His revelations demystify the strategy behind the company's recent flurry of bold moves, all driven by the pursuit of a business plan unlike any other: to become the indispensable gatekeeper of all the world's information, the one-stop destination for all our information needs. Will Google succeed? And what are the implications of a single company commanding so much information and knowing so much about us? As ambitious as Google's goal is, with 68 percent of all Web searches (and growing), profits that are the envy of the business world, and a surplus of talent, the company is, Stross shows, well along the way to fulfilling its ambition, becoming as dominant a force on the Web as Microsoft became on the PC. Google isn't just a superior search service anymore. In recent years it has launched a dizzying array of new services and advanced into whole new businesses, from the introductions of its controversial Book Search and the irresistible Google Earth, to bidding for a slice of the wireless-phone spectrum and nonchalantly purchasing YouTube for $1.65 billion. Google has also taken direct aim at Microsoft's core business, offering free e-mail and software from word processing to spreadsheets and calendars, pushing a transformative -- and highly disruptive -- concept known as "cloud computing." According to this plan, users will increasingly store all of their data on Google's massive servers -- a network of a million computers that amounts to the world's largest supercomputer, with unlimited capacity to house all the information Google seeks. The more offerings Google adds, and the more ubiquitous a presence it becomes, the more dependent its users become on its services and the more information they contribute to its uni

    But the truth is that Artificial Intelligence is already among us. It exists in our smartphones, fitness trackers, and refrigerators that tell us when the milk will expire. In some ways, the future people dreamed of at the World's Fair in the 1960s is already here. We're teaching our machines how to think like humans, and they're learning at an incredible rate.In Thinking Machines, technology journalist Luke Dormehl takes you through the history of AI and how it makes up the foundations of the machines that think for us today. Furthermore, Dormehl speculates on the incredible--and possibly terrifying--future that's much closer than many would imagine. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to broaden itself to include intelligent machines.

    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

    Because it was extremely difficult to verbalize the events to her own children years later, her adult family knew very little of the details until 30 years after her passing in 1967. That is when her granddaughter discovered her writings and promised to tell the story of what she endured.

    Two of the enemy soldiers, which we often referred to as gooks, quickly came after me. As I quickly mowed them down with my automatic rifle, I crawled backwards away from the enemy gunfire, using my helmet to push sand in front of me as I went, which made it possible to look behind me. But as I looked back, I realized that my safety net was no longer safe. I saw my entire company falling like dominoes. Medics were running left and right, risking their lives to help others with bravery that even the most amazing soldier couldn’t hope to match. Some of the events I witnessed during that moment were beyond comprehension. I watched a young, courageous black medic take an 81-millimeter round to his head, and his whole body instantly turned to smoke. Young nineteen and twenty year old kids were crying like children, but fighting like someone had raped their sisters. So many things were going through my head at that moment, and in one single heartbeat I was overwhelmed with a flashback of my entire life. This is my story, from point A to B, of my life and times in the midst of hell on Earth.”

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    When I m alone I am haunted by my truth. A girl who entered this world in a jail cell. A girl who was served struggle with a side of pain on a broken platter. A girl who was thrown into a tank with sharks deep in a world whose motto is to eat or be eaten. I can still see the dirt underneath my nails; I've fought too hard to get where I am and I don t plan on looking back. However, there s always someone waiting to knock me down because they don t think I deserve it. Well I say to hell with them. I've put in too much to allow anyone to drag me down. So either you re riding with me or against me...

    This does not include scavenging. Although human beings can be attacked by many kinds of animals, man-eaters are those that have incorporated human flesh into their usual diet. Most reported cases of man-eaters have involved tigers, leopards, lions and crocodiles. However, they are by no means the only predators that will attack humans if given the chance; a wide variety of species have also been known to take humans as prey. ATTACKED BY A KING COBRA.ALADDIN'S CAVE.THE TERROR OF HUNSUR.THE TERROR OF HUNSUR II.AN ADVENTURE WITH A BOA.THE ONE EYED MAN EATER.A MAN EATING WOLF BOY.SEEALL, THE WOLF BOY.THE WHITE TIGER.THE FATE OF THE AHNAY PAYEE.THE BANDYPORE MAN EATER.THE KODERMA MAN EATER.TRAPPING A MAN EATER.THE MAN EATER OF BELKHERA.A NOTORIOUS MAN EATER.TUG OF-WAR WITH A LEOPARD.MISSED BY AN INCH.A FIGHTING TIGER.A NIGHT FRIGHT.CARRIED OFF BY A TIGER.

