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.

    This may seem hard to accept, but, as Harari explains in his trademark style—thorough, yet riveting—famine, plague and war have been transformed from incomprehensible and uncontrollable forces of nature into manageable challenges. For the first time ever, more people die from eating too much than from eating too little; more people die from old age than from infectious diseases; and more people commit suicide than are killed by soldiers, terrorists and criminals put together. The average American is a thousand times more likely to die from binging at McDonalds than from being blown up by Al Qaeda.What then will replace famine, plague, and war at the top of the human agenda? As the self-made gods of planet earth, what destinies will we set ourselves, and which quests will we undertake? Homo Deus explores the projects, dreams and nightmares that will shape the twenty-first century—from overcoming death to creating artificial life. It asks the fundamental questions: Where do we go from here? And how will we protect this fragile world from our own destructive powers? This is the next stage of evolution. This is Homo Deus.With the same insight and clarity that made Sapiens an international hit and a New York Times bestseller, Harari maps out our future.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    We have Mindstorms to thank for that. In this book, pioneering computer scientist Seymour Papert uses the invention of LOGO, the first child-friendly programming language, to make the case for the value of teaching children with computers. Papert argues that children are more than capable of mastering computers, and that teaching computational processes like de-bugging in the classroom can change the way we learn everything else. He also shows that schools saturated with technology can actually improve socialization and interaction among students and between students and teachers.

    will mean for us, it also forces readers to challenge their preconceptions. And it manages to do all this in a way that is both entertaining and engaging.” —The New York Times As we approach a great turning point in history when technology is poised to redefine what it means to be human, The Fourth Age offers fascinating insight into AI, robotics, and their extraordinary implications for our species.In The Fourth Age, Byron Reese makes the case that technology has reshaped humanity just three times in history: - 100,000 years ago, we harnessed fire, which led to language. - 10,000 years ago, we developed agriculture, which led to cities and warfare. - 5,000 years ago, we invented the wheel and writing, which lead to the nation state. We are now on the doorstep of a fourth change brought about by two technologies: AI and robotics. The Fourth Age provides extraordinary background information on how we got to this point, and how—rather than what—we should think about the topics we’ll soon all be facing: machine consciousness, automation, employment, creative computers, radical life extension, artificial life, AI ethics, the future of warfare, superintelligence, and the implications of extreme prosperity. By asking questions like “Are you a machine?” and “Could a computer feel anything?”, Reese leads you through a discussion along the cutting edge in robotics and AI, and, provides a framework by which we can all understand, discuss, and act on the issues of the Fourth Age, and how they’ll transform humanity.

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Pulitzer prize-winning New York Times science writer John Markoff argues that we must decide to design ourselves into our future, or risk being excluded from it altogether.In the past decade, Google introduced us to driverless cars; Apple debuted Siri, a personal assistant that we keep in our pockets; and an Internet of Things connected the smaller tasks of everyday life to the farthest reaches of the Web. Robots have become an integral part of society on the battlefield and the road; in business, education, and health care. Cheap sensors and powerful computers will ensure that in the coming years, these robots will act on their own. This new era offers the promise of immensely powerful machines, but it also reframes a question first raised more than half a century ago, when the intelligent machine was born. Will we control these systems, or will they control us?In Machines of Loving Grace, John Markoff offers a sweeping history of the complicated and evolving relationship between humans and computers. In recent years, the pace of technological change has accelerated dramatically, posing an ethical quandary. If humans delegate decisions to machines, who will be responsible for the consequences? As Markoff chronicles the history of automation, from the birth of the artificial intelligence and intelligence augmentation communities in the 1950s and 1960s, to the modern-day brain trusts at Google and Apple in Silicon Valley, and on to the expanding robotics economy around Boston, he traces the different ways developers have addressed this fundamental problem and urges them to carefully consider the consequences of their work. We are on the brink of the next stage of the computer revolution, Markoff argues, and robots will profoundly transform modern life. Yet it remains for us to determine whether this new world will be a utopia. Moreover, it is now incumbent upon the designers of these robots to draw a bright line between what is human and what is machine.After nearly forty years covering the tech industry, Markoff offers an unmatched perspective on the most drastic technology-driven societal shifts since the introduction of the Internet. Machines of Loving Grace draws on an extensive array of research and interviews to present an eye-opening history of one of the most pressing questions of our time, and urges us to remember that we still have the opportunity to design ourselves into the future—before it's too late.

