Like so many good stories, it got its start from a time of great turmoil and ended in a dramatic fashion. What happened between 1920 and 1929 has passed beyond history and has become legend. The lessons of the 1920s are still relevant today. Many of the debates and issues of the era are still part of the national conversation. Economic policies, consumer behaviors and mass culture of the 1920s are reflected in our culture almost 100 years later. By understanding the past, we can better prepare for the future and this new captivating history book is all about giving you that knowledge. This book includes topics such as: World War One and the 1920s Fear of the Other Old Causes Finishing Business The Cost of Prohibition A New World African-Americans Politics and Policies How Did It All End? And much, much more! Scroll to the top and download the book now for instant access!

    According to the August Forrester Report, 56 percent of IT managers interviewed at Global 2,500 companies are already using some type of open source software in their infrastructure and another 6 percent will install it in the next two years. This revolutionary model for collaborative software development is being embraced and studied by many of the biggest players in the high-tech industry, from Sun Microsystems to IBM to Intel.The Cathedral & the Bazaar is a must for anyone who cares about the future of the computer industry or the dynamics of the information economy. Already, billions of dollars have been made and lost based on the ideas in this book. Its conclusions will be studied, debated, and implemented for years to come. According to Bob Young, "This is Eric Raymond's great contribution to the success of the open source revolution, to the adoption of Linux-based operating systems, and to the success of open source users and the companies that supply them."The interest in open source software development has grown enormously in the past year. This revised and expanded paperback edition includes new material on open source developments in 1999 and 2000. Raymond's clear and effective writing style accurately describing the benefits of open source software has been key to its success. With major vendors creating acceptance for open source within companies, independent vendors will become the open source story in 2001.

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    It’s already happening in impressive ways. But as we’ve witnessed over the past 20 years, new technology also inevitably raises complex questions and broad societal concerns.” – Brad Smith and Harry Shum on The Future Computed. “As we look to a future powered by a partnership between computers and humans, it’s important that we address these challenges head on. How do we ensure that AI is designed and used responsibly? How do we establish ethical principles to protect people? How should we govern its use? And how will AI impact employment and jobs?” – Brad Smith and Harry Shum on The Future Computed. As Artificial Intelligence shows up in every aspect of our lives, Microsoft's top minds provide a guide discussing how we should prepare for the future. Whether you're a government leader crafting new laws, an entrepreneur looking to incorporate AI into your business, or a parent contemplating the future of education, this book explains the trends driving the AI revolution, identifies the complex ethics and workforce issues we all need to think about and suggests a path forward. Read more: The Future Computed: Artificial Intelligence and its role in society provides Microsoft’s perspective on where AI technology is going and the new societal issues it is raising – ensuring AI is designed and used responsibly, establishing ethical principles to protect people, and how AI will impact employment and jobs. The principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability are critical to addressing the societal impacts of AI and building trust as AI becomes more and more a part of the products and services that people use at work and at home every day. A central theme in The Future Computed is that for AI to deliver on its potential drive widespread economic and social progress, the technology needs to be human-centered – combining the capabilities of computers with human capabilities to enable people to achieve more. But a human-centered approach can only be realized if researchers, policymakers, and leaders from government, business and civil society come together to develop a shared ethical framework for AI. This in turn will help foster responsible development of AI systems that will engender trust. Because in an increasingly AI-driven world the question is not what computers can do, it is what computers should do. The Future Computed also draws a few conclusions as we chart our path forward. First, the companies and countries that will fare best in the AI era will be those that embrace these changes rapidly and effectively. Second, while AI will help solve big societal problems, we must look to this future with a critical eye as there will be challenges as well as opportunities. Third, we need to act with a sense of shared responsibility because AI won’t be created by the tech sector alone. Finally, skilling-up for an AI-powered world involves more than science, technology, engineering and math. As computers behave more like humans, the social sciences and humanities will become grow in importance.

    Into this creative chaos came Google with its dazzling mission—“To organize the world’s information and make it universally accessible”—and its much-quoted motto, “Don’t be evil.” In this provocative book, Siva Vaidhyanathan examines the ways we have used and embraced Google—and the growing resistance to its expansion across the globe. He exposes the dark side of our Google fantasies, raising red flags about issues of intellectual property and the much-touted Google Book Search. He assesses Google’s global impact, particularly in China, and explains the insidious effect of Googlization on the way we think. Finally, Vaidhyanathan proposes the construction of an Internet ecosystem designed to benefit the whole world and keep one brilliant and powerful company from falling into the “evil” it pledged to avoid.

    Has social media robbed us of our privacy and fed us with false information? Are the decisions about our health, security and finances made by computer programs inexplicable and biased? Will these algorithms become so complex that we can no longer control them? Are robots going to take our jobs? Will better health care lead to an aging population which cannot be cared for? Can we provide housing for our ever-growing urban populations? And has our demand for energy driven the Earth's climate to the edge of catastrophe?            John Browne argues that we need not and must not put the brakes on technological advance. Civilization is founded on engineering innovation; all progress stems from the human urge to make things and to shape the world around us, resulting in greater freedom, health and wealth for all. Drawing on history, his own experiences and conversations with many of today's great innovators, he uncovers the basis for all progress and its consequences, both good and bad. He argues compellingly that the same spark that triggers each innovation can be used to counter its negative consequences. Make, Think, Imagine provides an eloquent blueprint for how we can keep moving towards a brighter future.

