This booklet contains the prophetic and epochal lectures delivered by Swami Vivekananda at the Parliament of Religions, Chicago, in 1893

    AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” —Steven D. Levitt, bestselling co-author of Freakonomics From leading data scientists Nick Polson and James Scott, what everyone needs to know to understand how artificial intelligence is changing the world and how we can use this knowledge to make better decisions in our own lives. Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the 21st century in the same way that the Industrial Revolution remade the world in the 19th century. AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language—but in an unconventional way, anchored in stories rather than equations. You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability, and better thinking. Along the way, you'll see how these same ideas are playing out in the modern age of big data and intelligent machines—and how these technologies will soon help you to overcome some of your built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    But the truth is that Artificial Intelligence is already among us. It exists in our smartphones, fitness trackers, and refrigerators that tell us when the milk will expire. In some ways, the future people dreamed of at the World's Fair in the 1960s is already here. We're teaching our machines how to think like humans, and they're learning at an incredible rate.In Thinking Machines, technology journalist Luke Dormehl takes you through the history of AI and how it makes up the foundations of the machines that think for us today. Furthermore, Dormehl speculates on the incredible--and possibly terrifying--future that's much closer than many would imagine. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to broaden itself to include intelligent machines.

    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.

    The show combines a surfeit of mad science, some old-school sci-fi flair, and a dash of strawberry-milkshake whimsy to create the challenging, fascinating Pattern that keeps us coming back season after season and universe after universe.Now, in Fringe Science, cutting-edge scientists, science writers, and science fiction authors and historians provide a smart, savvy, and accessible look at the world(s) of Fringe.MIT physics professor Max Tegmark illuminates the real-life possibilities of parallel universesStephen Cass, founding editor of Discover's Science Not Fiction blog and a Senior Editor with Technology Review, unravels Fringe's use of time travelAward-winning science fiction historian Amy H. Sturgis walks us through the show's literary and television ancestors, from the 1800s onTelevision Without Pity staff writer Jacob Clifton looks at the role of the scientist, and scientific redemption, through the ever-shifting role of Massive DynamicGarth Sundem, bestselling author of Brain Candy, explores the mysterious way that memory works, from why Walter forgets to how Olivia remembers And more, from lab cow Gene's scientific résumé to why the Observers should be wearing white lab coats

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    We're baffled when we're confronted with complex challenges like recession, political gridlock, climate change, childhood obesity, pollution, and failing schools. We see them as separate ills, each requiring a separate remedy-if we can imagine a remedy at all.Why are so many jobs disappearing? Why are a few people getting rich while the rest of us struggle? How can we pay for the costs of healthcare? Why can't our trusted institutions behave ethically? What's the cause of governmental gridlock? How can we afford to educate our children? How do we stop damaging the ecosystem? Why do we create ugliness?Author Marty Neumeier suggests that these problems are merely symptoms of a much larger problem-our inability to deal with interconnected, non-linear, and amorphous challenges. It's not that our problems are too difficult, he argues, but that our skills are too basic. Success in the post-industrial era demands that we move our thinking from the static, the linear, and the step-by-step to the dynamic, the holistic, and the all-at-once.In this sweeping vision for personal mastery in a post-industrial era, Neumeier presents five metaskills-feeling, seeing, dreaming, making, and learning-that can help you reach your true potential. They'll keep you two or three steps ahead of the machines, the algorithms, and the outsourcing forces of the "robot curve". They'll also bring you greater creativity, higher purpose, and a deeper sense of fulfillment.Metaskills is more than a manifesto. It's a compass for visionary leaders, policymakers, educators, and planners. It's a creative framework for designers, engineers, scientists, and artists. It's a picture of the future that allows people from a wide range of disciplines, industries, and professions to envision new ways to create value together. Perhaps more important, it's a long-overdue examination of what it means to be human in the 21st century.

    In the next ten years, change will happen even faster. As Hillary Clinton's Senior Advisor for Innovation, Alec Ross travelled nearly a million miles to forty-one countries, the equivalent of two round-trips to the moon. From refugee camps in the Congo and Syrian war zones, to visiting the world's most powerful people in business and government, Ross's travels amounted to a four-year masterclass in the changing nature of innovation. In The Industries of the Future, Ross distils his observations on the forces that are changing the world. He highlights the best opportunities for progress and explains how countries thrive or sputter. Ross examines the specific fields that will most shape our economic future over the next ten years, including robotics, artificial intelligence, the commercialization of genomics, cybercrime and the impact of digital technology. Blending storytelling and economic analysis, he answers questions on how we will need to adapt. Ross gives readers a vivid and informed perspective on how sweeping global trends are affecting the ways we live, now and tomorrow.

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

    The changes ahead will challenge and alter fundamental concepts such as national identity, human rights, money, and markets. In this pivotal, complicated moment, what are the great questions we need to ask to navigate our way forward?David Rothkopf believes in the power of questions. When sweeping changes have occurred in history—the religious awakenings of the Reformation; the scientific advances of the Age of Exploration; the technological developments of the Renaissance, the Enlightenment, and the Industrial Revolution—they have brought with them, not just new knowledge, but provoked great questions about how we must live. With the world at the threshold of profound change, Rothkopf seeks the important questions of our time—ones that will remake the world and our understanding of it. From the foundational questions: "Why do we live within a society?" and "What is war?" to modern concerns such as "Is access to the internet a basic human right?" The Great Questions of Tomorrow confronts our approach to the future and forces us to reimagine fundamental aspects of our lives—identity, economics, technology, government, war, and peace.

    But what if we could escape these trappings? With The Possibility Principle, Mel Schwartz emerges as one of the first psychotherapists to distill the basic premises of quantum theory into an empowering and practical system for transcending limitations and opening to infinite possibility. New discoveries in quantum physics are revolutionizing the way we understand our world, but we're often unclear about how this applies to our own experience. Using three core tenets of quantum physics--inseparability, potentiality, and uncertainty--Schwartz demonstrates how each of us can overcome difficulties and live our fullest potential, so long as we are willing to challenge our operating beliefs. Drawing from his vast body of research and dozens of client success stories, Schwartz shows us how to break through communication impasses, create resilient relationships, build authentic self-esteem, overcome anxiety and depression, and catalyze our defining moments so we can live more fearless and expansive lives.

    Essays include: "On Three Ways of Writing for Children", "On Science Fiction", "The Hobbit", "Tolkien's The Lord of the Rings", and "George Orwell" He also comments on the novels of Charles Williams, Ryder Haggard and Dorothy L Sayers.

    The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.