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Knowledge engineering deals with the development of information systems in which knowledge and reasoning play pivotal roles. A newly-developed field at the intersection of computer science and management, it deals with knowledge as a key resource in modern organizations.
The disciplines of knowledge engineering and knowledge management are closely tied. Knowledge engineering deals with the development of information systems in which knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with knowledge as a key resource in modern organizations. Managing knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges.
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.
How engineered materials and machines powered by living biological cells can tackle technological challenges in medicine, agriculture, and global security. You are a biological machine whose movement is powered by skeletal muscle, just as a car is a machine whose movement is powered by an engine. If you can be built from the bottom up with biological materials, other machines can be as well. This is the conceptual starting point for biofabrication, the act of building with living cells--building with biology in the same way we build with synthetic materials. In this volume in the MIT Press Essential Knowledge series, Ritu Raman offers an accessible introduction to biofabrication, arguing that it can address some of our greatest technological challenges. After presenting the background information needed to understand the emergence and evolution of biofabrication and describing the fundamental technology that enables building with biology, Raman takes deep dives into four biofabrication applications that have the potential to affect our daily lives: tissue engineering, organs-on-a-chip, lab-grown meat and leather, and biohybrid machines. Organs-on-a-chip (devices composed of miniature model tissues), for example, could be used to test new medicine and therapies, and lab-grown meat could alleviate environmental damage done by animal farming. She shows that biological materials have abilities synthetic materials do not, including the ability to adapt dynamically to their environments. Exploring the principles of biofabrication, Raman tells us, should help us appreciate the beauty, adaptiveness, and persistence of the biological machinery that drives our bodies and our world.
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
An introduction to computational thinking that traces a genealogy beginning centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, “computational thinking” has become part of the K–12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it. The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as “computers”) who performed complex calculations as teams engaged in CT long before electronic computers. The authors identify six dimensions of today's highly developed CT—methods, machines, computing education, software engineering, computational science, and design—and cover each in a chapter. Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.
Discover the MIT-developed, “doer’s approach” to innovation with this guide that reveals you don’t need an earth-shattering idea to create a standout product, service, or business—just a hunch that you can scale up to impact. Innovation is the subject of countless books and courses, but there’s very little out there about how you actually innovate. Innovation and entrepreneurship are not one and the same, although aspiring innovators often think of them that way. They are told to get an idea and a team and to build a show-and-tell for potential investors. In Innovating, Luis Perez-Breva describes another approach—a doer’s approach developed over a decade at MIT and internationally in workshops, classes, and companies. He shows that innovating doesn’t require an earth-shattering idea; all it takes is a hunch. Anyone can do it. By prototyping a problem and learning by being wrong, innovating can be scaled up to make an impact. As Perez-Breva demonstrates, “nothing is new” at the outset of what we only later celebrate as innovation. In Innovating, the process—illustrated by unique and dynamic artwork—is shown to be empirical, experimental, nonlinear, and incremental. You give your hunch the structure of a problem. Anything can be a part. Your innovating accrues other people’s knowledge and skills. Perez-Breva describes how to create a kit for innovating, and outlines questions that will help you think in new ways. Finally, he shows how to systematize what you’ve learned: to advocate, communicate, scale up, manage innovating continuously, and document—“you need a notebook to converse with yourself,” he advises. Everyone interested in innovating also needs to read this book.
How companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like."
The future of the university as an open knowledge institution that institutionalizes diversity and contributes to a common resource of knowledge: a manifesto. In this book, a diverse group of authors—including open access pioneers, science communicators, scholars, researchers, and university administrators—offer a bold proposition: universities should become open knowledge institutions, acting with principles of openness at their center and working across boundaries and with broad communities to generate shared knowledge resources for the benefit of humanity. Calling on universities to adopt transparent protocols for the creation, use, and governance of these resources, the authors draw on cutting-edge theoretical work, offer real-world case studies, and outline ways to assess universities’ attempts to achieve openness. Digital technologies have already brought about dramatic changes in knowledge format and accessibility. The book describes further shifts that open knowledge institutions must make as they move away from closed processes for verifying expert knowledge and toward careful, mediated approaches to sharing it with wider publics. It examines these changes in terms of diversity, coordination, and communication; discusses policy principles that lay out paths for universities to become fully fledged open knowledge institutions; and suggests ways that openness can be introduced into existing rankings and metrics. Case studies—including Wikipedia, the Library Publishing Coalition, Creative Commons, and Open and Library Access—illustrate key processes.
A textbook that introduces integrated, sustainable design of urban infrastructures, drawing on civil engineering, environmental engineering, urban planning, electrical engineering, mechanical engineering, and computer science. This textbook introduces urban infrastructure from an engineering perspective, with an emphasis on sustainability. Bringing together both fundamental principles and practical knowledge from civil engineering, environmental engineering, urban planning, electrical engineering, mechanical engineering, and computer science, the book transcends disciplinary boundaries by viewing urban infrastructures as integrated networks. The text devotes a chapter to each of five engineering systems—electricity, water, transportation, buildings, and solid waste—covering such topics as fundamentals, demand, management, technology, and analytical models. Other chapters present a formal definition of sustainability; discuss population forecasting techniques; offer a history of urban planning, from the Neolithic era to Kevin Lynch and Jane Jacobs; define and discuss urban metabolism and infrastructure integration, reviewing system interdependencies; and describe approaches to urban design that draw on complexity theory, algorithmic models, and machine learning. Throughout, a hypothetical city state, Civitas, is used to explain and illustrate the concepts covered. Each chapter includes working examples and problem sets. An appendix offers tables, diagrams, and conversion factors. The book can be used in advanced undergraduate and graduate courses in civil engineering and as a reference for practitioners. It can also be helpful in preparation for the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) exams.