Ancient Knowledge Networks is a book about how knowledge travels, in minds and bodies as well as in writings. It explores the forms knowledge takes and the meanings it accrues, and how these meanings are shaped by the peoples who use it.Addressing the relationships between political power, family ties, religious commitments and literate scholarship in the ancient Middle East of the first millennium BC, Eleanor Robson focuses on two regions where cuneiform script was the predominant writing medium: Assyria in the north of modern-day Syria and Iraq, and Babylonia to the south of modern-day Baghdad. She investigates how networks of knowledge enabled cuneiform intellectual culture to endure and adapt over the course of five world empires until its eventual demise in the mid-first century BC. In doing so, she also studies Assyriological and historical method, both now and over the past two centuries, asking how the field has shaped and been shaped by the academic concerns and fashions of the day. Above all, Ancient Knowledge Networks is an experiment in writing about ‘Mesopotamian science’, as it has often been known, using geographical and social approaches to bring new insights into the intellectual history of the world’s first empires.

Motivated by the explosion of molecular data on humans-particularly data associated with individual patients-and the sense that there are large, as-yet-untapped opportunities to use this data to improve health outcomes, Toward Precision Medicine explores the feasibility and need for "a new taxonomy of human disease based on molecular biology" and develops a potential framework for creating one. The book says that a new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients could drive the development of a more accurate classification of diseases and ultimately enhance diagnosis and treatment. The "new taxonomy" that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms. The book adds that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights. This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia. Toward Precision Medicine notes that moving toward individualized medicine requires that researchers and health care providers have access to very large sets of health- and disease-related data linked to individual patients. These data are also critical for developing the information commons, the knowledge network of disease, and ultimately the new taxonomy.

How does a telecommunications company function when its right hand often doesn't know what its left hand is doing? How do rapidly expanding, interdisciplinary organizations hold together and perform their knowledge work? In this book, Clay Spinuzzi draws on two warring theories of work activity - activity theory and actor-network theory - to examine the networks of activity that make a telecommunications company work and thrive. In doing so, Spinuzzi calls a truce between the two theories, bringing them to the negotiating table to parley about work. Specifically, about net work: the coordinative work that connects, coordinates, and stabilizes polycontextual work activities. To develop this uneasy dialogue, Spinuzzi examines the texts, trades, and technologies at play at Telecorp, both historically and empirically. Drawing on both theories, Spinuzzi provides new insights into how net work actually works and how our theories and research methods can be extended to better understand it.

Principles of Semantic Networks: Explorations in the Representation of Knowledge provides information pertinent to the theory and applications of semantic networks. This book deals with issues in knowledge representation, which discusses theoretical topics independent of particular implementations. Organized into three parts encompassing 19 chapters, this book begins with an overview of semantic network structure for representing knowledge as a pattern of interconnected nodes and arcs. This text then analyzes the concepts of subsumption and taxonomy and synthesizes a framework that integrates many previous approaches and goes beyond them to provide an account of abstract and partially defines concepts. Other chapters consider formal analyses, which treat the methods of reasoning with semantic networks and their computational complexity. This book discusses as well encoding linguistic knowledge. The final chapter deals with a formal approach to knowledge representation that builds on ideas originating outside the artificial intelligence literature in research on foundations for programming languages. This book is a valuable resource for mathematicians.

Knowledge Networking explains the strategic, organizational and human impact of technologies that support knowledge: the internet, groupware, collaborative technologies. It shows how they can transform organizational practices and help to improve both individual and team performances. Based on proven experience and includes customised toolkits, cases and action plans. From pooling expertise on a sales bid via computer referencing, to improving customer service using the flexible office, the author demonstrates how potential can become practice. Knowledge management is the big management idea currently influencing organizations, and Knowledge Networking explores the global impact of sharing knowledge and expertise. It is a highly practical text which includes customised toolkits, cases and action plans to enable individuals and teams to improve their performance.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