    They are typically used to categorize something based on other data that you have. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. This book is focused on understanding Random Forests at the conceptual level. Knowing how they work, why they work the way that they do, and what options are available to improve results. This book covers how Random Forests work in an intuitive way, and also explains the equations behind many of the functions, but it only has a small amount of actual code (in python). This book is focused on giving examples and providing analogies for the most fundamental aspects of how random forests and decision trees work. The reason is that those are easy to understand and they stick with you. There are also some really interesting aspects of random forests, such as information gain, feature importances, or out of bag error, that simply cannot be well covered without diving into the equations of how they work. For those the focus is providing the information in a straight forward and easy to understand way.

    This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

    But, if you type in "white girls," the results are radically different. The suggested porn sites and un-moderated discussions about "why black women are so sassy" or "why black women are so angry" presents a disturbing portrait of black womanhood in modern society.In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color.Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance - operating as a source for email, a major vehicle for primary and secondary school learning, and beyond - understanding and reversing these disquieting trends and discriminatory practices is of utmost importance.An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.

    He looks at the difficult dialogue between science and religion; the conflict between our human desire for permanence and the impermanence of nature; the possibility that our universe is simply an accident; the manner in which modern technology has separated us from direct experience of the world; and our resistance to the view that our bodies and minds can be explained by scientific logic and laws. And behind all of these considerations is the suggestion—at once haunting and exhilarating—that what we see and understand of the world is only a tiny piece of the extraordinary, perhaps unfathomable whole.

    After decades of sci-fi fantasies and hype, artificial intelligence has leapt out of research labs and Silicon Valley engineering departments and into the center of our lives. Algorithms shape everything around us, from the news we see to the products we buy and the relationships we form. And while the debate over whether or not automation will destroy jobs rages on, a much more important question is being ignored:What does it mean to be a human in a world that is increasingly built by and for machines?In Futureproof: 9 Rules for Humans in the Age of Automation, New York Times technology columnist Kevin Roose lays out a hopeful, pragmatic vision of how people can succeed in the machine age by making themselves irreplaceably human. He shares the secrets of people and organizations that have survived technological change, and explains how we can protect our own futures, with lessons like- Do work that is surprising, social, and scarce (the types of work machines can't do). - Demote your phone. - Work near other people. - Treat A.I. like an army of chimpanzees. - Add more friction to your life.Roose rejects the conventional wisdom that in order to compete with machines, we have to become more like them--hyper-efficient, data-driven, code-writing workhorses. Instead, he says, we should let machines be machines, and focus on doing the kinds of creative, inspiring, and meaningful things only humans can do.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    If we get it right it will make humans almost godlike. If we get it wrong... well, extinction is not the worst possible outcome.“Surviving AI” is a concise, easy-to-read guide to what's coming, taking you through technological unemployment (the economic singularity) and the possible creation of a superintelligence (the technological singularity).Here's what some of the leading thinkers in the field have to say about it:A sober and easy-to-read review of the risks and opportunities that humanity will face from AI. Jaan Tallinn – co-founder of Skype Understanding AI – its promise and its dangers – is emerging as one of the great challenges of coming decades and this is an invaluable guide to anyone who’s interested, confused, excited or scared. David Shukman – BBC Science Editor We have recently seen a surge in the volume of scholarly analysis of this topic; Chace impressively augments that with this high-quality, more general-audience discussion. Aubrey de Grey – CSO of SENS Research Foundation; former AI researcher It's rare to see a book about the potential End of the World that is fun to read without descending into sensationalism or crass oversimplification. Ben Goertzel – chairman of Novamente LLC Calum Chace is a prescient messenger of the risks and rewards of artificial intelligence. In “Surviving AI” he has identified the most essential issues and developed them with insight and wit – so that the very framing of the questions aids our search for answers. Chace’s sensible balance between AI’s promise and peril makes “Surviving AI” an excellent primer for anyone interested in what’s happening, how we got here, and where we are headed. Kenneth Cukier – co-author of “Big Data” If you’re not thinking about AI, you’re not thinking.  “Surviving AI” combines an essential grounding in the state of the art with a survey of scenarios that will be discussed with equal vigor at cocktail parties and academic colloquia. Chris Meyer – author of “Blur”, “It’s Alive”, and “Standing on the Sun” The appearance of Calum Chace's book is of some considerable personal satisfaction to me, because it signifies the fact that the level of social awareness of the rise of massively intelligent machines has finally reached the mainstream. If you want to survive the next few decades, you cannot afford NOT to read Chace's book. Prof. Dr. Hugo de Garis – former director of the Artificial Brain Lab, Xiamen University, China “Surviving AI” is an exceptionally clear, well-researched and balanced introduction to a complex and controversial topic, and is a compelling read to boot. Seán Ó hÉigeartaigh – executive director of Cambridge Centre for the Study of Existential Risk In “Surviving AI”, Calum Chace provides a marvellously accessible guide to the swirls of controversy that surround discussion of what is likely to be the single most important event in human history - the emergence of artificial super