AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions.Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein's monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.

    

    The enhancements to ActionScript's performance, feature set, ease of use, cleanliness, and sophistication are considerable. Essential ActionScript 3.0 focuses on the core language and object-oriented programming, along with the Flash Player API. Essential ActionScript has become the #1 resource for the Flash and ActionScript development community, and the reason is the author, Colin Moock. Many people even refer to it simply as "The Colin Moock book."And for good reason: No one is better at turning ActionScript inside out, learning its nuances and capabilities, and then explaining everything in such an accessible way. Colin Moock is not just a talented programmer and technologist; he's also a gifted teacher.Essential ActionScript 3.0 is a radically overhauled update to Essential ActionScript 2.0. True to its roots, the book once again focuses on the core language and object-oriented programming, but also adds a deep look at the centerpiece of Flash Player's new API: display programming. Enjoy hundreds of brand new pages covering exciting new language features, such as the DOM-based event architecture, E4X, and namespaces--all brimming with real-world sample code.The ActionScript 3.0 revolution is here, and Essential ActionScript 3.0's steady hand is waiting to guide you through it.Adobe Developer Library is a co-publishing partnership between O'Reilly Media and Adobe Systems, Inc. and is designed to produce the number one information resources for developers who use Adobe technologies. Created in 2006, the Adobe Developer Library is the official source for comprehensive learning solutions to help developers create expressive and interactive web applications that can reach virtually anyone on any platform. With top-notch books and innovative online resources covering the latest in rich Internet application development, the Adobe Developer Library offers expert training and in-depth resources, straight from the source.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Sold in over 85 countries and translated into more than 5 languages.---------------Want to get started on data science?Our promise: no math added.This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.Popular concepts covered include:- A/B Testing- Anomaly Detection- Association Rules- Clustering- Decision Trees and Random Forests- Regression Analysis- Social Network Analysis- Neural NetworksFeatures:- Intuitive explanations and visuals- Real-world applications to illustrate each algorithm- Point summaries at the end of each chapter- Reference sheets comparing the pros and cons of algorithms- Glossary list of commonly-used termsWith this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    Jager-Hyman also offers a startlingly frank appraisal of the college admission process and the important roles race and class continue to play in a student's efforts to attend the best school possible.

    This is official Amazon Web Services (AWS) documentation for Amazon Compute Cloud (Amazon EC2).This guide explains the infrastructure provided by the Amazon EC2 web service, and steps you through how to configure and manage your virtual servers using the AWS Management Console (an easy-to-use graphical interface), the Amazon EC2 API, or web tools and utilities.Amazon EC2 provides resizable computing capacity—literally, server instances in Amazon's data centers—that you use to build and host your software systems.

    '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.

    D.S. Malik is ideal for a one-semester course focused on data structures. Clearly written with the student in mind, this text focuses on Data Structures and includes advanced topics in C++ such as Linked Lists and the Standard Template Library (STL). This student-friendly text features abundant Programming Examples and extensive use of visual diagrams to reinforce difficult topics. Students will find Dr. Malik's use of complete programming code and clear display of syntax, explanation, and example easy to read and conducive to learning.

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.

    This happens automatically when following agood, simple set of guidelines.*They improve development speed, because the programmer doesn't need toalways make decisions starting from first principles.*They enhance teamwork by eliminating needless debates on inconsequentialissues and by making it easy for teammates to read and maintain each other'scode.The coding standards introduced by this book are a collection of guidelines forwriting high-quality C++ code.***They are the distilled conclusions of a rich collective experience of the C++community. Until now, this body of knowledge has been available only asfolklore or spread in bits and pieces throughout books.

    In cultivating a deep understanding and appreciation of Objective-C, its frameworks and ecosystem, one is able to create apps that delight and inspire users. Combining articles from NSHipster.com with new essays, this book is the essential guide for modern iOS and Mac OS X developers.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    The aim of the book is to enable students to write effective C programs.The book starts with an introduction to programming in general followed by a detailed introduction to C programming. It then delves into a complete analysis of various constructs of C such as decision control and looping statements, functions, arrays, strings, pointers, structure and union, file management, and preprocessor directives. It also provides a separate chapter on linked list detailing the various kinds of linked lists and how they are used to allocate memory dynamically.A highly detailed pedagogical approach is followed throughout the book, which includes plenty of examples, figures, programming tips, keywords, and end-chapter exercises which make this book an ideal resource for students to master and fine-tune the art of writing C programs.