In today's de-layered, knowledge-intensive organizations, most work of importance is heavily reliant on informal networks of employees within organizations. However, most organizations do not know how to effectively analyze this informal structure in ways that can have a positive impact on organizational performance. Networks in the Knowledge Economy is a collection of readings on the application of social network analysis to managerial concerns. Social network analysis (SNA), a set of analytic tools that can be used to map networks of relationships, allows one to conduct very powerful assessments of information sharing within a network with relatively little effort. This approach makes the invisible web of relationships between people visible, helping managers make informed decisions for improving both their own and their group's performance. Networks in the Knowledge Economy is specifically concerned with networks inside of organizations and addresses three critical areas in the study of social networks: Social Networks as Important Individual and Organizational Assets, Social Network Implications for Knowledge Creation and Sharing, and Managerial Implications of Social Networks in Organizations. Professionals and students alike will find this book especially valuable, as it provides readings on the application of social network analysis that reflect managerial concerns.

Nagasaki during the Tokugawa (1603–1868) was truly Japan's window on the world with its Chinese residences and Deshima island, where Western foreigners, including representatives of the Dutch East India Company, were confined. In 1785 Ōtsuki Gentaku (1757–1827) journeyed from the capital to Nagasaki to meet Dutch physicians and the Japanese who acted as their interpreters. Gentaku was himself a physician, but he was also a Dutch studies (rangaku) scholar who passionately believed that European science and medicine were critical to Japan's progress. Network of Knowledge examines the development of Dutch studies during the crucial years 1770–1830 as Gentaku, with the help of likeminded colleagues, worked to facilitate its growth, creating a school, participating in and hosting scholarly and social gatherings, and circulating books. In time the modest, informal gatherings of Dutch studies devotees (rangakusha), mostly in Edo and Nagasaki, would grow into a pan-national society. Applying ideas from social network theory and Bourdieu's conceptions of habitus, field, and capital, this volume shows how Dutch studies scholars used networks to grow their numbers and overcome government indifference to create a dynamic community. The social significance of rangakusha, as much as the knowledge they pursued in medicine, astronomy, cartography, and military science, was integral to the creation of a Tokugawa information revolution—one that saw an increase in information gathering among all classes and innovative methods for collecting and storing that information. Although their salons were not as politically charged as those of their European counterparts, rangakusha were subversive in their decision to include scholars from a wide range of socio-economic backgrounds. They created a cultural society of civility and play in which members worked toward a common cultural goal. This insightful study reveals the strength of the community's ties as it follows rangakusha into the Meiji era (1868–1912), when a new generation championed values and ambitions similar to those of Gentaku and his peers. Network of Knowledge offers a fresh look at the cultural and intellectual environment of the late Tokugawa that will be welcomed by scholars and students of Japanese intellectual and social history.

This book discusses a core question in many fields of the social sciences, namely how to create, share and adopt new knowledge. It creates an original space for conversation between two lines of research that have developed largely in parallel for a long time: social network theory and the geography of knowledge. This book considers that relational thinking has become increasingly important for scholars to capture societal outcomes by studying social relations and networks, whereas the role of place, space and spatial scales has been somewhat neglected outside an emergent geography of knowledge. The individual contributions help integrate network arguments of connectivity, geographical arguments of contiguity and contextuality into a more comprehensive understanding of the ways in which people and organizations are constrained by and make use of space and networks for learning and innovation. Experts in the fields of geography, sociology, economics, political science, psychology, management and organizational studies develop conceptual models and propose empirical research that illustrates the ways in which networks and geography play together in processes of innovation, learning, leadership, and power. This book is open access under a CC BY 4.0 license.

Knowledge Management has evolved into one of the most important streams of management research, affecting organizations of all types at many different levels. The Encyclopedia of Knowledge Management, Second Edition provides a compendium of terms, definitions and explanations of concepts, processes and acronyms addressing the challenges of knowledge management. This two-volume collection covers all aspects of this critical discipline, which range from knowledge identification and representation, to the impact of Knowledge Management Systems on organizational culture, to the significant integration and cost issues being faced by Human Resources, MIS/IT, and production departments.