C. R. Licklider, whose visionary dream of a human-computer symbiosis transformed the course of modern science and led to the development of the personal computer. Reprint.

    This may seem hard to accept, but, as Harari explains in his trademark style—thorough, yet riveting—famine, plague and war have been transformed from incomprehensible and uncontrollable forces of nature into manageable challenges. For the first time ever, more people die from eating too much than from eating too little; more people die from old age than from infectious diseases; and more people commit suicide than are killed by soldiers, terrorists and criminals put together. The average American is a thousand times more likely to die from binging at McDonalds than from being blown up by Al Qaeda.What then will replace famine, plague, and war at the top of the human agenda? As the self-made gods of planet earth, what destinies will we set ourselves, and which quests will we undertake? Homo Deus explores the projects, dreams and nightmares that will shape the twenty-first century—from overcoming death to creating artificial life. It asks the fundamental questions: Where do we go from here? And how will we protect this fragile world from our own destructive powers? This is the next stage of evolution. This is Homo Deus.With the same insight and clarity that made Sapiens an international hit and a New York Times bestseller, Harari maps out our future.

    Your online and in-store purchasing patterns are recorded, and reveal if you're unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you’re thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it.The powers that surveil us do more than simply store this information. Corporations use surveillance to manipulate not only the news articles and advertisements we each see, but also the prices we’re offered. Governments use surveillance to discriminate, censor, chill free speech, and put people in danger worldwide. And both sides share this information with each other or, even worse, lose it to cybercriminals in huge data breaches.Much of this is voluntary: we cooperate with corporate surveillance because it promises us convenience, and we submit to government surveillance because it promises us protection. The result is a mass surveillance society of our own making. But have we given up more than we’ve gained? In Data and Goliath, security expert Bruce Schneier offers another path, one that values both security and privacy. He brings his bestseller up-to-date with a new preface covering the latest developments, and then shows us exactly what we can do to reform government surveillance programs, shake up surveillance-based business models, and protect our individual privacy. You'll never look at your phone, your computer, your credit cards, or even your car in the same way again.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    It already drives most of the world’s top technology companies. And now it’s starting to spread to every domain where leaders wrestle with complex projects. If you’ve ever been startled by how fast the world is changing, Scrum is one of the reasons why. Productivity gains of as much as 1200% have been recorded, and there’s no more lucid – or compelling – explainer of Scrum and its bright promise than Jeff Sutherland, the man who put together the first Scrum team more than twenty years ago. The thorny problem Jeff began tackling back then boils down to this: people are spectacularly bad at doing things with agility and efficiency. Best laid plans go up in smoke. Teams often work at cross purposes to each other. And when the pressure rises, unhappiness soars. Drawing on his experience as a West Point-educated fighter pilot, biometrics expert, early innovator of ATM technology, and V.P. of engineering or CTO at eleven different technology companies, Jeff began challenging those dysfunctional realities, looking for solutions that would have global impact. In this book you’ll journey to Scrum’s front lines where Jeff’s system of deep accountability, team interaction, and constant iterative improvement is, among other feats, bringing the FBI into the 21st century, perfecting the design of an affordable 140 mile per hour/100 mile per gallon car, helping NPR report fast-moving action in the Middle East, changing the way pharmacists interact with patients, reducing poverty in the Third World, and even helping people plan their weddings and accomplish weekend chores. Woven with insights from martial arts, judicial decision making, advanced aerial combat, robotics, and many other disciplines, Scrum is consistently riveting. But the most important reason to read this book is that it may just help you achieve what others consider unachievable – whether it be inventing a trailblazing technology, devising a new system of education, pioneering a way to feed the hungry, or, closer to home, a building a foundation for your family to thrive and prosper.

