But concern over what Louis Brandeis called the "curse of bigness" can no longer remain the province of specialist lawyers and economists, for it has spilled over into policy and politics, even threatening democracy itself. History suggests that tolerance of inequality and failing to control excessive corporate power may prompt the rise of populism, nationalism, extremist politicians, and fascist regimes. In short, as Wu warns, we are in grave danger of repeating the signature errors of the twentieth century.In The Curse of Bigness, Columbia professor Tim Wu tells of how figures like Brandeis and Theodore Roosevelt first confronted the democratic threats posed by the great trusts of the Gilded Age--but the lessons of the Progressive Era were forgotten in the last 40 years. He calls for recovering the lost tenets of the trustbusting age as part of a broader revival of American progressive ideas as we confront the fallout of persistent and extreme economic inequality.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    Norbert Wiener's classic is one in that small company. Founder of the science of cybernetics—the study of the relationship between computers and the human nervous system—Wiener was widely misunderstood as one who advocated the automation of human life. As this book reveals, his vision was much more complex and interesting. He hoped that machines would release people from relentless and repetitive drudgery in order to achieve more creative pursuits. At the same time he realized the danger of dehumanizing and displacement. His book examines the implications of cybernetics for education, law, language, science, technology, as he anticipates the enormous impact—in effect, a third industrial revolution—that the computer has had on our lives.

    Among them would be history makers, including the first American woman and the first African American in space. This assembly of astronauts would carry NASA through the most tumultuous years of the space shuttle program. Four would die on Challenger. USAF Colonel Mike Mullane was a member of this astronaut class, and Riding Rockets is his story -- told with a candor never before seen in an astronaut's memoir. Mullane strips the heroic veneer from the astronaut corps and paints them as they are -- human. His tales of arrested development among military flyboys working with feminist pioneers and post-doc scientists are sometimes bawdy, often hilarious, and always entertaining. Mullane vividly portrays every aspect of the astronaut experience -- from telling a female technician which urine-collection condom size is a fit; to walking along a Florida beach in a last, tearful goodbye with a spouse; to a wild, intoxicating, terrifying ride into space; to hearing "Taps" played over a friend's grave. Mullane is brutally honest in his criticism of a NASA leadership whose bungling would precipitate the Challenger disaster. Riding Rockets is a story of life in all its fateful uncertainty, of the impact of a family tragedy on a nine-year-old boy, of the revelatory effect of a machine called Sputnik, and of the life-steering powers of lust, love, and marriage. It is a story of the human experience that will resonate long after the call of "Wheel stop."

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    individual whose contributions to the scientific, business and cultural realms are extensive."—BookpageBefore slim laptops that fit into briefcases, computers looked like strange, alien vending machines. But in "the most staggering burst of technical invention by a single person in high-tech history" (BusinessWeek​) Steve Wozniak invented the first true personal computer. Wozniak teamed up with Steve Jobs, and Apple Computer was born, igniting the computer revolution and transforming the world. Here, thirty years later, the mischievous genius with the low profile treats readers to a rollicking, no-holds-barred account of his life—for once, in the voice of the wizard himself.

    Robots can climb stairs, open doors, win Jeopardy, analyze stocks, work in factories, find parking spaces, advise oncologists. In the past, automation was considered a threat to low-skilled labor. Now, many high-skilled functions, including interpreting medical images, doing legal research, and analyzing data, are within the skill sets of machines. How can higher education prepare students for their professional lives when professions themselves are disappearing? In Robot-Proof, Northeastern University president Joseph Aoun proposes a way to educate the next generation of college students to invent, to create, and to discover--to fill needs in society that even the most sophisticated artificial intelligence agent cannot.A "robot-proof" education, Aoun argues, is not concerned solely with topping up students' minds with high-octane facts. Rather, it calibrates them with a creative mindset and the mental elasticity to invent, discover, or create something valuable to society--a scientific proof, a hip-hop recording, a web comic, a cure for cancer. Aoun lays out the framework for a new discipline, humanics, which builds on our innate strengths and prepares students to compete in a labor market in which smart machines work alongside human professionals. The new literacies of Aoun's humanics are data literacy, technological literacy, and human literacy. Students will need data literacy to manage the flow of big data, and technological literacy to know how their machines work, but human literacy--the humanities, communication, and design--to function as a human being. Life-long learning opportunities will support their ability to adapt to change.The only certainty about the future is change. Higher education based on the new literacies of humanics can equip students for living and working through change.

    A young Puritan woman travels to the New World with her unwanted new husband. Alan Turing, the renowned mathematician and code breaker, writes letters to his best friend's mother. A Jewish refugee and professor of computer science struggles to reconnect with his increasingly detached wife. An isolated and traumatized young girl exchanges messages with an intelligent software program. A former Silicon Valley Wunderkind is imprisoned for creating illegal lifelike dolls.Each of these characters is attempting to communicate across gaps — to estranged spouses, lost friends, future readers, or a computer program that may or may not understand them. In dazzling and electrifying prose, Louisa Hall explores how the chasm between computer and human — shrinking rapidly with today's technological advances — echoes the gaps that exist between ordinary people. Though each speaks from a distinct place and moment in time, all five characters share the need to express themselves while simultaneously wondering if they will ever be heard, or understood.

    While nursing their wounds in Ibiza, they accidentally run into an eccentric character who tells them about a brand-new idea: cryptocurrency. Immersing themselves in what is then an obscure and sometimes sinister world, they begin to realize “crypto” is, in their own words, "either the next big thing or total bulls--t." There’s nothing left to do but make a bet.From the Silk Road to the halls of the Securities and Exchange Commission, Bitcoin Billionaires will take us on a wild and surprising ride while illuminating a tantalizing economic future. On November 26, 2017, the Winklevoss brothers became the first bitcoin billionaires. Here’s the story of how they got there—as only Ben Mezrich could tell it.

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    The technology is powerful but it is still a tool—one used by people to apply human ingenuity, imagination, and problem-solving skills to see trends, patterns, anomalies, and relationships in what were once inscrutable or unmanageable issues. In their years spent working with hundreds of companies, governments, and non-profit organizations, Josh Sullivan and Angela Zutavern have consulted with a wide range of leaders developing new capabilities that lead to new business models, the creation of breakthrough products and services, and potential solutions to vexing global problems. Their stories include Ford developing not just smarter cars but also smarter roads and cities; an oceanographer obtaining a holistic map of the oceans, with ramifications for both the fishing industry but for humanity at large; and health care entrepreneurs developing new products that significantly reduce heart attack fatalities.These are but a few examples of leaders tapping the power of the digital world and creatively collaborating with computers. New capabilities are developed that then give birth to new business models as leaders envision and shape the future. Businesses are reaching goals that until recently seemed difficult, if not impossible, to attain. The winnings will go to organizations that take steps to deliver "impossible strategies," and The Mathematical Corporation provides leaders with the new way to think and work in this era of data science and drive the revolution.

    Except the minor non-player character in the world Riley is building makes her own impossible decision—veering wildly off course and exploring the boundaries of the map. When the curious Riley extracts her code for closer examination, an emotional relationship develops between them. Soon Riley has all new plans for her spontaneous AI, including bringing Max into the real world. But what if Max has real-world plans of her own?Blake Crouch’s Summer Frost is part of Forward, a collection of six stories of the near and far future from out-of-this-world authors. Each piece can be read or listened to in a single thought-provoking sitting.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    The company was forced to write-down $900 million in inventory and Surface’s future was in jeopardy.Beneath A Surface tells the inside story of how Microsoft turned its hardware dreams into a reality with new details about the challenges Panos and his team had to overcome as well as the internal politics that nearly killed the brand.For fans of Microsoft and those who are interested in the business of building brands, Beneath A Surface is a must read that tells the inside story of how Microsoft turned a failure into a fortune.