Drawing on research by their team at the Center for Digital Business, they show that there's been no stagnation in technology -- in fact, the digital revolution is accelerating. Recent advances are the stuff of science fiction: computers now drive cars in traffic, translate between human languages effectively, and beat the best human Jeopardy! players.As these examples show, digital technologies are rapidly encroaching on skills that used to belong to humans alone. This phenomenon is both broad and deep, and has profound economic implications. Many of these implications are positive; digital innovation increases productivity, reduces prices (sometimes to zero), and grows the overall economic pie.But digital innovation has also changed how the economic pie is distributed, and here the news is not good for the median worker. As technology races ahead, it can leave many people behind. Workers whose skills have been mastered by computers have less to offer the job market, and see their wages and prospects shrink. Entrepreneurial business models, new organizational structures and different institutions are needed to ensure that the average worker is not left behind by cutting-edge machines.In Race Against the Machine Brynjolfsson and McAfee bring together a range of statistics, examples, and arguments to show that technological progress is accelerating, and that this trend has deep consequences for skills, wages, and jobs. The book makes the case that employment prospects are grim for many today not because there's been technology has stagnated, but instead because we humans and our organizations aren't keeping up.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    The book also provides a balanced presentation of time-varying and static fields, preparingstudents for employment in today's industrial and manufacturing sectors. Streamlined to facilitate student understanding, this edition features worked examples in every chapter that explain how to use the theory presented in the text to solve different kinds of problems. Numerical methods, including MATLAB and vector analysis, are also included to help students analyzesituations that they are likely to encounter in industry practice. Elements of Electromagnetics, Fifth Edition, is designed for introductory undergraduate courses in electromagnetics.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    He traces AI's origins in ancient myth, through literary classics like Frankenstein, to today's sci-fi blockbusters, arguing that a fascination with AI is hardwired into the human psyche. He explains AI's history, technology, and potential; its manifestations in intelligent machines; its connections to neurology and consciousness, as well as—perhaps most tellingly—what AI reveals about us as human beings.In Our Own Image argues that we are on the brink of a fourth industrial revolution—poised to enter the age of Artificial Intelligence as science fiction becomes science fact. Ultimately, Zarkadakis observes, the fate of AI has profound implications for the future of science and humanity itself.

    Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

    Meet Bina48, the world's most sentient robot, commissioned by Martine Rothblatt and created by Hanson Robotics. Bina48 is a nascent Mindclone of Martine's wife that can engage in conversation, answer questions, and even have spontaneous thoughts that are derived from multimedia data in a Mindfile created by the real Bina. If you're active on Twitter or Facebook, share photos through Instagram, or blogging regularly, you're already on your way to creating a Mindfile—a digital database of your thoughts, memories, feelings, and opinions that is essentially a back-up copy of your mind. Soon, this Mindfile can be made conscious with special software—Mindware—that mimics the way human brains organize information, create emotions and achieve self-awareness. This may sound like science-fiction A.I. (artificial intelligence), but the nascent technology already exists. Thousands of software engineers across the globe are working to create cyberconsciousness based on human consciousness and the Obama administration recently announced plans to invest in a decade-long Brain Activity Map project. Virtually Human is the only book to examine the ethical issues relating to cyberconsciousness and Rothblatt, with a Ph.D. in medical ethics, is uniquely qualified to lead the dialogue.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    Servers give sysadmins a incredible visibility into the network—once they know how to unlock it. Most sysadmins don’t need to understand window scaling, or the differences between IPv4 and IPv6 echo requests, or other intricacies of the TCP/IP protocols. You need only enough to deploy your own applications and get easy support from the network team.This book teaches you:•How modern networks really work•The essentials of TCP/IP•The next-generation protocol, IPv6•The right tools to diagnose network problems, and how to use them•Troubleshooting everything from the physical wire to DNS•How to see the traffic you send and receive•Connectivity testing•How to communicate with your network team to quickly resolve problemsA systems administrator doesn’t need to know the innards of TCP/IP, but knowing enough to diagnose your own network issues transforms a good sysadmin into a great one.

    Quine's areas of interest are panoramic, as this lively book amply demonstrates.Moving from A (alphabet) to Z (zero), Quiddities roams through more than eighty topics, each providing a full measure of piquant thought, wordplay, and wisdom, couched in easy and elegant prose--"Quine at his unbuttoned best," in Donald Davidson's words. Philosophy, language, and mathematics are the subjects most fully represented; tides of entries include belief, communication, free will, idiotisms, longitude and latitude, marks, prizes, Latin pronunciation, tolerance, trinity. Even the more technical entries are larded with homely lore, anecdote, and whimsical humor.Quiddities will be a treat for admirers of Quine and for others who like to think, who care about language, and who enjoy the free play of intellect on topics large and small. For this select audience, it is an ideal book for browsing.

    Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.

    Explore computer buildings (datacenters), the flat rocks we live on (tectonic plates), the things you use to steer a plane (airliner cockpit controls), and the little bags of water you're made of (cells).

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.