Another is composing classical music. Labs are creating life-forms from synthetic DNA. A doctor designs an artificial trachea, uses a 3D printer to produce it, and implants it and saves a child's life. Astonishing technological advances like these are arriving in increasing numbers. Scholar and entrepreneur Vivek Wadhwa uses this book to alert us to dozens of them and raise important questions about what they may mean for us. Breakthroughs such as personalized genomics, self-driving vehicles, drones, and artificial intelligence could make our lives healthier, safer, and easier. But the same technologies raise the specter of a frightening, alienating future: eugenics, a jobless economy, complete loss of privacy, and ever-worsening economic inequality. As Wadhwa puts it, our choices will determine if our future is Star Trek or Mad Max. Wadhwa offers us three questions to ask about every emerging technology: Does it have the potential to benefit everyone equally? What are its risks and rewards? And does it promote autonomy or dependence? Looking at a broad array of advances in this light, he emphasizes that the future is up to us to create--that even if our hands are not on the wheel, we will decide the driverless car's destination.ContentsPreface IntroductionPART ONE: The Here and Now1. A bitter taste of dystopia2. Welcome to Moore’s world3. How change will affect us personally and why choices matter4. If change is always the answer, what are the questions?PART TWO: Does the technology have the potential to benefit everyone equally?5. The amazing and scary rise of Artificial Intelligence6. Remaking education with avatars and A.I.7. We are becoming data; our doctors, softwarePART THREE: What are the risks and the rewards?8. Robotics and Biology: The inevitable merging of man and machine9. Security and privacy in an era of ubiquitous connectivity10. The drones are coming11. Designer genes, the bacteria in our guts, and precision medicinePART FOUR: Does the technology foster autonomy or dependency?12. Your own private driver: Self-driving cars, trucks, and planes13. When your scale talks to your refrigerator: The Internet of Things14. The future of your body is electric15. Almost free energy and foodConclusion: So will it be Star Trek or Mad Max?NotesAcknowledgmentsIndexAbout the authors

    Even as they bring ease to our lives, these programs are stealing something essential from us.Drawing on psychological and neurological studies that underscore how tightly people’s happiness and satisfaction are tied to performing hard work in the real world, Carr reveals something we already suspect: shifting our attention to computer screens can leave us disengaged and discontented.From nineteenth-century textile mills to the cockpits of modern jets, from the frozen hunting grounds of Inuit tribes to the sterile landscapes of GPS maps, The Glass Cage explores the impact of automation from a deeply human perspective, examining the personal as well as the economic consequences of our growing dependence on computers.With a characteristic blend of history and philosophy, poetry and science, Carr takes us on a journey from the work and early theory of Adam Smith and Alfred North Whitehead to the latest research into human attention, memory, and happiness, culminating in a moving meditation on how we can use technology to expand the human experience.

    

    After decades of sci-fi fantasies and hype, artificial intelligence has leapt out of research labs and Silicon Valley engineering departments and into the center of our lives. Algorithms shape everything around us, from the news we see to the products we buy and the relationships we form. And while the debate over whether or not automation will destroy jobs rages on, a much more important question is being ignored:What does it mean to be a human in a world that is increasingly built by and for machines?In Futureproof: 9 Rules for Humans in the Age of Automation, New York Times technology columnist Kevin Roose lays out a hopeful, pragmatic vision of how people can succeed in the machine age by making themselves irreplaceably human. He shares the secrets of people and organizations that have survived technological change, and explains how we can protect our own futures, with lessons like- Do work that is surprising, social, and scarce (the types of work machines can't do). - Demote your phone. - Work near other people. - Treat A.I. like an army of chimpanzees. - Add more friction to your life.Roose rejects the conventional wisdom that in order to compete with machines, we have to become more like them--hyper-efficient, data-driven, code-writing workhorses. Instead, he says, we should let machines be machines, and focus on doing the kinds of creative, inspiring, and meaningful things only humans can do.

    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.

    But MIT Media Lab researcher and technology policy expert Kate Darling argues just the opposite, and that treating robots with a bit of humanity, more like the way we treat animals, will actually serve us better. From a social, legal, and ethical perspective, she shows that our current ways of thinking don't leave room for the robot technology that is soon to become part of our everyday routines. Robots are likely to supplement--rather than replace--our own skills and relationships. So if we consider our history of incorporating animals into our work, transportation, military, and even families, we actually have a solid basis for how to contend with this future.A deeply original analysis of our technological future and the ethical dilemmas that await us, The New Breed explains how the treatment of machines can reveal a new understanding of our own history, our own systems and how we relate--not just to non-humans, but also to each other.

    By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.

    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.

    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.

    

    It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    Mind Children, written by an internationally renowned roboticist, offers a comparable experience--a mind-boggling glimpse of a world we may soon share with our artificial progeny. Filled with fresh ideas and insights, this book is one of the most engaging and controversial visions of the future ever written by a serious scholar.Hans Moravec convincingly argues that we are approaching a watershed in the history of life--a time when the boundaries between biological and postbiological intelligence will begin to dissolve. Within forty years, Moravec believes, we will achieve human equivalence in our machines, not only in their capacity to reason but also in their ability to perceive, interact with, and change their complex environment. The critical factor is mobility. A computer rooted to one place is doomed to static iterations, whereas a machine on the prowl, like a mobile organism, must evolve a richer fund of knowledge about an ever-changing world upon which to base its actions.In order to achieve anything near human equivalence, robots will need, at the least, the capacity to perform ten trillion calculations per second. Given the trillion-fold increase in computational power since the end of the nineteenth century, and the promise of exotic technologies far surpassing the now-familiar lasers and even superconductors, Moravec concludes that our hardware will have no trouble meeting this forty-year timetable.But human equivalence is just the beginning, not an upper bound. Once the tireless thinking capacity of robots is directed to the problem of their own improvement and reproduction, even the sky will not limit their voracious exploration of the universe. In the concluding chapters Moravec challenges us to imagine with him the possibilities and pitfalls of such a scenario. Rather than warning us of takeover by robots, the author invites us, as we approach the end of this millennium, to speculate about a plausible, wonderful postbiological future and the ways in which our minds might participate in its unfolding.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases