It might seem that there’s an obvious reason that true beliefs matter: false beliefs will hurt you. But if that’s right, then why is it (apparently) irrelevant to many people whether they believe true things or not? The Misinformation Age, written for a political era riven by “fake news,” “alternative facts,” and disputes over the validity of everything from climate change to the size of inauguration crowds, shows convincingly that what you believe depends on who you know. If social forces explain the persistence of false belief, we must understand how those forces work in order to fight misinformation effectively.

    But is it for the better? Amid a chorus of doomsayers, Clive Thompson delivers a resounding "yes." The Internet age has produced a radical new style of human intelligence, worthy of both celebration and analysis. We learn more and retain it longer, write and think with global audiences, and even gain an ESP-like awareness of the world around us. Modern technology is making us smarter, better connected, and often deeper—both as individuals and as a society. In Smarter Than You Think Thompson shows that every technological innovation—from the written word to the printing press to the telegraph—has provoked the very same anxieties that plague us today. We panic that life will never be the same, that our attentions are eroding, that culture is being trivialized. But as in the past, we adapt—learning to use the new and retaining what’s good of the old. Thompson introduces us to a cast of extraordinary characters who augment their minds in inventive ways. There's the seventy-six-year old millionaire who digitally records his every waking moment—giving him instant recall of the events and ideas of his life, even going back decades. There's a group of courageous Chinese students who mounted an online movement that shut down a $1.6 billion toxic copper plant. There are experts and there are amateurs, including a global set of gamers who took a puzzle that had baffled HIV scientists for a decade—and solved it collaboratively in only one month. Smarter Than You Think isn't just about pioneers. It's about everyday users of technology and how our digital tools—from Google to Twitter to Facebook and smartphones—are giving us new ways to learn, talk, and share our ideas. Thompson harnesses the latest discoveries in social science to explore how digital technology taps into our long-standing habits of mind—pushing them in powerful new directions. Our thinking will continue to evolve as newer tools enter our lives. Smarter Than You Think embraces and extols this transformation, presenting an exciting vision of the present and the future.

    Weinberg adds new insights and highlights the similarities and differences between now and then. Using a conversational style that invites the reader to join him, Weinberg reunites with some of his most insightful writings on the human side of software engineering.Topics include egoless programming, intelligence, psychological measurement, personality factors, motivation, training, social problems on large projects, problem-solving ability, programming language design, team formation, the programming environment, and much more.Dorset House Publishing is proud to make this important text available to new generations of programmers -- and to encourage readers of the first edition to return to its valuable lessons.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    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.

    The shift toward automation is about to create a tsunami of unemployment. Not in the distant future—now. One recent estimate predicts 45 million American workers will lose their jobs within the next twelve years—jobs that won't be replaced. In a future marked by restlessness and chronic unemployment, what will happen to American society? In The War on Normal People, Andrew Yang paints a dire portrait of the American economy. Rapidly advancing technologies like artificial intelligence, robotics and automation software are making millions of Americans' livelihoods irrelevant. The consequences of these trends are already being felt across our communities in the form of political unrest, drug use, and other social ills. The future looks dire-but is it unavoidable? In The War on Normal People, Yang imagines a different future—one in which having a job is distinct from the capacity to prosper and seek fulfillment. At this vision's core is Universal Basic Income, the concept of providing all citizens with a guaranteed income-and one that is rapidly gaining popularity among forward-thinking politicians and economists. Yang proposes that UBI is an essential step toward a new, more durable kind of economy, one he calls "human capitalism."

    In this eloquent, persuasive book, Neil Postman alerts us to the real and present dangers of this state of affairs, and offers compelling suggestions as to how to withstand the media onslaught. Before we hand over politics, education, religion, and journalism to the show business demands of the television age, we must recognize the ways in which the media shape our lives and the ways we can, in turn, shape them to serve out highest goals.

    A world that is as creative and complex as it is dangerous and disturbing. A world that is much closer than you think.The dark net is an underworld that stretches from popular social media sites to the most secretive corners of the encrypted web. It is a world that frequently appears in newspaper headlines, but one that is little understood, and rarely explored. The Dark Net is a revelatory examination of the internet today, and of its most innovative and dangerous subcultures: trolls and pornographers, drug dealers and hackers, political extremists and computer scientists, Bitcoin programmers and self-harmers, libertarians and vigilantes.Based on extensive first-hand experience, exclusive interviews and shocking documentary evidence, The Dark Net offers a startling glimpse of human nature under the conditions of freedom and anonymity, and shines a light on an enigmatic and ever-changing world.

    Drawing heavily on the author's own real-world experiences, the book stresses design and analysis. Coverage is divided into two parts, the first being a general guide to techniques for the design and analysis of computer algorithms. The second is a reference section, which includes a catalog of the 75 most important algorithmic problems. By browsing this catalog, readers can quickly identify what the problem they have encountered is called, what is known about it, and how they should proceed if they need to solve it. This book is ideal for the working professional who uses algorithms on a daily basis and has need for a handy reference. This work can also readily be used in an upper-division course or as a student reference guide. THE ALGORITHM DESIGN MANUAL comes with a CD-ROM that contains: * a complete hypertext version of the full printed book. * the source code and URLs for all cited implementations. * over 30 hours of audio lectures on the design and analysis of algorithms are provided, all keyed to on-line lecture notes.

    Developing technology promises closeness. Sometimes it delivers, but much of our modern life leaves us less connected with people and more connected to simulations of them.In Alone Together, MIT technology and society professor Sherry Turkle explores the power of our new tools and toys to dramatically alter our social lives. It’s a nuanced exploration of what we are looking for—and sacrificing—in a world of electronic companions and social networking tools, and an argument that, despite the hand-waving of today’s self-described prophets of the future, it will be the next generation who will chart the path between isolation and connectivity.

    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.

    It has created a new, unprecedented cultural space that we are all a part of—even if we don’t participate, that is how we participate—but by which we’re continually surprised, betrayed, enriched, befuddled. We have churned through platforms and technologies and in turn been churned by them. And yet, the internet is us and always has been.In Lurking, Joanne McNeil digs deep and identifies the primary (if sometimes contradictory) concerns of people online: searching, safety, privacy, identity, community, anonymity, and visibility. She charts what it is that brought people online and what keeps us here even as the social equations of digital life—what we’re made to trade, knowingly or otherwise, for the benefits of the internet—have shifted radically beneath us. It is a story we are accustomed to hearing as tales of entrepreneurs and visionaries and dynamic and powerful corporations, but there is a more profound, intimate story that hasn’t yet been told.Long one of the most incisive, ferociously intelligent, and widely respected cultural critics online, McNeil here establishes a singular vision of who we are now, tells the stories of how we became us, and helps us start to figure out what we do now.

    This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts.The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants.Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

    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

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.