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.

    Underlying this trend is a single idea: the belief that our existence is understandable through computation, and more data is enough to help us build a better world.   In actual fact, we are lost in a sea of information, increasingly divided by fundamentalism, simplistic narratives, conspiracy theories, and post-factual politics. Meanwhile, those in power use our lack of understanding to further their own interests. Despite the accessibility of information, we’re living in a new Dark Age.   From rogue financial systems to shopping algorithms, from artificial intelligence to state secrecy, we no longer understand how our world is governed or presented to us. The media is filled with unverifiable speculation, much of it generated by anonymous software, while companies dominate their employees through surveillance and the threat of automation.   In his brilliant new work, leading artist and writer James Bridle excavates the limits of technology and how it aids our understanding of the world. Surveying the history of art, technology, and information systems, he explores the dark clouds that gather over our dreams of the digital sublime.

    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.

    We like to think that we are in control of the future of "artificial" intelligence. The reality, though, is that we -- the everyday people whose data powers AI -- aren't actually in control of anything. When, for example, we speak with Alexa, we contribute that data to a system we can't see and have no input into -- one largely free from regulation or oversight. The big nine corporations -- Amazon, Google, Facebook, Tencent, Baidu, Alibaba, Microsoft, IBM and Apple--are the new gods of AI and are short-changing our futures to reap immediate financial gain. In this book, Amy Webb reveals the pervasive, invisible ways in which the foundations of AI -- the people working on the system, their motivations, the technology itself -- is broken. Within our lifetimes, AI will, by design, begin to behave unpredictably, thinking and acting in ways which defy human logic. The big nine corporations may be inadvertently building and enabling vast arrays of intelligent systems that don't share our motivations, desires, or hopes for the future of humanity. Much more than a passionate, human-centered call-to-arms, this book delivers a strategy for changing course, and provides a path for liberating us from algorithmic decision-makers and powerful corporations.

    Politicians and marketers present shoddy evidence for dubious claims all the time. But smart people make mistakes too, and when it comes to statistics, plenty of otherwise great scientists--yes, even those published in peer-reviewed journals--are doing statistics wrong."Statistics Done Wrong" comes to the rescue with cautionary tales of all-too-common statistical fallacies. It'll help you see where and why researchers often go wrong and teach you the best practices for avoiding their mistakes.In this book, you'll learn: - Why "statistically significant" doesn't necessarily imply practical significance- Ideas behind hypothesis testing and regression analysis, and common misinterpretations of those ideas- How and how not to ask questions, design experiments, and work with data- Why many studies have too little data to detect what they're looking for-and, surprisingly, why this means published results are often overestimates- Why false positives are much more common than "significant at the 5% level" would suggestBy walking through colorful examples of statistics gone awry, the book offers approachable lessons on proper methodology, and each chapter ends with pro tips for practicing scientists and statisticians. No matter what your level of experience, "Statistics Done Wrong" will teach you how to be a better analyst, data scientist, or researcher.

    More than 750 million people (and half of all Americans) use Facebook, where we share a billion times a day. The collective voice of Twitter echoes instantly 100 million times daily, from Tahrir Square to the Mall of America, on subjects that range from democratic reform to unfolding natural disasters to celebrity gossip. New tools let us share our photos, videos, purchases, knowledge, friendships, locations, and lives.Yet change brings fear, and many people--nostalgic for a more homogeneous mass culture and provoked by well-meaning advocates for privacy--despair that the internet and how we share there is making us dumber, crasser, distracted, and vulnerable to threats of all kinds. But not Jeff Jarvis.In this shibboleth-destroying book, "Public Parts "argues persuasively and personally that the internet and our new sense of publicness are, in fact, doing the opposite. Jarvis travels back in time to show the amazing parallels of fear and resistance that met the advent of other innovations such as the camera and the printing press. The internet, he argues, will change business, society, and life as profoundly as Gutenberg's invention, shifting power from old institutions to us all.Based on extensive interviews, "Public Parts "introduces us to the men and women building a new industry based on sharing. Some of them have become household names--Facebook's Mark Zuckerberg, Google's Eric Schmidt, and Twitter's Evan Williams. Others may soon be recognized as the industrialists, philosophers, and designers of our future. Jarvis explores the promising ways in which the internet and publicness allow us to collaborate, think, ways--how we manufacture and market, buy and sell, organize and govern, teach and learn. He also examines the necessity as well as the limits of privacy in an effort to understand and thus protect it. This new and open era has already profoundly disrupted economies, industries, laws, ethics, childhood, and many other facets of our daily lives. But the change has just begun. The shape of the future is not assured. The amazing new tools of publicness can be used to good ends and bad. The choices--and the responsibilities--lie with us. Jarvis makes an urgent case that the future of the internet--what one technologist calls "the eighth continent"--requires as much protection as the physical space we share, the air we breathe, and the rights we afford one another. It is a space of the public, for the public, and by the public. It needs protection and respect from all of us. As Secretary of State Hillary Clinton said in the wake of the uprisings in the Middle East, "If people around the world are going to come together every day online and have a safe and productive experience, we need a shared vision to guide us." Jeff Jarvis has that vision and will be that guide.

     If you have ever found yourself lost halfway through other introductory materials on this topic, this is the book for you. If you don't understand set terminology such as vectors, hyperplanes, and centroids, then this is also the book for you. This starter course isn't a picture story book but does include many visual examples that break algorithms down into a digestible and practical format. As a starter course, this book connects the dots and offers the crash course I wish I had when I first started. The kind of guide I wish had before I started taking on introductory courses that presume you’re two days away from an advanced mathematics exam. That’s why this introductory course doesn’t go further on the subject than other introductory books, but rather, goes a step back. A half-step back in order to help everyone make his or her first strides in machine learning and is an ideal study companion for the visual learner. In this step-by-step guide you will learn: - How to download free datasets - What tools and software packages you need - Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data - Preparing data for analysis, including k-fold Validation - Regression analysis to create trend lines - Clustering, including k-means and k-nearest Neighbors - Naive Bayes Classifier to predict new classes - Anomaly detection and SVM algorithms to combat anomalies and outliers - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification Please feel welcome to join this starter course by buying a copy, or sending a free sample to your preferred device.

    Starting with Alexander Graham Bell's revolutionary "harmonic telegraph," by the middle of the twentieth century the phone system had grown into something extraordinary, a web of cutting-edge switching machines and human operators that linked together millions of people like never before. But the network had a billion-dollar flaw, and once people discovered it, things would never be the same.Exploding the Phone tells this story in full for the first time. It traces the birth of long-distance communication and the telephone, the rise of AT&T's monopoly, the creation of the sophisticated machines that made it all work, and the discovery of Ma Bell's Achilles' heel. Phil Lapsley expertly weaves together the clandestine underground of "phone phreaks" who turned the network into their electronic playground, the mobsters who exploited its flaws to avoid the feds, the explosion of telephone hacking in the counterculture, and the war between the phreaks, the phone company, and the FBI.The product of extensive original research, Exploding the Phone is a ground-breaking, captivating book.

    What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same constraints, so computer scientists have been grappling with their version of such issues for decades. And the solutions they've found have much to teach us.In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

    But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

    Today, Data is the vital raw material of the information economy. The explosive abundance of this digital asset, more than doubling every two years, is creating a new world of opportunity and challenge.Data-ism is about this next phase, in which vast, Internet-scale data sets are used for discovery and prediction in virtually every field. It is a journey across this emerging world with people, illuminating narrative examples, and insights. It shows that, if exploited, this new revolution will change the way decisions are made—relying more on data and analysis, and less on intuition and experience—and transform the nature of leadership and management.Lohr explains how individuals and institutions will need to exploit, protect, and manage their data to stay competitive in the coming years. Filled with rich examples and anecdotes of the various ways in which the rise of Big Data is affecting everyday life it raises provocative questions about policy and practice that have wide implications for all of our lives.

    The book is broken into two parts, the first deals with security-related reverse engineering and the second explores the more practical aspects of reverse engineering. In addition, the author explains how to reverse engineer a third-party software library to improve interfacing and how to reverse engineer a competitor's software to build a better product. * The first popular book to show how software reverse engineering can help defend against security threats, speed up development, and unlock the secrets of competitive products * Helps developers plug security holes by demonstrating how hackers exploit reverse engineering techniques to crack copy-protection schemes and identify software targets for viruses and other malware * Offers a primer on advanced reverse-engineering, delving into disassembly-code-level reverse engineering-and explaining how to decipher assembly language

    Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer winning in games like Jeopardy and go does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices. Reboot provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better.

    They gleefully whip the media into a frenzy over a fake teen drug crisis; they post offensive messages on Facebook memorial pages, traumatizing grief-stricken friends and family; they use unabashedly racist language and images. They take pleasure in ruining a complete stranger's day and find amusement in their victim's anguish. In short, trolling is the obstacle to a kinder, gentler Internet. To quote a famous Internet meme, trolling is why we can't have nice things online.Or at least that's what we have been led to believe. In this provocative book, Whitney Phillips argues that trolling, widely condemned as obscene and deviant, actually fits comfortably within the contemporary media landscape. Trolling may be obscene, but, Phillips argues, it isn't all that deviant. Trolls' actions are born of and fueled by culturally sanctioned impulses -- which are just as damaging as the trolls' most disruptive behaviors.Phillips describes, for example, the relationship between trolling and sensationalist corporate media -- pointing out that for trolls, exploitation is a leisure activity; for media, it's a business strategy. She shows how trolls, "the grimacing poster children for a socially networked world," align with social media. And she documents how trolls, in addition to parroting media tropes, also offer a grotesque pantomime of dominant cultural tropes, including gendered notions of dominance and success and an ideology of entitlement. We don't just have a trolling problem, Phillips argues; we have a culture problem. This Is Why We Can't Have Nice Things isn't only about trolls; it's about a culture in which trolls thrive.

    For more than a year, Washington Post reporter Robert O'Harrow has explored the threats proliferating in our digital universe. This eBook is a compilation of that reporting. With chapters built around real people, including hackers, security researchers and corporate executives, this book will help regular people, lawmakers and businesses better understand the mind-bending challenge of keeping the internet safe from hackers and security breaches -- and all out war.