    Joesten carefully documents the little-known facts behind Johnson's involvement in scandals stretching back to his first stolen election in 1948, thru the Bobby Baker, Billy Sol Estes and Walter Jenkins affairs, and culminates with the assassination of John F. Kennedy. Included are LBJ's connection to mobsters, big Texas oil, political graft and corruption, blackmailing of FBI chief J. Edgar Hoover, and a disturbing number of murders committed by his henchmen for LBJ's personal gain.FROM THE BOOK:The true nature of Lyndon B. Johnson has long been hidden from the public through the frenzied efforts of highly paid P.R. wizards and artificial image-builders. William Manchester came closer than most other people to seeing through the benign public relations mask of Lyndon Johnson, but one wouldn't know it from scanning the pages of 'The Death of a President'.If there are two persons in the world who have really come to know Johnson at close quarters, outside of his own family, they are Robert and Jacqueline Kennedy. Manchester interviewed both of them at length and they told him, without mincing their words, what they thought of That Man in the White House. But when Manchester, having faithfully recorded everything the Kennedys had told him, rushed into print with his story, years ahead of schedule, they both got panicky and practically forced him to 'revise' his story out of recognition.Edward J. Epstein, the author of Inquest, somehow managed to get hold of a copy of the original, unedited manuscript of the Manchester book, then entitled 'Death of a Lancer', and revealed in the July issue 1967 of Commentary, some of its contents.In his original draft, Manchester, it seems, made some very pungent remarks about Lyndon Johnson whom he described, among other things, as a 'chameleon who constantly changes loyalties'; 'a capon' and 'a crafty schemer who has a gaunt, hunted look about him'.He also pictured Johnson as 'a full-fledged hypomaniac' and 'the crafty seducer with six nimble hands who can persuade a woman to surrender her favors in the course of a long conversation confined to obscure words. No woman, even a lady, can discern his intentions until the critical moment'.By far the most interesting aspect of this matter, however, is Epstein's contention that Manchester's original theme, which gave unity to his book, was 'the notion that Johnson, the successor, was somehow responsible for the death-of his predecessor'.Several quotations from the original draft bear out this contention. At one point, the Lancer version states, 'The shattering fact of the assassination is that a Texas murder has made a Texan President'.At another, Kenneth O'Donnell, Kennedy's appointments secretary, is quoted as exclaiming 'They did it. I always knew they'd do it. You couldn't expect anything else from them. They finally made it'.Then Manchester comments: 'He didn't specify who "they" were. It was unnecessary. They were Texans, Johnsonians'.But what is one to think of an author who allows his most important work not only to be castrated, but to be turned completely upside down by a publisher more committed to the dictates of expediency than to the search for historical truth?

    Among the women she profiles are Supreme Court Justice Sandra Day O'Connor, architect and urban planner Denise Scott Brown, and Mileva Maric Einstein, the scientist whose marriage to Einstein ended in tragedy.

    Less than 20 years after its founding, it boasts the largest constellation of commercial satellites in orbit, has pioneered reusable rockets, and in 2020 became the first private company to launch human beings into orbit. Half a century after the space race it is private companies, led by SpaceX, standing alongside NASA pushing forward into the cosmos, and laying the foundation for our exploration of other worlds.But before it became one of the most powerful players in the aerospace industry, SpaceX was a fledgling startup, scrambling to develop a single workable rocket before the money ran dry. The engineering challenge was immense; numerous other private companies had failed similar attempts. And even if SpaceX succeeded, they would then have to compete for government contracts with titans such as Lockheed Martin and Boeing, who had tens of thousands of employees and tens of billions of dollars in annual revenue. SpaceX had fewer than 200 employees and the relative pittance of $100 million in the bank.In Liftoff, Eric Berger, senior space editor at Ars Technica, takes readers inside the wild early days that made SpaceX. Focusing on the company’s first four launches of the Falcon 1 rocket, he charts the bumpy journey from scrappy underdog to aerospace pioneer. We travel from company headquarters in El Segundo, to the isolated Texas ranchland where they performed engine tests, to Kwajalein, the tiny atoll in the Pacific where SpaceX launched the Falcon 1. Berger has reported on SpaceX for more than a decade, enjoying unparalleled journalistic access to the company’s inner workings. Liftoff is the culmination of these efforts, drawing upon exclusive interviews with dozens of former and current engineers, designers, mechanics, and executives, including Elon Musk. The enigmatic Musk, who founded the company with the dream of one day settling Mars, is the fuel that propels the book, with his daring vision for the future of space.Filled with never-before-told stories of SpaceX’s turbulent beginning, Liftoff is a saga of cosmic proportions.

    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.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Dear, simple mother, in her terrible clothes, and the twins, got up with more thought for economy than for beauty! I shopped extravagantly with them. The youngsters wanted to see everything in New York; but mother, despite all of those hard, lonely years in our rough country and the many interesting things for her to do and see in New York-- mother wanted nothing better than to stay with the baby.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases