Some singularity theorists predict that if the field of artificial intelligence (AI) continues to develop at its current dizzying rate, the singularity could come about in the middle of the present century. Murray Shanahan offers an introduction to the idea of the singularity and considers the ramifications of such a potentially seismic event. Shanahan's aim is not to make predictions but rather to investigate a range of scenarios. Whether we believe that singularity is near or far, likely or impossible, apocalypse or utopia, the very idea raises crucial philosophical and pragmatic questions, forcing us to think seriously about what we want as a species. Shanahan describes technological advances in AI, both biologically inspired and engineered from scratch. Once human-level AI -- theoretically possible, but difficult to accomplish -- has been achieved, he explains, the transition to superintelligent AI could be very rapid. Shanahan considers what the existence of superintelligent machines could mean for such matters as personhood, responsibility, rights, and identity. Some superhuman AI agents might be created to benefit humankind; some might go rogue. (Is Siri the template, or HAL?) The singularity presents both an existential threat to humanity and an existential opportunity for humanity to transcend its limitations. Shanahan makes it clear that we need to imagine both possibilities if we want to bring about the better outcome.

    Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling.--Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library JournalTHE UNTOLD TECH STORY OF OUR TIMEWhat does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create?With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.Long dismissed as a technology of the distant future, artificial intelligence was a project consigned to the fringes of the scientific community. Then two researchers changed everything. One was a sixty-four-year-old computer science professor who didn't drive and didn't fly because he could no longer sit down--but still made his way across North America for the moment that would define a new age of technology. The other was a thirty-six-year-old neuroscientist and chess prodigy who laid claim to being the greatest game player of all time before vowing to build a machine that could do anything the human brain could do.They took two very different paths to that lofty goal, and they disagreed on how quickly it would arrive. But both were soon drawn into the heart of the tech industry. Their ideas drove a new kind of arms race, spanning Google, Microsoft, Facebook, and OpenAI, a new lab founded by Silicon Valley kingpin Elon Musk. But some believed that China would beat them all to the finish line.Genius Makers dramatically presents the fierce conflict between national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice. Like a great Victorian novel, this world of eccentric, brilliant, often unimaginably yet suddenly wealthy characters draws you into the most profound moral questions we can ask. And like a great mystery, it presents the story and facts that lead to a core, vital question:How far will we let it go?

    Rise of the Machines reclaims the spectacular story of cybernetics, one of the twentieth century’s pivotal ideas.Springing from the mind of mathematician Norbert Wiener amid the devastation of World War II, the cybernetic vision underpinned a host of seductive myths about the future of machines. Cybernetics triggered blissful cults and military gizmos, the Whole Earth Catalog and the air force’s foray into virtual space, as well as crypto-anarchists fighting for internet freedom.In Rise of the Machines, Thomas Rid draws on unpublished sources—including interviews with hippies, anarchists, sleuths, and spies—to offer an unparalleled perspective into our anxious embrace of technology.

    Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades. You'll find lucid, easy-to-read coverage of problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis and specific artificial-intelligence accomplishments. Related subjects are also included: predicate-calculus theorem proving, machine architecture, psychological simulation, automatic programming, novel software techniques, industrial automation and much more.A supplementary section updates the original book with major research from the decade 1974-1984. Abundant illustrations, diagrams and photographs enhance the text, and challenging practice exercises at the end of each chapter test the student's grasp of each subject.The combination of introductory and advanced material makes Introduction to Artificial Intelligence ideal for both the layman and the student of mathematics and computer science. For anyone interested in the nature of thought, it will inspire visions of what computer technology might produce tomorrow.

