It used to be that to diagnose an illness, interpret legal documents, analyze foreign policy, or write a newspaper article you needed a human being with specific skills—and maybe an advanced degree or two. These days, high-level tasks are increasingly being handled by algorithms that can do precise work not only with speed but also with nuance. These “bots” started with human programming and logic, but now their reach extends beyond what their creators ever expected. In this fascinating, frightening book, Christopher Steiner tells the story of how algorithms took over—and shows why the “bot revolution” is about to spill into every aspect of our lives, often silently, without our knowledge. The May 2010 “Flash Crash” exposed Wall Street’s reliance on trading bots to the tune of a 998-point market drop and $1 trillion in vanished market value. But that was just the beginning. In Automate This, we meet bots that are driving cars, penning haiku, and writing music mistaken for Bach’s. They listen in on our customer service calls and figure out what Iran would do in the event of a nuclear standoff. There are algorithms that can pick out the most cohesive crew of astronauts for a space mission or identify the next Jeremy Lin. Some can even ingest statistics from baseball games and spit out pitch-perfect sports journalism indistinguishable from that produced by humans. The interaction of man and machine can make our lives easier. But what will the world look like when algorithms control our hospitals, our roads, our culture, and our national security? What hap­pens to businesses when we automate judgment and eliminate human instinct? And what role will be left for doctors, lawyers, writers, truck drivers, and many others?  Who knows—maybe there’s a bot learning to do your job this minute.

    We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant’s theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence.Offering a powerful and elegant model that encompasses life’s complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.

    On 3rd August 2012, Tia Sharp, a 12-year-old school girl, was reported missing from her grandmotherOCOs house in New Addington, south London. A call by her mother alerted the police to TiaOCOs disappearance and a massive search operation began. A nationwide appeal was launched to find Tia and her family, including her step-grandfather, 37-year-old Stuart Hazell, made a public appeal to find her. It was reported that Tia had disappeared after being dropped off at a train station to go shopping, but in the days that followed a very different story emerged. Only seven days after Tia was reported missing the terrible news came that her body had been found; wrapped in bin bags and hidden in her grandmotherOCOs attic. The truth that unfolded over the course of the day horrified the public; not only had the police searched the house on three separate occasions before discovering TiaOCOs body, late the following evening, Stuart HazellOCothe man who Tia trusted, the man who appealed for her returnOCoas change with murder. Nigel Cawthorne examines the appalling case of an evil step-grandfather who betrayed his familyOCOs trust, deceived friends and neighbors, and cut short the life of a young, well-loved girl."

    This bundle contains the first three novels of Kingdoms of Sand, a fantasy series from a USA Today bestselling author. That's over 1,000 pages of battles, intrigue, and magic.Sweeping from snowy forests to cruel deserts, from bazaars of wonder to fields of war, here is a tale of legionaries and lepers, priests and paupers, kings and crows. Here a girl travels across endless dunes, seeking magic; a cruel prince struggles to claim a bloodstained throne; and a young soldier fights to hold back an overwhelming host. The fate of all civilization stands upon a knife's edge, for under the storm of war, even the greatest nations are but kingdoms of sand.

    A terrifying Halloween treat perfect for Stephen King fans... BY THE LIGHT OF THE MOONWhen Dylan O'Conner pulls into a motel, all he wants is sleep. But he soon finds himself bound, gagged and being injected with a mysterious fluid by a lunatic doctor, who claims Dylan will be the carrier of his 'life's work'. He warns Dylan and fellow victim Jillian that he's being pursued and that they too are now targets. They're sceptical. But soon they realise he isn't so mad after all. . .ONE DOOR AWAY FROM HEAVEN Leilani Maddoc's tenth birthday is nine months away. Micky Bellsong is convinced that in nine months and one day, the girl will be dead. Micky's decision to save the child's life - and pit herself against an adversary as fearsome as he is cunning - takes her on a journey of incredible peril and stunning discoveries, a journey that will change her for ever...SEIZE THE NIGHTOne by one, the children of Moonlight Bay are disappearing. No one knows if they are dead or alive. Christopher Snow has glimpsed the dark and torrid secrets of the small-town community where he has spent his life. And only he has the key to the truth - a truth that could only exist in the genetic chaos of Moonlight Bay.

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    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.

    It is specially designed to provide you with a detailed illustration of the use of Fibonacci (one of the most commonly used indicators by successful traders) with a number of selected real charts. This book presents Fibonacci in close combination with other tools to help you make the best use of this indicator.Inside, what you will learn includes but not limited to: The deep reason behind the use of leading oscillators and how they can save you tons of money which lagging indicators can't. Step-by-step guidance on how to draw Fibonacci retracement and extension levels in the most accurate way (in combination with other tools). How to combine Fibonacci with price action to best predict market movements. How to determine the ideal time to enter and exit a trade based on Fibonacci (and other market signals). The importance of Fibonacci projection and how to use it in your trading. Secrets on using Fibonacci convergence in planning the size of the position, the place of stop loss and the whole action plan. How to take risks entering a trade which maximizes profits using advanced Fibonacci techniques. What is the 3-part rule and how to use it to protect your profits and let profits grow? How to set up Ichimoku chart and combine with Fibonacci levels to enter and exit trades. How to combine Fibonacci and Pivot Points techniques to gain the maximum profits from the market. How to execute the best money management strategy to beat Mr. Market. Also, this edition is full of real trade examples which disclose untold Fibonacci secrets.Would you like to discover more?Scroll up and click the "Buy now with 1-click" button.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt. You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century. You’ll explore: Data scientist skills—and why every company needs a Spock How the benefits of giving company-wide access to data outweigh the costs Why data-driven organizations use the scientific method to explore and solve data problems Key questions to help you develop a research-specific process for tackling important issues What to consider when assembling your data team Developing processes to keep your data team (and company) engaged Choosing technologies that are powerful, support teamwork, and easy to use and learn

    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

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    It tells the story of how Orbis Rex, Young Matt and his Circle of Friends, Queen Dido and the blind Dom'nic did battle with 'a wet and withered bat' from Wuhan.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.