    Most of the time this math works quietly behind the scenes . . . until it doesn't. All sorts of seemingly innocuous mathematical mistakes can have significant consequences.Math is easy to ignore until a misplaced decimal point upends the stock market, a unit conversion error causes a plane to crash, or someone divides by zero and stalls a battleship in the middle of the ocean.Exploring and explaining a litany of glitches, near misses, and mathematical mishaps involving the internet, big data, elections, street signs, lotteries, the Roman Empire, and an Olympic team, Matt Parker uncovers the bizarre ways math trips us up, and what this reveals about its essential place in our world. Getting it wrong has never been more fun.

    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.

    And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries. Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who’s ever wondered about the secret inner life of computers and other smart machines. It’s a cleverly illustrated and eminently comprehensible story—and along the way, you’ll discover you’ve gained a real context for understanding today’s world of PCs, digital media, and the Internet. No matter what your level of technical savvy, CODE will charm you—and perhaps even awaken the technophile within.

    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.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    But what is it physically? And where is it really? Our mental map of the network is as blank as the map of the ocean that Columbus carried on his first Atlantic voyage. The Internet, its material nuts and bolts, is an unexplored territory. Until now.In Tubes, journalist Andrew Blum goes inside the Internet's physical infrastructure and flips on the lights, revealing an utterly fresh look at the online world we think we know. It is a shockingly tactile realm of unmarked compounds, populated by a special caste of engineer who pieces together our networks by hand; where glass fibers pulse with light and creaky telegraph buildings, tortuously rewired, become communication hubs once again. From the room in Los Angeles where the Internet first flickered to life to the caverns beneath Manhattan where new fiber-optic cable is buried; from the coast of Portugal, where a ten-thousand-mile undersea cable just two thumbs wide connects Europe and Africa, to the wilds of the Pacific Northwest, where Google, Microsoft, and Facebook have built monumental data centers—Blum chronicles the dramatic story of the Internet's development, explains how it all works, and takes the first-ever in-depth look inside its hidden monuments.This is a book about real places on the map: their sounds and smells, their storied pasts, their physical details, and the people who live there. For all the talk of the "placelessness" of our digital age, the Internet is as fixed in real, physical spaces as the railroad or telephone. You can map it and touch it, and you can visit it. Is the Internet in fact "a series of tubes" as Ted Stevens, the late senator from Alaska, once famously described it? How can we know the Internet's possibilities if we don't know its parts?Like Tracy Kidder's classic The Soul of a New Machine or Tom Vanderbilt's recent bestseller Traffic, Tubes combines on-the-ground reporting and lucid explanation into an engaging, mind-bending narrative to help us understand the physical world that underlies our digital lives.

    Drawing on insights from evolutionary biology, neuroscience, and linguistics, Torey reconstructs the sequence of events by which "Homo erectus" became "Homo sapiens." He describes the augmented functioning that underpins the emergent mind -- a new ("off-line") internal response system with which the brain accesses itself and then forms a selection mechanism for mentally generated behavior options. This functional breakthrough, Torey argues, explains how the animal brain's "awareness" became self-accessible and reflective -- that is, how the human brain acquired a conscious mind. Consciousness, unlike animal awareness, is not a unitary phenomenon but a composite process. Torey's account shows how protolanguage evolved into language, how a brain subsystem for the emergent mind was built, and why these developments are opaque to introspection. We experience the brain's functional autonomy, he argues, as free will. Torey proposes that once life began, consciousness had to emerge -- because consciousness is the informational source of the brain's behavioral response. Consciousness, he argues, is not a newly acquired "quality," "cosmic principle," "circuitry arrangement," or "epiphenomenon," as others have argued, but an indispensable working component of the living system's manner of functioning.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life. In A Mind for Numbers, Dr. Oakley lets us in on the secrets to effectively learning math and science—secrets that even dedicated and successful students wish they’d known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there’s only one way to do a problem, when in actuality, there are often a number of different solutions—you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn math. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. A Mind for Numbers shows us that we all have what it takes to excel in math, and learning it is not as painful as some might think!