation of small- and large-scale, interacting, computing, hardware and software systems dedicated for human use. Man-machine interaction builds on supportive knowledge from both sides, the machine side providing techniques, methods and technologies relevant for computer graphics, visualisation, programming environments, the human side bringing elements of communication theory, linguistics, social sciences, models of behaviour. The discipline aims to improve ways in which machines and their users interact, making hardware and software systems better adapted to user's needs, more usable, more receptive, and optimised for desired properties.

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.

This book is concerned with important problems of robust (stable) statistical pat tern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which prin ciples and methods are constructed for classification and identification of objects, phenomena, processes, situations, and signals, i. e. , of objects that can be specified by a finite set of features, or properties characterizing the objects (Mathematical Encyclopedia (1984)). Two stages in development of the mathematical theory of pattern recognition may be observed. At the first stage, until the middle of the 1970s, pattern recogni tion theory was replenished mainly from adjacent mathematical disciplines: mathe matical statistics, functional analysis, discrete mathematics, and information theory. This development stage is characterized by successful solution of pattern recognition problems of different physical nature, but of the simplest form in the sense of used mathematical models. One of the main approaches to solve pattern recognition problems is the statisti cal approach, which uses stochastic models of feature variables. Under the statistical approach, the first stage of pattern recognition theory development is characterized by the assumption that the probability data model is known exactly or it is esti mated from a representative sample of large size with negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978), (Vasiljev, 1983)).
Emerging in the 1940s, the first cybernetics—the study of communication and control systems—was mainstreamed under the names artificial intelligence and computer science and taken up by the social sciences, the humanities, and the creative arts. In Emergence and Embodiment, Bruce Clarke and Mark B. N. Hansen focus on cybernetic developments that stem from the second-order turn in the 1970s, when the cyberneticist Heinz von Foerster catalyzed new thinking about the cognitive implications of self-referential systems. The crucial shift he inspired was from first-order cybernetics’ attention to homeostasis as a mode of autonomous self-regulation in mechanical and informatic systems, to second-order concepts of self-organization and autopoiesis in embodied and metabiotic systems. The collection opens with an interview with von Foerster and then traces the lines of neocybernetic thought that have followed from his work.

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

Today, we associate the relationship between feedback, control, and computing with Norbert Wiener's 1948 formulation of cybernetics. But the theoretical and practical foundations for cybernetics, control engineering, and digital computing were laid earlier, between the two world wars. In Between Human and Machine: Feedback, Control, and Computing before Cybernetics, David A. Mindell shows how the modern sciences of systems emerged from disparate engineering cultures and their convergence during World War II.

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

©2022 GoogleSite Terms of ServicePrivacyDevelopersAbout Google Play|Location: United StatesLanguage: English (United States)
By purchasing this item, you are transacting with Google Payments and agreeing to the Google Payments Terms of Service and Privacy Notice.