These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of Web intelligence, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

    But many of those failures are preventable. The Lean Startup is a new approach being adopted across the globe, changing the way companies are built and new products are launched. Eric Ries defines a startup as an organization dedicated to creating something new under conditions of extreme uncertainty. This is just as true for one person in a garage or a group of seasoned professionals in a Fortune 500 boardroom. What they have in common is a mission to penetrate that fog of uncertainty to discover a successful path to a sustainable business.The Lean Startup approach fosters companies that are both more capital efficient and that leverage human creativity more effectively. Inspired by lessons from lean manufacturing, it relies on "validated learning," rapid scientific experimentation, as well as a number of counter-intuitive practices that shorten product development cycles, measure actual progress without resorting to vanity metrics, and learn what customers really want. It enables a company to shift directions with agility, altering plans inch by inch, minute by minute.Rather than wasting time creating elaborate business plans, The Lean Startup offers entrepreneurs - in companies of all sizes - a way to test their vision continuously, to adapt and adjust before it's too late. Ries provides a scientific approach to creating and managing successful startups in a age when companies need to innovate more than ever.

    It is one of the fastest growing companies in history, an essential part of the social life not only of teenagers but hundreds of millions of adults worldwide. As Facebook spreads around the globe, it creates surprising effects—even becoming instrumental in political protests from Colombia to Iran. Veteran technology reporter David Kirkpatrick had the full cooperation of Facebook’s key executives in researching this fascinating history of the company and its impact on our lives. Kirkpatrick tells us how Facebook was created, why it has flourished, and where it is going next. He chronicles its successes and missteps, and gives readers the most complete assessment anywhere of founder and CEO Mark Zuckerberg, the central figure in the company’s remarkable ascent. This is the Facebook story that can be found nowhere else. How did a nineteen-year-old Harvard student create a company that has transformed the Internet and how did he grow it to its current enormous size? Kirkpatrick shows how Zuckerberg steadfastly refused to compromise his vision, insistently focusing on growth over profits and preaching that Facebook must dominate (his word) communication on the Internet. In the process, he and a small group of key executives have created a company that has changed social life in the United States and elsewhere, a company that has become a ubiquitous presence in marketing, altering politics, business, and even our sense of our own identity. This is the Facebook Effect.

    Just about everyone thinks they know how they got there. Just about everyone is wrong. For all that's been written about the Four over the last two decades, no one has captured their power and staggering success as insightfully as Scott Galloway.Instead of buying the myths these compa-nies broadcast, Galloway asks fundamental questions. How did the Four infiltrate our lives so completely that they're almost impossible to avoid (or boycott)? Why does the stock market forgive them for sins that would destroy other firms? And as they race to become the world's first trillion-dollar company, can anyone chal-lenge them?In the same irreverent style that has made him one of the world's most celebrated business professors, Galloway deconstructs the strategies of the Four that lurk beneath their shiny veneers. He shows how they manipulate the fundamental emotional needs that have driven us since our ancestors lived in caves, at a speed and scope others can't match. And he reveals how you can apply the lessons of their ascent to your own business or career.Whether you want to compete with them, do business with them, or simply live in the world they dominate, you need to understand the Four.

    Now, in one of the greatest undertakings of the twenty-first century, a savvy group of mathematicians and computer scientists is beginning to sift through this data to dissect us and map out our next steps. Their goal? To manipulate our behavior -- what we buy, how we vote -- without our even realizing it.In this tour de force of original reporting and analysis, journalist Stephen Baker provides us with a fascinating guide to the world we're all entering -- and to the people controlling that world. The Numerati have infiltrated every realm of human affairs, profiling us as workers, shoppers, patients, voters, potential terrorists -- and lovers. The implications are vast. Our privacy evaporates. Our bosses can monitor and measure our every move (then reward or punish us). Politicians can find the swing voters among us, by plunking us all into new political groupings with names like "Hearth Keepers" and "Crossing Guards." It can sound scary. But the Numerati can also work on our behalf, diagnosing an illness before we're aware of the symptoms, or even helping us find our soul mate. Surprising, enlightening, and deeply relevant, The Numerati shows how a powerful new endeavor -- the mathematical modeling of humanity -- will transform every aspect of our lives. STEPHEN BAKER has written for BusinessWeek for over twenty years, covering Mexico and Latin America, the Rust Belt, European technology, and a host of other topics, including blogs, math, and nanotechnology. But he's always considered himself a foreign correspondent. This, he says, was especially useful as he met the Numerati. "While I came from the world of words, they inhabited the symbolic realms of math and computer science. This was foreign to me. My reporting became an anthropological mission." Baker has written for many publications, including the Wall Street Journal, the Los Angeles Times, and the Boston Globe. He won an Overseas Press Club Award for his portrait of the rising Mexican auto industry. He is the coauthor of blogspotting.net, featured by the New York Times as one of fifty blogs to watch.

    The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful--possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    It has sold 30,000 copies in each edition and will continue to reach a huge academic audience.In this renowned book, Everett M. Rogers, professor and chair of the Department of Communication & Journalism at the University of New Mexico, explains how new ideas spread via communication channels over time. Such innovations are initially perceived as uncertain and even risky. To overcome this uncertainty, most people seek out others like themselves who have already adopted the new idea. Thus the diffusion process consists of a few individuals who first adopt an innovation, then spread the word among their circle of acquaintances--a process which typically takes months or years. But there are exceptions: use of the Internet in the 1990s, for example, may have spread more rapidly than any other innovation in the history of humankind. Furthermore, the Internet is changing the very nature of diffusion by decreasing the importance of physical distance between people. The fifth edition addresses the spread of the Internet, and how it has transformed the way human beings communicate and adopt new ideas.

    It's a "Brotopia," where men hold all the cards and make all the rules. Vastly outnumbered, women face toxic workplaces rife with discrimination and sexual harassment, where investors take meetings in hot tubs and network at sex parties.In this powerful exposé, Bloomberg TV journalist Emily Chang reveals how Silicon Valley got so sexist despite its utopian ideals, why bro culture endures despite decades of companies claiming the moral high ground (Don't Be Evil! Connect the World!)--and how women are finally starting to speak out and fight back.Drawing on her deep network of Silicon Valley insiders, Chang opens the boardroom doors of male-dominated venture capital firms like Kleiner Perkins, the subject of Ellen Pao's high-profile gender discrimination lawsuit, and Sequoia, where a partner once famously said they "won't lower their standards" just to hire women. Interviews with Facebook COO Sheryl Sandberg, YouTube CEO Susan Wojcicki, and former Yahoo! CEO Marissa Mayer--who got their start at Google, where just one in five engineers is a woman--reveal just how hard it is to crack the Silicon Ceiling. And Chang shows how women such as former Uber engineer Susan Fowler, entrepreneur Niniane Wang, and game developer Brianna Wu, have risked their careers and sometimes their lives to pave a way for other women.Silicon Valley's aggressive, misogynistic, work-at-all costs culture has shut women out of the greatest wealth creation in the history of the world. It's time to break up the boys' club. Emily Chang shows us how to fix this toxic culture--to bring down Brotopia, once and for all.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Gilder says or writes is ever delivered at anything less than the fullest philosophical decibel... Mr. Gilder sounds less like a tech guru than a poet, and his words tumble out in a romantic cascade." “Google’s algorithms assume the world’s future is nothing more than the next moment in a random process. George Gilder shows how deep this assumption goes, what motivates people to make it, and why it’s wrong: the future depends on human action.” — Peter Thiel, founder of PayPal and Palantir Technologies and author of Zero to One: Notes on Startups, or How to Build the Future The Age of Google, built on big data and machine intelligence, has been an awesome era. But it’s coming to an end. In Life after Google, George Gilder—the peerless visionary of technology and culture—explains why Silicon Valley is suffering a nervous breakdown and what to expect as the post-Google age dawns. Google’s astonishing ability to “search and sort” attracts the entire world to its search engine and countless other goodies—videos, maps, email, calendars….And everything it offers is free, or so it seems. Instead of paying directly, users submit to advertising. The system of “aggregate and advertise” works—for a while—if you control an empire of data centers, but a market without prices strangles entrepreneurship and turns the Internet into a wasteland of ads. The crisis is not just economic. Even as advances in artificial intelligence induce delusions of omnipotence and transcendence, Silicon Valley has pretty much given up on security. The Internet firewalls supposedly protecting all those passwords and personal information have proved hopelessly permeable. The crisis cannot be solved within the current computer and network architecture. The future lies with the “cryptocosm”—the new architecture of the blockchain and its derivatives. Enabling cryptocurrencies such as bitcoin and ether, NEO and Hashgraph, it will provide the Internet a secure global payments system, ending the aggregate-and-advertise Age of Google. Silicon Valley, long dominated by a few giants, faces a “great unbundling,” which will disperse computer power and commerce and transform the economy and the Internet. Life after Google is almost here.   For fans of "Wealth and Poverty," "Knowledge and Power," and "The Scandal of Money."

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    Since 2001, newspaper and music revenues have fallen by 70%, book publishing, film and television profits have also fallen dramatically. Revenues at Google in this same period grew from $400 million to $74.5 billion. Google's YouTube today controls 60% of the streaming audio business and pays only 11% of the streaming audio revenues. More creative content is being consumed than ever before, but less revenue is flowing to creators and owners of the content.With the reallocation of money to monopoly platforms comes a shift in power. Google, Facebook, and Amazon now enjoy political power on par with Big Oil and Big Pharma, which in part explains how such a tremendous shift in revenues from artists to platforms could have been achieved and why it has gone unchallenged for so long.The stakes in this story go far beyond the livelihood of any one musician or journalist. As Taplin observes, the fact that more and more Americans receive their news, music and other forms of entertainment from a small group of companies poses a real threat to democracy. Move Fast and Break Things offers a vital, forward-thinking prescription for how artists can reclaim their audiences using knowledge of the past and a determination to work together. Using his own half-century career as a music and film producer and early pioneer of streaming video online, Taplin offers new ways to think about the design of the World Wide Web and specifically the way we live with the firms that dominate it.Table of contentsIntroduction1. The Great Disruption2. Levon's Story3. Tech's Counterculture Roots4. The Libertarian Counterinsurgency5. Digital Destruction6. Monopoly in the Digital Age7. Google's Regulatory Capture8. The Social Media Revolution9. Pirates of the Internet10. Libertarian and the 1 Percent11. What It Means to Be Human12. The Digital RenaissanceAfterword

    Mountains of print have been written about culture. Loonshots identifies the small shifts in structure that control this transition, the same way that temperature controls the change from water to ice.Using examples that range from the spread of fires in forests to the hunt for terrorists online, and stories of thieves and geniuses and kings, Bahcall shows how this new kind of science helps us understand the behavior of companies and the fate of empires. Loonshots distills these insights into lessons for creatives, entrepreneurs, and visionaries everywhere.