It also works beautifully with other open source tools, such as the MySQL database and the Apache web server. However, as more web sites are developed in PHP, they become targets for malicious attackers, and developers need to prepare for the attacks.Security is an issue that demands attention, given the growing frequency of attacks on web sites. Essential PHP Security explains the most common types of attacks and how to write code that isn't susceptible to them. By examining specific attacks and the techniques used to protect against them, you will have a deeper understanding and appreciation of the safeguards you are about to learn in this book.In the much-needed (and highly-requested) Essential PHP Security, each chapter covers an aspect of a web application (such as form processing, database programming, session management, and authentication). Chapters describe potential attacks with examples and then explain techniques to help you prevent those attacks.Topics covered include:Preventing cross-site scripting (XSS) vulnerabilitiesProtecting against SQL injection attacksComplicating session hijacking attemptsYou are in good hands with author Chris Shiflett, an internationally-recognized expert in the field of PHP security. Shiflett is also the founder and President of Brain Bulb, a PHP consultancy that offers a variety of services to clients around the world.

    They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

    Jill Lepore, best-selling author of These Truths, came across the company’s papers in MIT’s archives and set out to tell this forgotten history, the long-lost backstory to the methods, and the arrogance, of Silicon Valley.Founded in 1959 by some of the nation’s leading social scientists—“the best and the brightest, fatally brilliant, Icaruses with wings of feathers and wax, flying to the sun”—Simulmatics proposed to predict and manipulate the future by way of the computer simulation of human behavior. In summers, with their wives and children in tow, the company’s scientists met on the beach in Long Island under a geodesic, honeycombed dome, where they built a “People Machine” that aimed to model everything from buying a dishwasher to counterinsurgency to casting a vote. Deploying their “People Machine” from New York, Washington, Cambridge, and even Saigon, Simulmatics’ clients included the John F. Kennedy presidential campaign, the New York Times, the Department of Defense, and dozens of major manufacturers: Simulmatics had a hand in everything from political races to the Vietnam War to the Johnson administration’s ill-fated attempt to predict race riots. The company’s collapse was almost as rapid as its ascent, a collapse that involved failed marriages, a suspicious death, and bankruptcy. Exposed for false claims, and even accused of war crimes, it closed its doors in 1970 and all but vanished. Until Lepore came across the records of its remains.The scientists of Simulmatics believed they had invented “the A-bomb of the social sciences.” They did not predict that it would take decades to detonate, like a long-buried grenade. But, in the early years of the twenty-first century, that bomb did detonate, creating a world in which corporations collect data and model behavior and target messages about the most ordinary of decisions, leaving people all over the world, long before the global pandemic, crushed by feelings of helplessness. This history has a past; If Then is its cautionary tale.

    Soon, few of us will own our own automobiles and instead will get around in driverless electric vehicles that we summon with the touch of an app. We will be liberated from driving, prevent over 90% of car crashes, provide freedom of mobility to the elderly and disabled, and decrease our dependence on fossil fuels. Autonomy is the story of the maverick engineers and computer nerds who are creating the revolution. Longtime advisor to the Google Self-Driving Car team and former GM research and development chief Lawrence D. Burns provides the perfectly-timed history of how we arrived at this point, in a character-driven and heavily reported account of the unlikely thinkers who accomplished what billion-dollar automakers never dared.Beginning with the way 9/11 spurred the U.S. government to set a million-dollar prize for a series of off-road robot races in the Mojave Desert up to the early 2016 stampede to develop driverless technology, Autonomy is a page-turner that represents a chronicle of the past, diagnosis of the present, and prediction of the future—the ultimate guide to understanding the driverless car and navigating the revolution it sparks.

    Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

    This is Nick's story of his world record-breaking adventure and the extraordinary people who joined him along the way. On January 6th 2018, Nick Butter tied his laces and stepped out on to an icy pavement in Toronto, where he began to take the first steps of an epic journey that would see him run 196 marathons in every one of the world's 196 countries. Spending almost two years on the road and relying on the kindness of strangers to keep him moving, Nick's odyssey allowed him to travel slowly, on foot, immersing himself in the diverse cultures and customs of his host nations. Running through capital cities and deserts, around islands and through spectacular landscapes, Nick dodges bullets in Guinea-Bissau, crosses battlefields in Syria, survives a wild dog attack in Tunisia and runs around an erupting volcano in Guatemala. Along the way, he is often joined by local supporters and fellow runners, curious children and bemused passers-by. Telling their stories alongside his own, Nick captures the unique spirit of each place he visits and forges a new relationship with the world around him. Running the World captures Nick's journey as he sets three world records and covers over five thousand miles. As he recounts his adventures, he shares his unique perspective on our glorious planet, celebrates the diversity of human experience, and reflects on the overwhelming power of running.

    With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    

    Real rationality, of the sort studied by psychologists, social scientists, and mathematicians. The kind of rationality where you make good decisions, even when it's hard; where you reason well, even in the face of massive uncertainty; where you recognize and make full use of your fuzzy intuitions and emotions, rather than trying to discard them. In "Rationality: From AI to Zombies," Eliezer Yudkowsky explains the science underlying human irrationality with a mix of fables, argumentative essays, and personal vignettes. These eye-opening accounts of how the mind works (and how, all too often, it doesn't!) are then put to the test through some genuinely difficult puzzles: computer scientists' debates about the future of artificial intelligence (AI), physicists' debates about the relationship between the quantum and classical worlds, philosophers' debates about the metaphysics of zombies and the nature of morality, and many more. In the process, "Rationality: From AI to Zombies" delves into the human significance of correct reasoning more deeply than you'll find in any conventional textbook on cognitive science or philosophy of mind. A decision theorist and researcher at the Machine Intelligence Research Institute, Yudkowsky published earlier drafts of his writings to the websites Overcoming Bias and Less Wrong. "Rationality: From AI to Zombies" compiles six volumes of Yudkowsky's essays into a single electronic tome. Collectively, these sequences of linked essays serve as a rich and lively introduction to the science—and the art—of human rationality.

    What hope do we have against superintelligent machines? But we aren't really on the path to developing intelligent machines. In fact, we don't even know where that path might be.A tech entrepreneur and pioneering research scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence, and what it would take to get there. Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets: we make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world. We haven't a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense. That's why Alexa can't understand what you are asking, and why AI can only take us so far.Larson argues that AI hype is both bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we want to make real progress, we will need to start by more fully appreciating the only true intelligence we know--our own.

    You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic formats and a self-paced free online course where you can explore the course materials. All the supporting materials for the book are available under open and remixable licenses. This book is designed to teach people to program even if they have no prior experience.

    It’s not only blue-collar jobs at stake. Millions of educated knowledge workers—writers, paralegals, assistants, medical technicians—are threatened by accelerating advances in artificial intelligence. The industrial revolution shifted workers from farms to factories. In the first era of automation, machines relieved humans of manually exhausting work. Today, Era Two of automation continues to wash across the entire services-based economy that has replaced jobs in agriculture and manufacturing. Era Three, and the rise of AI, is dawning. Smart computers are demonstrating they are capable of making better decisions than humans. Brilliant technologies can now decide, learn, predict, and even comprehend much faster and more accurately than the human brain, and their progress is accelerating. Where will this leave lawyers, nurses, teachers, and editors?In Only Humans Need Apply, Thomas Hayes Davenport and Julia Kirby reframe the conversation about automation, arguing that the future of increased productivity and business success isn’t either human or machine. It’s both. The key is augmentation, utilizing technology to help humans work better, smarter, and faster. Instead of viewing these machines as competitive interlopers, we can see them as partners and collaborators in creative problem solving as we move into the next era. The choice is ours.

    In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us. In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it. Big Data is here to stay. Now is the time to find out how we can be empowered by it.

    This behavior is very much different from what we humans are used to dealing with in our everyday lives, so naturally this subject is quite hard to comprehend for many. We believed that the best way to introduce the subject reliably is to start at the beginning, presenting the observations, thoughts and conclusions of each of the world’s greatest physicists through their eyes, one at a time. In this way we hope that the reader may take an enjoyable journey through the strange truths of quantum theory and understand why the conclusions of these great minds are what they are. This book starts with the most general view of the world and gradually leads readers to those new, unbelievable but real facts about the very nature of our universe.