    When research suggests how to harness brain evolution, a hunt ensues for a missing link―one that allows to design humans with skills that prodigies of old would have envied.As germline engineering and biological enhancement have become routine, ancient doubts have emerged under new guises: Who are we? Is there a purpose to life? Why is there so much suffering? When faith and science fail to answer these questions, personal greed and national interest quickly fill the void. But gene selection is expensive, and many are excluded from its benefits. The stage is set for tribalism and social discontent on a scale without precedent, and those caught in the fray, whether by choice or by chance, must play roles not always to their liking in the struggle of all creatures against the arbitrariness of existence.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Laughlin transports us two centuries into the future, when we've ceased to use carbon from the ground--either because humans have banned carbon burning or because fuel has simply run out. Boldly, Laughlin predicts no earth-shattering transformations will have taken place. Six generations from now, there will still be soccer moms, shopping malls, and business trips. Firesides will still be snug and warm.How will we do it? Not by discovering a magic bullet to slay our energy problems, but through a slew of fascinating technologies, drawing on wind, water, and fire. Powering the Future is an objective yet optimistic tour through alternative fuel sources, set in a world where we've burned every last drop of petroleum and every last shovelful of coal.The Predictable: Fossil fuels will run out. The present flow of crude oil out of the ground equals in one day the average flow of the Mississippi River past New Orleans in thirteen minutes. If you add the energy equivalents of gas and coal, it's thirty-six minutes. At the present rate of consumption, we'll be out of fossil fuels in two centuries' time. We always choose the cheapest gas. From the nineteenth-century consolidation of the oil business to the California energy crisis of 2000-2001, the energy business has shown, time and again, how low prices dominate market share. Market forces--not green technology--will be the driver of energy innovation in the next 200 years. The laws of physics remain fixed. Energy will still be conserved, degrade entropically with use, and have to be disposed of as waste heat into outer space. How much energy a fuel can pack away in a given space is fixed by quantum mechanics--and if we want to keep flying jet planes, we will need carbon-based fuels. The Potential: Animal waste. If dried and burned, the world's agricultural manure would supply about one-third as much energy as all the coal we presently consume. Trash. The United States disposes of 88 million tons of carbon in its trash per year. While the incineration of waste trash is not enough to contribute meaningfully to the global demand for energy, it will constrain fuel prices by providing a cheap supply of carbon. Solar energy. The power used to light all the cities around the world is only one-millionth of the total power of sunlight pouring down on earth's daytime side. And the amount of hydropump storage required to store the world's daily electrical surge is equal to only eight times the volume of Lake Mead. PRAISE FOR ROBERT B. LAUGHLIN -Perhaps the most brilliant theoretical physicist since Richard Feynman---George Chapline, Lawrence Livermore National Laboratory -Powerful but controversial.--- Financial Times -[Laughlin's] company ... is inspirational.- --New Scientist

    With the unwitting help of its users, the generative Internet is on a path to a lockdown, ending its cycle of innovation—and facilitating unsettling new kinds of control.IPods, iPhones, Xboxes, and TiVos represent the first wave of Internet-centered products that can’t be easily modified by anyone except their vendors or selected partners. These “tethered appliances” have already been used in remarkable but little-known ways: car GPS systems have been reconfigured at the demand of law enforcement to eavesdrop on the occupants at all times, and digital video recorders have been ordered to self-destruct thanks to a lawsuit against the manufacturer thousands of miles away. New Web 2.0 platforms like Google mash-ups and Facebook are rightly touted—but their applications can be similarly monitored and eliminated from a central source. As tethered appliances and applications eclipse the PC, the very nature of the Internet—its “generativity,” or innovative character—is at risk.The Internet’s current trajectory is one of lost opportunity. Its salvation, Zittrain argues, lies in the hands of its millions of users. Drawing on generative technologies like Wikipedia that have so far survived their own successes, this book shows how to develop new technologies and social structures that allow users to work creatively and collaboratively, participate in solutions, and become true “netizens.”The book is available to download under a Creative Commons Attribution Non-Commercial Share-Alike 3.0 license: Download PDF. http://futureoftheinternet.org/download

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Si todos debemos movernos para sobrevivir, vale la pena preguntarse: ¿qué factores de nuestro entorno nos impulsan a movernos y cuáles, por el contrario, nos detienen? ¿Por qué algunas personas tienen la oportunidad de moverse hacia donde quieren y otras no? ¿Por qué ciertas sociedades evolucionan y otras no? Para responder a estas interrogantes, los autores del libro estudiaron los códigos culturales y el comportamiento Bio-Lógico de 71 países para desarrollar un índice de que permite medir la movilidad social dentro de estas sociedades.Andrés Roemer y Clotaire Rapaille señalan que las culturas más exitosas son aquellas que han sabido preservar los mejores aspectos de su tradición, al mismo tiempo que han estado dispuestas a innovar y buscar nuevos horizontes. Se trata de sociedades abiertas al cambio y sin temor al statu quo. Otra clave del éxito evolutivo de las sociedades es el equilibrio entre el aspecto biológico (determinado por cuatro factores: supervivencia, sexo, seguridad y superación) y el aspecto cultural. El reto, concluyen los autores, es aprender a armonizar nuestros instintos (nuestro cerebro reptiliano) con nuestras emociones (nuestro cerebro límbico) y nuestra lógica (el neocórtex).ENGLISH DESCRIPTION If we all know we must move to survive, shouldn’t we ask ourselves which factors in our environment propel us and which halt us? Why do certain societies evolve while others don’t? In this book, Andrés Roemer and Clotaire Rapaille point out that the most successful cultures are those that are not afraid of the status quo: they have learned to preserve the best qualities of their traditions while being open to innovation and to uncovering new horizons. Another key to the success of these societies is the equilibrium between biological and the cultural aspects. The challenge is to harmonize our instincts, our emotions, and our logic.

    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.

    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