The big stories -- The skills of the new machines : technology races ahead -- Moore's law and the second half of the chessboard -- The digitization of just about everything -- Innovation : declining or recombining? -- Artificial and human intelligence in the second machine age -- Computing bounty -- Beyond GDP -- The spread -- The biggest winners : stars and superstars -- Implications of the bounty and the spread -- Learning to race with machines : recommendations for individuals -- Policy recommendations -- Long-term recommendations -- Technology and the future (which is very different from "technology is the future").
FINALIST FOR 2018 KIRKUS PRIZE NAMED ONE OF THE "BEST LITERARY FICTION OF 2018' BY KIRKUS REVIEWS "Sci-fi in its most perfect expression…Reading it is like having a lucid dream of six years from next week, filled with people you don't know, but will." —NPR "[Williams’s] wit is sharp, but her touch is light, and her novel is a winner." – San Francisco Chronicle "Between seasons of Black Mirror, look to Katie Williams' debut novel." —Refinery29 Smart and inventive, a page-turner that considers the elusive definition of happiness. Pearl's job is to make people happy. As a technician for the Apricity Corporation, with its patented happiness machine, she provides customers with personalized recommendations for greater contentment. She's good at her job, her office manager tells her, successful. But how does one measure an emotion? Meanwhile, there's Pearl's teenage son, Rhett. A sensitive kid who has forged an unconventional path through adolescence, Rhett seems to find greater satisfaction in being unhappy. The very rejection of joy is his own kind of "pursuit of happiness." As his mother, Pearl wants nothing more than to help Rhett--but is it for his sake or for hers? Certainly it would make Pearl happier. Regardless, her son is one person whose emotional life does not fall under the parameters of her job--not as happiness technician, and not as mother, either. Told from an alternating cast of endearing characters from within Pearl and Rhett's world, Tell the Machine Goodnight delivers a smartly moving and entertaining story about the advance of technology and the ways that it can most surprise and define us. Along the way, Katie Williams playfully illuminates our national obsession with positive psychology, our reliance on quick fixes. What happens when these obsessions begin to overlap? With warmth, humor, and a clever touch, Williams taps into our collective unease about the modern world and allows us see it a little more clearly.
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
MACHINE OF DEATH tells thirty-four different stories about people who know how they will die. Prepare to have your tears jerked, your spine tingled, your funny bone tickled, your mind blown, your pulse quickened, or your heart warmed. Or better yet, simply prepare to be surprised. Because even when people do have perfect knowledge of the future, there's no telling exactly how things will turn out.
“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.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
This is a comprehensive textbook catering for BTEC students at NIII and Higher National levels, advanced City and Guilds courses, and the early years of degree courses. It is also ideal for use in industrial retraining and post-experience programmes.
Sequel to the groundbreaking Machine Rendering, The Book of Iron showcases the latest and greatest work in mechanical rendering and design by some of the world's leading artists in the field. The book is conveniently divided into two sections: Automated Machines and Manned Machines. The first part covers robots, androids and other self powered technology. The second part includes spaceships, transport vehicles, weapons and equipment. With multi-angle concept sketches, models, details and interviews, unique access is granted to the creative process of these talented artists as they experiment to perfect their visions and breathe life into their creations utilizing a variety of materials and techniques.