Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    One of the most coveted tools in a spy's arsenal, a zero day has the power to silently spy on your iPhone, dismantle the safety controls at a chemical plant, alter an election, and shut down the electric grid (just ask Ukraine).For decades, under cover of classification levels and non-disclosure agreements, the United States government became the world's dominant hoarder of zero days. U.S. government agents paid top dollar-first thousands, and later millions of dollars- to hackers willing to sell their lock-picking code and their silence. Then the United States lost control of its hoard and the market. Now those zero days are in the hands of hostile nations and mercenaries who do not care if your vote goes missing, your clean water is contaminated, or our nuclear plants melt down.Filled with spies, hackers, arms dealers, and a few unsung heroes, written like a thriller and a reference, This Is How They Tell Me the World Ends is an astonishing feat of journalism. Based on years of reporting and hundreds of interviews, The New York Times reporter Nicole Perlroth lifts the curtain on a market in shadow, revealing the urgent threat faced by us all if we cannot bring the global cyber arms race to heel.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    More then seven thousand robotic systems are now in Iraq. Pilots in Nevada are remotely killing terrorists in Afghanistan. Scientists are debating just how smart - and how lethal - to make their current robotic prototypes. And many of the most renowned science fiction authors are secretly consulting for the Pentagon on the next generation.Blending historic evidence with interviews from the field, Singer vividly shows that as these technologies multiply, they will have profound effects on the front lines as well as on the politics back home. Moving humans off the battlefield makes wars easier to start, but more complex to fight. Replacing men with machines may save some lives, but will lower the morale and psychological barriers to killing. The "warrior ethos", which has long defined soldiers' identity, will erode, as will the laws of war that have governed military conflict for generations.While his analysis is unnerving, there's an irresistible gee-whiz quality to the innovations Singer uncovers. Wired for War travels from Iraq to see these robots in combat to the latter-day "skunk works" in America's suburbia, where tomorrow's technologies of war are quietly being designed. In Singer's hands, the future of war is as fascinating as it is frightening.

    The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    

    The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs.The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action.Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework.This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    There are updates throughout the book as well. These take into account important advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. The chapter "Economic Reality" has also been revised to reflect a change in emphasis in Simon's thinking about the respective roles of organizations and markets in economic systems."People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers." -- George A. Miller

    It has found adherents in Silicon Valley billionaires Ray Kurzweil and Peter Diamandis. Google has entered the picture, establishing a bio-tech subsidiary aimed at solving the problem of aging.In To Be a Machine, journalist Mark O'Connell takes a headlong dive into this burgeoning movement. He travels to the laboratories, conferences, and basements of today's foremost transhumanists, where he's presented with the staggering possibilities and moral quandaries of new technologies like mind uploading, artificial superintelligence, cryonics, and device implants.A contributor to Slate, The New Yorker, and The New York Times Magazine, O'Connell serves as a sharp and lively guide to the outer limits of technology in the twenty first century. In investigating what it means to be a machine, he offers a surprising, singular meditation on what it means to be human."

    But it's also more importantly about a community of people who are trying to think rationally about intelligence, and the places that these thoughts are taking them, and what insight they can and can't give us about the future of the human race over the next few years. It explains why these people are worried, why they might be right, and why they might be wrong. It is a book about the cutting edge of our thinking on intelligence and rationality right now by the people who stay up all night worrying about it.Along the way, we discover why we probably don't need to worry about a future AI resurrecting a perfect copy of our minds and torturing us for not inventing it sooner, but we perhaps should be concerned about paperclips destroying life as we know it; how Mickey Mouse can teach us an important lesson about how to program AI; and how a more rational approach to life could be what saves us all.

    Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.

    This might seem uncontroversial, but it flies in the face of a tech sector long obsessed with rapid growth and sometimes on disruption as an end in itself. Now, though, we have reached an inflection point: Silicon Valley has moved fast and it has broken things. A new understanding has emerged that companies that create technology must accept greater responsibility for the future. And governments will need to regulate technology by moving faster and catching up with the pace of innovation that is impacting our communities and changing the world.In Tools and Weapons, Brad Smith takes us into the cockpit of one of the world's largest and most powerful tech companies as it finds itself in the middle of some of the thorniest emerging issues of our time. These are challenges that come with no preexisting playbook, including privacy, cybercrime and cyberwar, social media, the moral conundrums of AI, big tech's relationship to inequality and the challenges for democracy, far and near. While in no way a self-glorifying "Microsoft memoir," the book opens up the curtain remarkably wide onto some of the company's most crucial recent decision points, as it strives to protect the hopes technology offers against the very real threats it also presents. Every tool can be a weapon in the wrong person's hands, and companies are being challenged in entirely new ways to embrace the totality of their responsibilities. We have moved from a world in which Silicon Valley could take no prisoners to one in which tech companies and governments must work together to address the challenges and adapt to the changes technology has unleashed. There are huge ramifications to be thought through, and Brad Smith provides a marvelous and urgently necessary contribution to that effort.

    They can navigate more data than a doctor or lawyer and act with greater precision. For many years we’ve taken solace in the notion that they can’t create. But now that algorithms can learn and adapt, does the future of creativity belong to machines, too?It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. The Pollockizer can produce drip paintings in the style of Jackson Pollock, Botnik spins off fanciful (if improbable) scenes inspired by J. K. Rowling, and the music-composing algorithm Emmy managed to fool a panel of Bach experts. But do these programs just mimic, or do they have what it takes to create? Du Sautoy argues that to answer this question, we need to understand how the algorithms that drive them work―and this brings him back to his own subject of mathematics, with its puzzles, constraints, and enticing possibilities.While most recent books on AI focus on the future of work, The Creativity Code moves us to the forefront of creative new technologies and offers a more positive and unexpected vision of our future cohabitation with machines. It challenges us to reconsider what it means to be human―and to crack the creativity code.