Just as a single sick person can start an epidemic of the flu, so too can a small but precisely targeted push cause a fashion trend, the popularity of a new product, or a drop in the crime rate. This widely acclaimed bestseller, in which Malcolm Gladwell explores and brilliantly illuminates the tipping point phenomenon, is already changing the way people throughout the world think about selling products and disseminating ideas.Gladwell introduces us to the particular personality types who are natural pollinators of new ideas and trends, the people who create the phenomenon of word of mouth. He analyzes fashion trends, smoking, children's television, direct mail, and the early days of the American Revolution for clues about making ideas infectious, and visits a religious commune, a successful high-tech company, and one of the world's greatest salesmen to show how to start and sustain social epidemics.

    Bannon had long sensed that deep within America's soul lurked an explosive tension. Cambridge Analytica had the data to prove it, and in 2016 Bannon had a presidential campaign to use as his proving ground.Christopher Wylie might have seemed an unlikely figure to be at the center of such an operation. Canadian and liberal in his politics, he was only twenty-four when he got a job with a London firm that worked with the U.K. Ministry of Defense and was charged putatively with helping to build a team of data scientists to create new tools to identify and combat radical extremism online. In short order, those same military tools were turned to political purposes, and Cambridge Analytica was born. Wylie's decision to become a whistleblower prompted the largest data crime investigation in history. His story is both exposé and dire warning about a sudden problem born of very new and powerful capabilities. It has not only exposed the profound vulnerabilities and profound carelessness in the enormous companies that drive the attention economy, it has also exposed the profound vulnerabilities of democracy itself. What happened in 2016 was just a trial run. Ruthless actors are coming for your data, and they want to control what you think.

    And, the Director of Google Ideas, Jared Cohen, formerly an advisor to both Secretaries of State Condoleezza Rice and Hillary Clinton.Never before has the future been so vividly and transparently imagined. From technologies that will change lives (information systems that greatly increase productivity, safety and our quality of life, thought controlled motion technology that can revolutionize medical procedures, and near-perfect translation technology that allows us to have more diversified interactions) to our most important future considerations (curating our online identity and fighting those who would do harm with it) to the widespread political change that will transform the globe (through transformations in conflict, increasingly active and global citizenries, a new wave of cyber-terrorism and states operating simultaneously in the physical and virtual realms) to the ever present threats to our privacy and security, Schmidt and Cohen outline in great detail and scope all the promise and peril awaiting us in the coming decades.

    In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract incidents that never occur in real life, but rather touches everything we do—the whole world is shot through with it.Math allows us to see the hidden structures underneath the messy and chaotic surface of our world. It’s a science of not being wrong, hammered out by centuries of hard work and argument. Armed with the tools of mathematics, we can see through to the true meaning of information we take for granted: How early should you get to the airport? What does “public opinion” really represent? Why do tall parents have shorter children? Who really won Florida in 2000? And how likely are you, really, to develop cancer?How Not to Be Wrong presents the surprising revelations behind all of these questions and many more, using the mathematician’s method of analyzing life and exposing the hard-won insights of the academic community to the layman—minus the jargon. Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia’s views on crime and punishment, the psychology of slime molds, what Facebook can and can’t figure out about you, and the existence of God.Ellenberg pulls from history as well as from the latest theoretical developments to provide those not trained in math with the knowledge they need. Math, as Ellenberg says, is “an atomic-powered prosthesis that you attach to your common sense, vastly multiplying its reach and strength.” With the tools of mathematics in hand, you can understand the world in a deeper, more meaningful way. How Not to Be Wrong will show you how.

    Based on exhaustive research and interviews with driving experts and traffic officials around the globe, Traffic gets under the hood of the everyday activity of driving to uncover the surprisingly complex web of physical, psychological, and technical factors that explain how traffic works, why we drive the way we do, and what our driving says about us. Vanderbilt examines the perceptual limits and cognitive underpinnings that make us worse drivers than we think we are. He demonstrates why plans to protect pedestrians from cars often lead to more accidents. He shows how roundabouts, which can feel dangerous and chaotic, actually make roads safer and reduce traffic in the bargain. He uncovers who is more likely to honk at whom, and why. He explains why traffic jams form, outlines the unintended consequences of our quest for safety, and even identifies the most common mistake drivers make in parking lots. The car has long been a central part of American life; whether we see it as a symbol of freedom or a symptom of sprawl, we define ourselves by what and how we drive. As Vanderbilt shows, driving is a provocatively revealing prism for examining how our minds work and the ways in which we interact with one another. Ultimately, Traffic is about more than driving: it s about human nature. This book will change the way we see ourselves and the world around us. And who knows? It may even make us better drivers."

    It could forever change how you experience and interact with your physical surroundings, open your eyes to the perversity of bad design and the desirability of good design, and raise your expectations about how things should be designed.B & W photographs and illustrations throughout.

    

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    It’s not just that the grid has grown old and is now in dire need of basic repair. Today, as we invest great hope in new energy sources--solar, wind, and other alternatives--the grid is what stands most firmly in the way of a brighter energy future. If we hope to realize this future, we need to re-imagine the grid according to twenty-first-century values. It’s a project which forces visionaries to work with bureaucrats, legislators with storm-flattened communities, moneymen with hippies, and the left with the right. And though it might not yet be obvious, this revolution is already well under way.Cultural anthropologist Gretchen Bakke unveils the many facets of America's energy infrastructure, its most dynamic moments and its most stable ones, and its essential role in personal and national life. The grid, she argues, is an essentially American artifact, one which developed with us: a product of bold expansion, the occasional foolhardy vision, some genius technologies, and constant improvisation. Most of all, her focus is on how Americans are changing the grid right now, sometimes with gumption and big dreams and sometimes with legislation or the brandishing of guns.The Grid tells--entertainingly, perceptively--the story of what has been called “the largest machine in the world”: its fascinating history, its problematic present, and its potential role in a brighter, cleaner future.

    Charles Darwin played a crucial part in turning heredity into a scientific question, and yet he failed spectacularly to answer it. The birth of genetics in the early 1900s seemed to do precisely that. Gradually, people translated their old notions about heredity into a language of genes. As the technology for studying genes became cheaper, millions of people ordered genetic tests to link themselves to missing parents, to distant ancestors, to ethnic identities. . . .But, Zimmer writes, "Each of us carries an amalgam of fragments of DNA, stitched together from some of our many ancestors. Each piece has its own ancestry, traveling a different path back through human history. A particular fragment may sometimes be cause for worry, but most of our DNA influences who we are--our appearance, our height, our penchants--in inconceivably subtle ways." Heredity isn't just about genes that pass from parent to child. Heredity continues within our own bodies, as a single cell gives rise to trillions of cells that make up our bodies. We say we inherit genes from our ancestors--using a word that once referred to kingdoms and estates--but we inherit other things that matter as much or more to our lives, from microbes to technologies we use to make life more comfortable. We need a new definition of what heredity is and, through Carl Zimmer's lucid exposition and storytelling, this resounding tour de force delivers it. Weaving historical and current scientific research, his own experience with his two daughters, and the kind of original reporting expected of one of the world's best science journalists, Zimmer ultimately unpacks urgent bioethical quandaries arising from new biomedical technologies, but also long-standing presumptions about who we really are and what we can pass on to future generations.

    Turing Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be "computable," creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing's own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.

    Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to fool rather than to inform.

    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

    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.

    After the horrors of the Nazi regime in WWII, the mainstream scientific world turned its back on eugenics and the study of racial difference. But a worldwide network of eugenicists founded journals and funded research, providing the kind of shoddy studies that were ultimately cited in Richard Hernstein's and Charles Murray's 1994 title, The Bell Curve, which purported to show differences in intelligence among races.Whether you think of racist science as bad science, evil science, alt-right science, or pseudoscience, why would any contemporary scientist imagine that gross inequality is a fact of nature, rather than of political history? Angela Saini's Superior connects the dots, laying bare the history, continuity, and connections of modern racist science, some more subtle than you might think.