And considering that the "one man" is Neal Stephenson, "the hacker Hemingway" (Newsweek) -- acclaimed novelist, pragmatist, seer, nerd-friendly philosopher, and nationally bestselling author of groundbreaking literary works (Snow Crash, Cryptonomicon, etc., etc.) -- the word is well worth hearing. Mostly well-reasoned examination and partial rant, Stephenson's In the Beginning... was the Command Line is a thoughtful, irreverent, hilarious treatise on the cyber-culture past and present; on operating system tyrannies and downloaded popular revolutions; on the Internet, Disney World, Big Bangs, not to mention the meaning of life itself.

    It doesn't shy away from the full range of viewpoints or from the most controversial issues--from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    The data compiled and portraits created are incredibly detailed, to the point of being invasive. But who connects the dots about what firms are doing with this information? The Black Box Society argues that we all need to be able to do so--and to set limits on how big data affects our lives.Hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only scratched the surface of this troubling behavior.Frank Pasquale exposes how powerful interests abuse secrecy for profit and explains ways to rein them in. Demanding transparency is only the first step. An intelligible society would assure that key decisions of its most important firms are fair, nondiscriminatory, and open to criticism. Silicon Valley and Wall Street need to accept as much accountability as they impose on others.

    With all our media now traveling a single network, an unprecedented potential is building for centralized control over what Americans see and hear. Could history repeat itself with the next industrial consolidation? Could the Internet—the entire flow of American information—come to be ruled by one corporate leviathan in possession of “the master switch”? That is the big question of Tim Wu’s pathbreaking book.As Wu’s sweeping history shows, each of the new media of the twentieth century—radio, telephone, television, and film—was born free and open. Each invited unrestricted use and enterprising experiment until some would-be mogul battled his way to total domination. Here are stories of an uncommon will to power, the power over information: Adolph Zukor, who took a technology once used as commonly as YouTube is today and made it the exclusive prerogative of a kingdom called Hollywood . . . NBC’s founder, David Sarnoff, who, to save his broadcast empire from disruptive visionaries, bullied one inventor (of electronic television) into alcoholic despair and another (this one of FM radio, and his boyhood friend) into suicide . . . And foremost, Theodore Vail, founder of the Bell System, the greatest information empire of all time, and a capitalist whose faith in Soviet-style central planning set the course of every information industry thereafter.Explaining how invention begets industry and industry begets empire—a progress often blessed by government, typically with stifling consequences for free expression and technical innovation alike—Wu identifies a time-honored pattern in the maneuvers of today’s great information powers: Apple, Google, and an eerily resurgent AT&T. A battle royal looms for the Internet’s future, and with almost every aspect of our lives now dependent on that network, this is one war we dare not tune out.Part industrial exposé, part meditation on what freedom requires in the information age, The Master Switch is a stirring illumination of a drama that has played out over decades in the shadows of our national life and now culminates with terrifying implications for our future.

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    And they did it without fanfare or recognition from their employer. Hiltzik's Dealers of Lightning provides a fascinating look at technohistory that sets the record straight. In Dealers of Lightning, Hiltzik describes the forces and faces behind the revolution that the Xerox PARC team single-handedly spawned. The Xerox PARC group was composed solely of top technical minds. The decision was made at Xerox headquarters to give the team complete freedom from deadlines and directives, in hopes of fostering a true creative environment. It worked — perhaps too well. The team responded with a steady output of amazing technology, including the first version of the Internet, the first personal computer, user-friendly word-processing programs, and pop-up menus. Xerox, far from ready for the explosion of innovation, failed to utilize the technology dreamed up by the group. Out of all the dazzling inventions born at Xerox PARC, only a handful were developed and marketed by Xerox. However, one of these inventions, the laser printer, proved successful enough to earn billions for the company, therefore justifying its investment in the research center. Most oftheteam's creations would go on to be developed and perfected by other companies, such as IBM, Apple, and Microsoft. Drawing from interviews with the engineers, executives, and scientists involved in the Xerox PARC, Dealers of Lightning chronicles an amazing era of egos, ideas, and inventions at the dawn of the computer age.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. Lance Fortnow traces the history and development of P-NP, giving examples from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The Golden Ticket explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.

    An inventive blend of autobiography, science writing, philosophy, and advice, this book tells the wild story of his personal and professional life as a scientist, from his childhood in the UFO territory of New Mexico, to the loss of his mother, the founding of the first start-up, and finally becoming a world-renowned technological guru. Understanding virtual reality as being both a scientific and cultural adventure, Lanier demonstrates it to be a humanistic setting for technology. While his previous books offered a more critical view of social media and other manifestations of technology, in this book he argues that virtual reality can actually make our lives richer and fuller. Dawn of the New Everything is ultimately a look at what it means to be human at a moment of unprecedented technological possibility, giving readers a new perspective on how the brain and body connect to the world.

    What if everything in life could be reduced to a simple formula? What if numbers were able to tell us which partners we were best matched with – not just in terms of attractiveness, but for a long-term committed marriage? Or if they could say which films would be the biggest hits at the box office, and what changes could be made to those films to make them even more successful? Or even who out of us is likely to commit certain crimes, and when? This may sound like the world of science-fiction, but in fact it is just the tip of the iceberg in a world that is increasingly ruled by complex algorithms and neural networks.In The Formula, Luke Dormehl takes you inside the world of numbers, asking how we came to believe in the all-conquering power of algorithms; introducing the mathematicians, artificial intelligence experts and Silicon Valley entrepreneurs who are shaping this brave new world, and ultimately asking how we survive in an era where numbers can sometimes seem to create as many problems as they solve.

    In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.This deeply researched and passionate book could not be more timely.Naomi Klein: "This book is downright scary."Ethan Zuckerman, MIT: "Should be required reading."Dorothy Roberts, author of Killing the Black Body: "A must-read for everyone concerned about modern tools of inequality in America."Astra Taylor, author of The People's Platform: "This is the single most important book about technology you will read this year."

    C. R. Licklider, whose visionary dream of a human-computer symbiosis transformed the course of modern science and led to the development of the personal computer. Reprint.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.