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.

    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.

    More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it.The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as "computers") who performed complex calculations as teams engaged in CT long before electronic computers. The authors identify six dimensions of today's highly developed CT--methods, machines, computing education, software engineering, computational science, and design--and cover each in a chapter. Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.

    So are we on the edge of an AI-pocalypse, with super-intelligent devices superseding humanity, as predicted by Stephen Hawking? Or will this herald a kind of Utopia, with machines doing a far better job at complex tasks than us? You might not realise it, but you interact with AIs every day. They route your phone calls, approve your credit card transactions and help your doctor interpret results. Driverless cars will soon be on the roads with a decision-making computer in charge. But how do machines actually think and learn? In Machines That Think, AI experts and New Scientist explore how artificial ingence helps us understand human intelligence, machines that compose music and write stories - and ask if AI is really a threat.--

    In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development: digitization--the coding of information, computation, and control in binary form, ones and zeros; the convergence of multiple streams of techniques, devices, and machines, yielding more than the sum of their parts; the steady advance of electronic technology, as characterized famously by "Moore's Law"; and the human-machine interface. Ceruzzi guides us through computing history, telling how a Bell Labs mathematician coined the word "digital" in 1942 (to describe a high-speed method of calculating used in anti-aircraft devices), and recounting the development of the punch card (for use in the 1890 U.S. Census). He describes the ENIAC, built for scientific and military applications; the UNIVAC, the first general purpose computer; and ARPANET, the Internet's precursor. Ceruzzi's account traces the world-changing evolution of the computer from a room-size ensemble of machinery to a "minicomputer" to a desktop computer to a pocket-sized smart phone. He describes the development of the silicon chip, which could store ever-increasing amounts of data and enabled ever-decreasing device size. He visits that hotbed of innovation, Silicon Valley, and brings the story up to the present with the Internet, the World Wide Web, and social networking.

    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.

    Blade Runner, The Matrix, Star Wars, Avatar, Star Trek, Ready Player One and Avengers show us futuristic worlds where holograms, intelligent robots, smart devices, virtual avatars, digital transactions, and universe-scale teleportation work together perfectly, somehow seamlessly combining the virtual and the physical with the mechanical and the biological. Science fiction has done an excellent job describing a vision of the future where the digital and physical merge naturally into one — in a way that just works everywhere, for everyone. However, none of these visionary fictional works go so far as to describe exactly how this would actually be accomplished. While it has inspired many of us to ask the question—How do we enable science fantasy to become....science fact? The Spatial Web achieves this by first describing how exponentially powerful computing technologies are creating a great “Convergence.” How Augmented and Virtual Reality will enable us to overlay our information and imaginations onto the world. How Artificial Intelligence will infuse the environments and objects around us with adaptive intelligence. How the Internet of Things and Robotics will enable our vehicles, appliances, clothing, furniture, and homes to become connected and embodied with the power to see, feel, hear, smell, touch and move things in the world, and how Blockchain and Cryptocurrencies will secure our data and enable real-time transactions between the human, machine and virtual economies of the future. The book then dives deeply into the challenges and shortcomings of the World Wide Web, the rise of fake news and surveillance capitalism in Web 2.0 and the risk of algorithmic terrorism and biological hacking and “fake-reality” in Web 3.0. It raises concerns about the threat that emerging technologies pose in the hands of rogue actors whether human, algorithmic, corporate or state-sponsored and calls for common sense governance and global cooperation. It calls for business leaders, organizations and governments to not only support interoperable standards for software code, but critically, for ethical, and social codes as well. Authors Gabriel René and Dan Mapes describe in vivid detail how a new “spatial” protocol is required in order to connect the various exponential technologies of the 21st century into an integrated network capable of tracking and managing the real-time activities of our cities, monitoring and adjusting the supply chains that feed them, optimizing our farms and natural resources, automating our manufacturing and distribution, transforming marketing and commerce, accelerating our global economies, running advanced planet-scale simulations and predictions, and even bridging the gap between our interior individual reality and our exterior collective one. Enabling the ability for humans, machines and AI to communicate, collaborate and coordinate activities in the world at a global scale and how the thoughtful application of these technologies could lead to an unprecedented opportunity to create a truly global “networked” civilization or "Smart World.” The book artfully shifts between cyberpunk futurism, cautionary tale-telling, and life-affirming call-to-arms. It challenges us to consider the importance of today’s technological choices as individuals, organizations, and as a species, as we face the historic opportunity we have to transform the web, the world, and our very definition of reality.

