“The Knowledge Machine is the most stunningly illuminating book of the last several decades regarding the all-important scientific enterprise.” —Rebecca Newberger Goldstein, author of Plato at the Googleplex A paradigm-shifting work, The Knowledge Machine revolutionizes our understanding of the origins and structure of science. • Why is science so powerful? • Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe? In a groundbreaking work that blends science, philosophy, and history, leading philosopher of science Michael Strevens answers these challenging questions, showing how science came about only once thinkers stumbled upon the astonishing idea that scientific breakthroughs could be accomplished by breaking the rules of logical argument. Like such classic works as Karl Popper’s The Logic of Scientific Discovery and Thomas Kuhn’s The Structure of Scientific Revolutions, The Knowledge Machine grapples with the meaning and origins of science, using a plethora of vivid historical examples to demonstrate that scientists willfully ignore religion, theoretical beauty, and even philosophy to embrace a constricted code of argument whose very narrowness channels unprecedented energy into empirical observation and experimentation. Strevens calls this scientific code the iron rule of explanation, and reveals the way in which the rule, precisely because it is unreasonably close-minded, overcomes individual prejudices to lead humanity inexorably toward the secrets of nature. “With a mixture of philosophical and historical argument, and written in an engrossing style” (Alan Ryan), The Knowledge Machine provides captivating portraits of some of the greatest luminaries in science’s history, including Isaac Newton, the chief architect of modern science and its foundational theories of motion and gravitation; William Whewell, perhaps the greatest philosopher-scientist of the early nineteenth century; and Murray Gell-Mann, discoverer of the quark. Today, Strevens argues, in the face of threats from a changing climate and global pandemics, the idiosyncratic but highly effective scientific knowledge machine must be protected from politicians, commercial interests, and even scientists themselves who seek to open it up, to make it less narrow and more rational—and thus to undermine its devotedly empirical search for truth. Rich with illuminating and often delightfully quirky illustrations, The Knowledge Machine, written in a winningly accessible style that belies the import of its revisionist and groundbreaking concepts, radically reframes much of what we thought we knew about the origins of the modern world.

An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

Provides a wide-ranging survey of the sociolinguistic issues raised by the impact of information technology. The author demonstrates how and in which ways the new technologies both affect human communication and are in turn affected by the way people communicate using the technologies.

An examination of the ways that digital and networked technologies have fundamentally changed research practices in disciplines from astronomy to literary analysis. In Knowledge Machines, Eric Meyer and Ralph Schroeder argue that digital technologies have fundamentally changed research practices in the sciences, social sciences, and humanities. Meyer and Schroeder show that digital tools and data, used collectively and in distributed mode—which they term e-research—have transformed not just the consumption of knowledge but also the production of knowledge. Digital technologies for research are reshaping how knowledge advances in disciplines that range from physics to literary analysis. Meyer and Schroeder map the rise of digital research and offer case studies from many fields, including biomedicine, social science uses of the Web, astronomy, and large-scale textual analysis in the humanities. They consider such topics as the challenges of sharing research data and of big data approaches, disciplinary differences and new forms of interdisciplinary collaboration, the shifting boundaries between researchers and their publics, and the ways that digital tools promote openness in science. This book considers the transformations of research from a number of perspectives, drawing especially on the sociology of science and technology and social informatics. It shows that the use of digital tools and data is not just a technical issue; it affects research practices, collaboration models, publishing choices, and even the kinds of research and research questions scholars choose to pursue. Knowledge Machines examines the nature and implications of these transformations for scholarly research.

Provides a wide-ranging survey of the sociolinguistic issues raised by the impact of information technology. The author demonstrates how and in which ways the new technologies both affect human communication and are in turn affected by the way people communicate using the technologies.

This book delivers the scientific and mathematical basis to treat and process knowledge as a quantifiable and dimensioned entity. It provides the units and measures for the value of information contained in a "body of knowledge" that can be measured, processed, enhanced, communicated and preserved. It provides a basis to evaluate the quantity of knowledge acquired by students at various levels and in different universities. The effect of time on the dynamics and flow of knowledge is tied to Internet knowledge banks and provides the basis for designing and building the next generation of novel machine to appear in society. This book ties the basic needs of all human beings to the modern machines that resolve such need based on Internet knowledge banks (KBs) distributed throughout nations and societies. The features of the Intelligent Internet are fully exploited to make a new generation of students and knowledge workers use the knowledge resources elegantly and optimally. It deals with topics and insight into the design and architecture of next-generation computing systems that deal with human and social problems. Processor and Internet technologies that have already revolutionized human lives form the subject matter and the focal point of this book. Information and knowledge on the Internet delivered by next-generation mobile networks form the technical core presented. Human thought processes and adjustments follow the solutions offered by machines. - Extends the established practices and designs documented in computer systems to encompass the evolving knowledge processing field - Provides an academic and industrial viewpoint of the concurrent dynamic changes in computer and communication industries - Presents information for all perspectives, from managers, scientists and researchers - Basic concepts can be applied to other disciplines and situations

Learn and survive. Behind this simple equation lies a revolution in the study of knowledge, which has left the halls of philosophy for the labs of science. This book offers a cogent account of what such a move does to our understanding of the nature of learning, rationality, and intelligence. Bringing together evolutionary biology, psychology, and philosophy, Henry Plotkin presents a new science of knowledge, one that traces an unbreakable link between instinct and our ability to know. Contrary to the modern liberal idea that knowledge is something derived from experience, this science shows us that what we know is what our nature allows us to know, what our instincts tell us we must know. Since our ability to know our world depends primarily on what we call intelligence, intelligence must be understood as an extension of instinct. Drawing on contemporary evolutionary theory, especially notions of hierarchical structure and universal Darwinism, Plotkin tells us that the capacity for knowledge, which is what makes us human, is deeply rooted in our biology and, in a special sense, is shared by all living things. This leads to a discussion of animal and human intelligence as well as an appraisal of what an instinct-based capacity for knowledge might mean to our understanding of language, reasoning, emotion, and culture. The result is nothing less than a three-dimensional theory of our nature, in which all knowledge is adaptation and all adaptation is a specific form of knowledge.

We are so accustomed to use digital memories as data storage devices, that we are oblivious to the improbability of such a practice. Habit hides what we habitually use. To understand the worldwide success of archives and card indexing systems that allow to remember more because they allow to forget more than before, the evolution of scholarly practices and the transformation of cognitive habits in the early modern age must be investigated. This volume contains contributions by nearly every distinguished scholar in the field of early modern knowledge management and filing systems, and offers a remarkable synthesis of the present state of scholarship. A final section explores some current issues in record-keeping and note-taking systems, and provides valuable cues for future research.

An in-depth look at the ordinary and extraordinary things computers can do.

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.