    Targets beginning to intermediate Excel users seeking real-world examples of how they can use Excel's powerful built-in functions Shows readers how to use Excel functions in formulas to help them decide between buying and leasing a car, calculate mortgage costs, compute grades, evaluate investment performance, figure college expenses, and more Gives explanations and examples of real-world situations Provides an abbreviated discussion of an additional 200 functions Excel commands nearly 90 percent of the market for spreadsheet applications; although this book is written for Excel 2003, the functions described are in earlier versions as well

    With SQL and this task-based guide to it, you can do it too—no programming experience required!After going over the relational database model and SQL syntax in the first few chapters, veteran author Chris Fehily launches into the tasks that will get you comfortable with SQL fast. In addition to explaining SQL basics, this updated reference covers the ANSI SQL:2003 standard and contains a wealth of brand-new information, including a new chapter on set operations and common tasks, well-placed optimization tips to make your queries run fast, sidebars on advanced topics, and added IBM DB2 coverage.Best of all, the book's examples were tested on the latest versions of Microsoft Access, Microsoft SQL Server, Oracle, IBM DB2, MySQL, and PostgreSQL. On the companion Web site, you can download the SQL scripts and sample database for all these systems and put your knowledge to work immediately on a real database..

    We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant’s theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence.Offering a powerful and elegant model that encompasses life’s complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.

    *A comprehensive overview of hardware and software issues make this a "must-have" for electrical and computer engineers*Contains new material on RISC processors, performance analysis, multiprocessors and memory systems*New two-color design and illustrations illuminate the text

    Negroponte's fans will want to get a copy of Being Digital, which is an edited version of the 18 articles he wrote for Wired about "being digital." Negroponte's text is mostly a history of media technology rather than a set of predictions for future technologies. In the beginning, he describes the evolution of CD-ROMs, multimedia, hypermedia, HDTV (high-definition television), and more. The section on interfaces is informative, offering an up-to-date history on visual interfaces, graphics, virtual reality (VR), holograms, teleconferencing hardware, the mouse and touch-sensitive interfaces, and speech recognition. In the last chapter and the epilogue, Negroponte offers visionary insight on what "being digital" means for our future. Negroponte praises computers for their educational value but recognizes certain dangers of technological advances, such as increased software and data piracy and huge shifts in our job market that will require workers to transfer their skills to the digital medium. Overall, Being Digital provides an informative history of the rise of technology and some interesting predictions for its future.

    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.

    It's a chunky book of 400 questions that range from the heinous to the nauseating to the downright disturbing, each a field-tested conversation starter—because no matter how strange or far-fetched, Would You Rather...? knows that choice provokes thinking, and thinking is fun. Some questions, like a Rorschach test, reveal values: Would you rather . . . Age only from the neck up -OR- age only from the neck down? Be stupid and rich -OR- smart and poor? Some delight in their own grossness: Eat three earthworms -OR- wear a necklace made of them on your wedding day? Be trapped in an elevator with wet dogs -OR- three fat men with bad breath? Some churn up prejudices: Lose your mate to the same sex as yourself -OR- the opposite sex? Some create that squirming sensation: Get a bad case of poison ivy way up inside your nose -OR- inside your inner ear? Or ethical dilemmas: Be president of a firm that poaches endangered species -OR- work for a corrupt politician? And some are just deliciously absurd: Catch a porcupine thrown from a second-story window -OR- a skunk thrown from the same window? Each question is followed up with related, often off-the-wall information, from odd trivia to dumb jokes to the occasional practical advice (go for the skunk—the porcupine's got 30,000 quills, while tomato juice will take away the skunk smell).

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    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."

    He hides in the corners of conformity. He only flaunts a quotidian nature. He meanders through the mundane and occupies the ordinary. Individual expression and exceptionalism are his enemies. The Gray Man is the forgettable face, the ghost guy, the hidden human. Implementing the concepts is more than looking less tactical, less hostile, or less threatening. It is the willful abandonment of anything and everything that defines oneself as different. Using his unique "S" word conceptual approach featured in Appear to Vanish, camouflage and concealment expert Matthew Dermody discusses the concepts, tactics and mindset necessary to assimilate into any urban environment. From the safety-conscious international traveler to the SERE contingencies of the deep cover foreign operative, GRAY MAN is the definitive urban concealment resource.

    You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled "Python for Informatics: Exploring Information".