    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.

    Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data--or anything in between--this guide makes it easier to understand and implement machine learning seamlessly.Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

    Then, in Bold, they chronicled the use of exponential technologies that allowed the emergence of powerful new entrepreneurs. Now the bestselling authors are back with The Future Is Faster Than You Think, a blueprint for how our world will change in response to the next ten years of rapid technological disruption. Technology is accelerating far more quickly than anyone could have imagined. During the next decade, we will experience more upheaval and create more wealth than we have in the past hundred years. In this gripping and insightful roadmap to our near future, Diamandis and Kotler investigate how wave after wave of exponentially accelerating technologies will impact both our daily lives and society as a whole. What happens as AI, robotics, virtual reality, digital biology, and sensors crash into 3D printing, blockchain, and global gigabit networks? How will these convergences transform today’s legacy industries? What will happen to the way we raise our kids, govern our nations, and care for our planet? Diamandis, a space-entrepreneur-turned-innovation-pioneer, and Kotler, bestselling author and peak performance expert, probe the science of technological convergence and how it will reinvent every part of our lives—transportation, retail, advertising, education, health, entertainment, food, and finance—taking humanity into uncharted territories and reimagining the world as we know it. As indispensable as it is gripping, The Future Is Faster Than You Think provides a prescient look at our impending future.

    In fact, AI will dramatically change our entire society.You might have heard that many jobs will be replaced by automation and robots, but did you also know that at the same time a huge number of new jobs will be created by AI?This book covers many fascinating and timely topics related to artificial intelligence, including: self-driving cars, robots, chatbots, and how AI will impact the job market, business processes, and entire industries, just to name a few.This book is divided into ten chapters:Chapter I: Introduction to Artificial IntelligenceChapter II: How Artificial Intelligence Is Changing Many IndustriesChapter III: How Artificial Intelligence Is Changing Business ProcessesChapter IV: Chatbots and How They Will Change CommunicationChapter V: How Artificial Intelligence Is Changing the Job MarketChapter VI: Self-Driving Cars and How They Will Change Traffic as We Know ItChapter VII: Robots and How They Will Change Our LivesChapter VIII: Artificial Intelligence Activities of Big Technology CompaniesChapter IX: Frequently Asked Questions About Artificial Intelligence Part IChapter X: Frequently Asked Questions About Artificial Intelligence Part IITo enhance your learning experience and help make the concepts easier to understand, there are more than 85 visual presentations included throughout the book.You will learn the answers to 101 questions about artificial intelligence, and also have access to a large number of resources, ideas and tips that will help you to understand how artificial intelligence will change our lives.Who is this book for?Managers and business professionalsMarketers and influencersEntrepreneurs and startupsConsultants and coachesEducators and teachersStudents and life-long learnersAnd everyone else who is interested in our future.Are you ready to discover how artificial intelligence will impact your life This guidebook offers a multitude of tools, techniques and strategies that every business and individual can quickly apply and benefit from.

      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.

    We take advantage of this revolutionary opportunity when we make our work "open access" digital, online, free of charge, and free of most copyright and licensing restrictions. Open access is made possible by the Internet and copyright-holder consent, and many authors, musicians, filmmakers, and other creators who depend on royalties are understandably unwilling to give their consent. But for 350 years, scholars have written peer-reviewed journal articles for impact, not for money, and are free to consent to open access without losing revenue.In this concise introduction, Peter Suber tells us what open access is and isn't, how it benefits authors and readers of research, how we pay for it, how it avoids copyright problems, how it has moved from the periphery to the mainstream, and what its future may hold. Distilling a decade of Suber's influential writing and thinking about open access, this is the indispensable book on the subject for researchers, librarians, administrators, funders, publishers, and policy makers.ContentsSeries Foreword viiPreface ix1 What Is Open Access? 12 Motivation 293 Varieties 494 Policies 775 Scope 976 Copyright 1257 Economics 1338 Casualties 1499 Future 16310 Self-Help 169Glossary 175Notes 177Additional Resources 219Index 223

    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.

    But few of us realize just how many oversights, biases, and downright ethical nightmares are baked inside the tech products we use every day. It’s time we change that.In Technically Wrong, Sara Wachter-Boettcher demystifies the tech industry, leaving those of us on the other side of the screen better prepared to make informed choices about the services we use—and to demand more from the companies behind them.