It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried."The first approach has been tried many times in both science fiction and reality. In this new novella, at over 30,000 words, his longest work to date, Ted Chiang offers a detailed imagining of how the second approach might work within the contemporary landscape of startup companies, massively-multiplayer online gaming, and open-source software. It's a story of two people and the artificial intelligences they helped create, following them for more than a decade as they deal with the upgrades and obsolescence that are inevitable in the world of software. At the same time, it's an examination of the difference between processing power and intelligence, and of what it means to have a real relationship with an artificial entity.

    Conflict between humans and machines is seen as inevitable and its outcome all too predictable.In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage.If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial.In a 2014 editorial co-authored with Stephen Hawking, Russell wrote, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." Solving the problem of control over AI is not just possible; it is the key that unlocks a future of unlimited promise.

    It gives an overview of every learning objective for the TOGAF 9 Foundation Syllabus and in-depth coverage on preparing and taking the TOGAF 9 Part 1 Examination. It is specifically designed to help individuals prepare for certification.This Study Guide is excellent material for:a) Individuals who require a basic understanding of TOGAF 9b) Professionals who are working in roles associated with an architecture project such as those responsible for planning, execution, development, delivery, and operationc) Architects who are looking for a first introduction to TOGAF 9d) Architects who want to achieve Level 2 certification in a stepwise manner and have not previously qualified as TOGAF 8 CertifiedA prior knowledge of enterprise architecture is advantageous but not required. While reading this Study Guide, the reader should also refer to the TOGAF Version 9.1 documentation available online at www.opengroup.org and also available as hard copy from www.vanharen.net and online booksellers

    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.

    But even though Moravec predicts the end of the domination by human beings, his is not a bleak vision. Far from railing against a future in which machines rule the world, Moravec embraces it, taking the startling view that intelligent robots will actually be our evolutionary heirs. Intelligent machines, which will grow from us, learn our skills, and share our goals and values, can be viewed as children of our minds. And since they are our children, we will want them to outdistance us. In fact, in a bid for immortality, many of our descendants will choose to transform into ex humans, as they upload themselves into advanced computers.This provocative new book, the highly anticipated follow-up to his bestselling volume Mind Children, charts the trajectory of robotics in breathtaking detail. A must read for artificial intelligence, technology, and computer enthusiasts, Moravec's freewheeling but informed speculations present a future far different than we ever dared imagine.

    Scientists have made progress: computers can now beat chess grandmasters and help prevent terrorist attacks. Yet we still await a machine that exhibits the rich complexity of human thought — one that doesn’t just crunch numbers, or take us to a relevant Web page, but understands us and gives us what we need.That vision has driven a team of engineers at IBM. Over three years, they created “Watson” and prepared it for a showdown on Jeopardy!, where it would take on two of the game’s all-time champions, Ken Jennings and Brad Rutter, in a nationally televised event. Final Jeopardy is the entertaining, illuminating story of that computer and that epic match.It’s a classic tale of Man vs. Machine. Like its human competitors, Watson has to understand language, including puns and irony, and master everything from history, literature, and science to arts, entertainment, and game strategy. After years of training, Watson can find the scrambled state capital in “Hair Gel” (“What is Raleigh?”) and even come up with the facial accessory that made Moshe Dayan recognizable worldwide (“What is an eye patch?”). Watson may just be the smartest machine on earth.Final Jeopardy traces the arc of Watson’s “life,” from its birth in the IBM labs to its big night on the podium. We meet Hollywood moguls and Jeopardy! masters, genius computer programmers and ambitious scientists, including Watson’s eccentric creator, David Ferrucci. We gain access to Ferrucci’s War Room, where the IBM team works tirelessly to boost Watson’s speed to the buzzer, improve its performance in “train wreck” categories (such as “Books in Español”), and fix glitches like the speech defect Watson developed during its testing phase, when it started adding a d to words ending in n (“What is Pakistand?”).Much is at stake, especially for IBM. A new generation of Watsons could transform medicine, the law, marketing, even science itself, as machines process huge amounts of data at lightning speed, answer our questions, and possibly come up with new hypotheses.Showdown aside, it’s clear that the future has arrived. But with it come questions: Where does it leave humans? What will Watson’s heirs be capable of in ten or twenty years? Is it time to declare defeat in the realm of facts? What should we teach our children? And what should we carry around in our own heads?Final Jeopardy takes on these questions and more in a narrative that’s as fast and fun as the game itself. Baker shows us how smart machines will fit into our world — and how they’ll disrupt it. www.finaljeopardy.net

    The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    We are so eager to do everything digitally--hiring, driving, paying bills, even choosing romantic partners--that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology--and issues a warning that we should never assume that computers always get things right.Making a case against technochauvinism--the belief that technology is always the solution--Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding "the cyborg future is not coming any time soon"; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    Pulitzer prize-winning New York Times science writer John Markoff argues that we must decide to design ourselves into our future, or risk being excluded from it altogether.In the past decade, Google introduced us to driverless cars; Apple debuted Siri, a personal assistant that we keep in our pockets; and an Internet of Things connected the smaller tasks of everyday life to the farthest reaches of the Web. Robots have become an integral part of society on the battlefield and the road; in business, education, and health care. Cheap sensors and powerful computers will ensure that in the coming years, these robots will act on their own. This new era offers the promise of immensely powerful machines, but it also reframes a question first raised more than half a century ago, when the intelligent machine was born. Will we control these systems, or will they control us?In Machines of Loving Grace, John Markoff offers a sweeping history of the complicated and evolving relationship between humans and computers. In recent years, the pace of technological change has accelerated dramatically, posing an ethical quandary. If humans delegate decisions to machines, who will be responsible for the consequences? As Markoff chronicles the history of automation, from the birth of the artificial intelligence and intelligence augmentation communities in the 1950s and 1960s, to the modern-day brain trusts at Google and Apple in Silicon Valley, and on to the expanding robotics economy around Boston, he traces the different ways developers have addressed this fundamental problem and urges them to carefully consider the consequences of their work. We are on the brink of the next stage of the computer revolution, Markoff argues, and robots will profoundly transform modern life. Yet it remains for us to determine whether this new world will be a utopia. Moreover, it is now incumbent upon the designers of these robots to draw a bright line between what is human and what is machine.After nearly forty years covering the tech industry, Markoff offers an unmatched perspective on the most drastic technology-driven societal shifts since the introduction of the Internet. Machines of Loving Grace draws on an extensive array of research and interviews to present an eye-opening history of one of the most pressing questions of our time, and urges us to remember that we still have the opportunity to design ourselves into the future—before it's too late.

    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.

    By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

    Another AI discovered a new antibiotic by analyzing molecular properties human scientists did not understand. Now, AI-powered jets are defeating experienced human pilots in simulated dogfights. AI is coming online in searching, streaming, medicine, education, and many other fields and, in so doing, transforming how humans are experiencing reality.In The Age of AI, three leading thinkers have come together to consider how AI will change our relationships with knowledge, politics, and the societies in which we live. The Age of AI is an essential roadmap to our present and our future, an era unlike any that has come before.

    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.