In the first-ever history about the organization, New York Times bestselling author Annie Jacobsen draws on inside sources, exclusive interviews, private documents, and declassified memos to paint a picture of DARPA, or "the Pentagon's brain," from its Cold War inception in 1958 to the present.This is the book on DARPA--a compelling narrative about this clandestine intersection of science and the American military and the often frightening results.

    Growing up in Egypt and Kuwait, el Kaliouby was raised by a strict father who valued tradition—yet also had high expectations for his daughters—and a mother who was one of the first female computer programmers in the Middle East. Even before el Kaliouby broke ground as a scientist, she broke the rules of what it meant to be an obedient daughter and, later, an obedient wife to pursue her own daring dream.After earning her PhD at Cambridge, el Kaliouby, now the divorced mother of two, moved to America to pursue her mission to humanize technology before it dehumanizes us. The majority of our communication is conveyed through nonverbal cues: facial expressions, tone of voice, body language. But that communication is lost when we interact with others through our smartphones and devices. The result is an emotion-blind digital universe that impairs the very intelligence and capabilities—including empathy—that distinguish human beings from our machines.To combat our fundamental loss of emotional intelligence online, she cofounded Affectiva, the pioneer in the new field of Emotion AI, allowing our technology to understand humans the way we understand one another. Girl Decoded chronicles el Kaliouby’s journey from being a “nice Egyptian girl” to becoming a woman, carving her own path as she revolutionizes technology. But decoding herself—learning to express and act on her own emotions—would prove to be the biggest challenge of all.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel).Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future. Editorial reviews: "In his newest book, Architects of Intelligence, Martin Ford provides us with an invaluable opportunity to learn from some of the most prominent thought leaders about the emerging fields of science that are shaping our future." -Al Gore, Former Vice President of the US "AI is going to shape our future, and Architects of Intelligence offers a unique and fascinating collection of perspectives from the top researchers and entrepreneurs who are driving progress in the field." - Eric Schmidt, former Chairman and CEO, Google "The best way to understand the challenges and consequences of AGI is to see inside the minds of industry experts shaping the field. Architects of Intelligence gives you that power." -Sam Altman, President of Y Combinator and co-chairman of OpenAI "Architects of Intelligence gets you inside the minds of the people building the technology that is going to transform our world. This is a book that everyone should read." -Reid Hoffman, Co-founder of LinkedIn

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    Drawing on new translations of Mutis's nearly forgotten writings, this fascinating story of scientific adventure in eighteenth-century South America retrieves Mutis's contributions from obscurity.In 1760, the 28-year-old Mutis—newly appointed as the personal physician of the Viceroy of the New Kingdom of Granada—embarked on a 48-year exploration of the natural world of northern South America. His thirst for knowledge led Mutis to study the region's flora, become a professor of mathematics, construct the first astronomical observatory in the Western Hemisphere, and amass one of the largest scientific libraries in the world. He translated Newton's writings and penned essays about Copernicus; lectured extensively on astronomy, geography, and meteorology; and eventually became a priest. But, as two-time Pulitzer Prize–winner Edward O. Wilson and Spanish natural history scholar José M. Gómez Durán reveal in this enjoyable and illustrative account, one of Mutis's most magnificent accomplishments involved ants.Acting at the urging of Carl Linnaeus—the father of taxonomy—shortly after he arrived in the New Kingdom of Granada, Mutis began studying the ants that swarmed everywhere. Though he lacked any entomological training, Mutis built his own classification for the species he found and named at a time when New World entomology was largely nonexistent. His unorthodox catalog of army ants, leafcutters, and other six-legged creatures found along the banks of the Magdalena provided a starting point for future study.Wilson and Durán weave a compelling, fast-paced story of ants on the march and the eighteenth-century scientist who followed them. A unique glance into the early world of science exploration, Kingdom of Ants is a delight to read and filled with intriguing information.

    Soon Gen Xers will be the only cohort of Americans who remember life as it was lived before the arrival of the Internet. They are, as Hennessey dubs them, “the last adult generation,” the sole remaining link to a time when childhood was still a bit dangerous but produced adults who were naturally resilient. More than a decade into the social media revolution, the American public is waking up to the idea that the tech sector’s intentions might not be as pure as advertised. The mountains of money being made off our browsing habits and purchase histories are used to fund ever-more extravagant and utopian projects that, by their very natures, will corrode the foundations of free society, leaving us all helpless and digitally enslaved to an elite crew of ultra-sophisticated tech geniuses. But it’s not too late to turn the tide. There’s still time for Gen X to write its own future. A spirited defense of free speech, eye contact, and the virtues of patience, Zero Hour for Gen X is a cultural history of the last 35 years, an analysis of the current social and historical moment, and a generational call to arms.

    He was eager to begin his first practice, but it turned out to be more than he bargained for. In just two years, in September, 1918, the entire village was sickened in rapid succession in the flu pandemic that killed quickly and indiscriminately throughout the world. It was wartime, and Nixon was unable to find help., This story is an up close and personal account of what it was like to be sick with the HINI type virus in 1918. It is a tale of a dedicated doctor whose selflessness, compassion and courage helped the villagers survive in the pandemic that killed more people in a year than the Black Death killed in a century...

    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.

     From Dr. Frankenstein to Dr. Jekyll, the image of the mad scientist surrounded by glass vials, copper coils, and electrical apparatus remains a popular fixture. In films and fiction, he's comically misguided, tragically misunderstood, or pathologically evil. But the origins of this stereotype can be found in the sometimes-eccentric real life men and women who challenged our view of the world and broke new scientific frontiers. They Called Me Mad recounts the amazing true stories of such historical luminaries as Archimedes, the calculator of pi and creator of the world's first death ray; Isaac Newton, the world's first great scientist and the last great alchemist; Nikola Tesla, who built the precursors of robots, fluorescent lighting, and particle beam weapons before the turn of the twentieth century-and more.

    Banks, telecommunications companies, and critical government networks (including the British Parliament and the French and German military) were infected. No one had ever seen anything like it. By January 2009 the worm lay hidden in at least eight million computers and the botnet of linked computers that it had created was big enough that an attack might crash the world. This is the gripping tale of the group of hackers, researches, millionaire Internet entrepreneurs, and computer security experts who united to defend the Internet from the Conficker worm: the story of the first digital world war.

    They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Consequently, organizations that understand these tools and know how to use them are benefiting at the expense of their rivals.Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies.This revised and fully updated edition contains several new sections and chapters, covering a broader set of topics than before, but retains the no-nonsense style of the original.Steven Finlay is a data scientist and author with more than 20 years’ experience of developing practical, business focused, analytical solutions. He holds a PhD in management science and is an honorary research fellow at Lancaster University in the UK.

    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.

    Famed for his dictatorial style, his political cunning and admired – initially – by Hitler, Mussolini led the National Fascist Party and ruled Italy as Prime Minister from 1922 until his ousting in 1943. In so doing, he paved the way towards Italy’s defeat in World War Two, and some of the 20th century’s most destructive ideologies and practices.Following expulsion from Italian Socialist Party, Mussolini denounced all efforts of class conflict, and instead later commanded a Fascist March on Rome to become the youngest Prime Minister in Italian history. Thereafter he set about dismantling the apparatus of democracy and initiated what would become known as the one-party totalitarian state. With World War II came defeat, humiliation and his bloody deposing. Explaining his ideologies, policies, actions and flaws, ‘Mussolini: History in an Hour’ is the concise life of the man whose ideas helped create some of the worst horrors of the modern history.Love history? Know your stuff with History in an Hour…

    To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.