It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected. In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge. The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans. The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What hap­pens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others?  Who knows—maybe there’s a bot learning to do your job this minute.

    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 facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

    Glimcher argues that Cartesian dualism operates from the false premise that the reflex is able to describe behavior in the real world that animals inhabit. A mathematically rich cognitive theory, he claims, could solve the most difficult problems that any environment could present, eliminating the need for dualism by eliminating the need for a reflex theory. Such a mathematically rigorous description of the neural processes that connect sensation and action, he explains, will have its roots in microeconomic theory. Economic theory allows physiologists to define both the optimal course of action that an animal might select and a mathematical route by which that optimal solution can be derived. Glimcher outlines what an economics-based cognitive model might look like and how one would begin to test it empirically. Along the way, he presents a fascinating history of neuroscience. He also discusses related questions about determinism, free will, and the stochastic nature of complex behavior.

    The book will teach you about:* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data* Deep learning, a powerful set of techniques for learning in neural networksNeural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

    This 'outside-in' method fueled a generation of brain research and now must confront hidden assumptions about causation and concepts that may not hold neatly for systems that act and react.György Buzsáki's The Brain from Inside Out examines why the outside-in framework for understanding brain function has become stagnant and points to new directions for understanding neural function. Building upon the success of 2011's Rhythms of the Brain, Professor Buzsáki presents the brain as a foretelling device that interacts with its environment through action and the examination of action's consequence. Consider that our brains are initially filled with nonsense patterns, all of which are gibberish until grounded by action-based interactions. By matching these nonsense "words" to the outcomes of action, they acquire meaning. Once its circuits are "calibrated" by action and experience, the brain can disengage from its sensors and actuators, and examine "what happens if" scenarios by peeking into its own computation, a process that we refer to as cognition.The Brain from Inside Out explains why our brain is not an information-absorbing coding device, as it is often portrayed, but a venture-seeking explorer constantly controlling the body to test hypotheses. Our brain does not process information: it creates it.

    This concept is at the root of the computational worldview, which basically says that very complex systems — the world we live in — have their beginnings in simple mathematical equations. We've lately come to understand that such an algorithm is only the start of a never-ending story — the real action occurs in the unfolding consequences of the rules. The chip-in-a-box computers so popular in our time have acted as a kind of microscope, letting us see into the secret machinery of the world. In Lifebox, Rucker uses whimsical drawings, fables, and humor to demonstrate that everything is a computation — that thoughts, computations, and physical processes are all the same. Rucker discusses the linguistic and computational advances that make this kind of "digital philosophy" possible, and explains how, like every great new principle, the computational world view contains the seeds of a next step.

    Gladwell showed us how snap decisions often yield better results than careful analysis. Now, Gigerenzer explains why our intuition is such a powerful decision-making tool. Drawing on a decade of research at the Max Plank Institute, Gigerenzer demonstrates that our gut feelings are actually the result of unconscious mental processes—processes that apply rules of thumb that we’ve derived from our environment and prior experiences. The value of these unconscious rules lies precisely in their difference from rational analysis—they take into account only the most useful bits of information rather than attempting to evaluate all possible factors. By examining various decisions we make—how we choose a spouse, a stock, a medical procedure, or the answer to a million-dollar game show question—Gigerenzer shows how gut feelings not only lead to good practical decisions, but also underlie the moral choices that make our society function. In the tradition of Blink and Freakonomics, Gut Feelings is an exploration of the myriad influences and factors (nature and nurture) that affect how the mind works, grounded in cutting-edge research and conveyed through compelling real-life examples.

    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.

    Lanza has teamed with Bob Berman, the most widely read astronomer in the world, to produce Biocentrism, a revolutionary new view of the universe.Every now and then a simple yet radical idea shakes the very foundations of knowledge. The startling discovery that the world was not flat challenged and ultimately changed the way people perceived themselves and their relationship with the world. For most humans of the 15th century, the notion of Earth as ball of rock was nonsense. The whole of Western, natural philosophy is undergoing a sea change again, increasingly being forced upon us by the experimental findings of quantum theory, and at the same time, towards doubt and uncertainty in the physical explanations of the universe’s genesis and structure. Biocentrism completes this shift in worldview, turning the planet upside down again with the revolutionary view that life creates the universe instead of the other way around.In this paradigm, life is not an accidental byproduct of the laws of physics. Biocentrism takes the reader on a seemingly improbable but ultimately inescapable journey through a foreign universe—our own—from the viewpoints of an acclaimed biologist and a leading astronomer. Switching perspective from physics to biology unlocks the cages in which Western science has unwittingly managed to confine itself. Biocentrism will shatter the reader’s ideas of life—time and space, and even death. At the same time it will release us from the dull worldview of life being merely the activity of an admixture of carbon and a few other elements; it suggests the exhilarating possibility that life is fundamentally immortal.The 21st century is predicted to be the Century of Biology, a shift from the previous century dominated by physics. It seems fitting, then, to begin the century by turning the universe outside-in and unifying the foundations of science with a simple idea discovered by one of the leading life-scientists of our age. Biocentrism awakens in readers a new sense of possibility, and is full of so many shocking new perspectives that the reader will never see reality the same way again.

    In fact, he points out, our cooperative instincts may have evolved as part of mankind?s natural selfish behavior--by exchanging favors we can benefit ourselves as well as others.Brilliantly orchestrating the newest findings of geneticists, psychologists, and anthropologists, The Origins of Virtue re-examines the everyday assumptions upon which we base our actions towards others, whether in our roles as parents, siblings, or trade partners. With the wit and brilliance of The Red Queen, his acclaimed study of human and animal sexuality, Matt Ridley shows us how breakthroughs in computer programming, microbiology, and economics have given us a new perspective on how and why we relate to each other.

    Through the eyes of a curious scientist, she watched her mind deteriorate whereby she could not walk, talk, read, write, or recall any of her life. Because of her understanding of the brain, her respect for the cells in her body, and an amazing mother, Jill completely recovered. In My Stroke of Insight, she shares her recommendations for recovery and the insight she gained into the unique functions of the two halves of her brain. When she lost the skills of her left brain, her consciousness shifted away from normal reality where she felt "at one with the universe." Taylor helps others not only rebuild their brains from trauma, but helps those of us with normal brains better understand how we can consciously influence the neural circuitry underlying what we think, how we feel and how we react to life's circumstances.

    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 living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    Gardner's trailblazing book revolutionized the worlds of education and psychology by positing that rather than a single type of intelligence, we have several--most of which are neglected by standard testing and educational methods.More than 200,00 copies of earlier editions have been sold; this reissue includes a new introduction by the author to mark the twenty-first birthday of this remarkable book.Download PDFhttp://uploading.com/files/ae6de5f6/0...http://www.filesonic.com/file/1882814...http://depositfiles.com/files/vx6nj38a9