In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.
The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projectsOffers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
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 co-developed much of the statistical modeling software and environment in R/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, projection pursuit and gradient boosting.
“Artfully envisions a breathtakingly better world.” —Los Angeles Times
“Elaborate, smart and persuasive.” —The Boston Globe
“A pleasure to read.” —The Wall Street Journal
One of CBS News’s Best Fall Books of 2005 • Among St Louis Post-Dispatch’s Best Nonfiction Books of 2005 • One of Amazon.com’s Best Science Books of 2005
A radical and optimistic view of the future course of human development from the bestselling author of How to Create a Mind and The Age of Spiritual Machines who Bill Gates calls “the best person I know at predicting the future of artificial intelligence”
For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.
From the Trade Paperback edition.
A Huffington Post Definitive Tech Book of 2013
Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence.
In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.
Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications of vastly increasing the powers of our intelligence in addressing the world’s problems. He thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating.
Certain to be one of the most widely discussed and debated science books of the year, How to Create a Mind is sure to take its place alongside Kurzweil’s previous classics which include Fantastic Voyage: Live Long Enough to Live Forever and The Age of Spiritual Machines.
From the Hardcover edition.
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
From the Trade Paperback edition.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.comDemystifies data mining concepts with easy to understand languageShows how to get up and running fast with 20 commonly used powerful techniques for predictive analysisExplains the process of using open source RapidMiner toolsDiscusses a simple 5 step process for implementing algorithms that can be used for performing predictive analyticsIncludes practical use cases and examples
In the beginning was Josh Levine, an idealistic programming genius who dreamed of wresting control of the market from the big exchanges that, again and again, gave the giant institutions an advantage over the little guy. Levine created a computerized trading hub named Island where small traders swapped stocks, and over time his invention morphed into a global electronic stock market that sent trillions in capital through a vast jungle of fiber-optic cables.
By then, the market that Levine had sought to fix had turned upside down, birthing secretive exchanges called dark pools and a new species of trading machines that could think, and that seemed, ominously, to be slipping the control of their human masters.
Dark Pools is the fascinating story of how global markets have been hijacked by trading robots--many so self-directed that humans can't predict what they'll do next.
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 mediaMethods of clustering to detect groups of similar items in a large datasetSearch engine features -- crawlers, indexers, query engines, and the PageRank algorithmOptimization algorithms that search millions of possible solutions to a problem and choose the best oneBayesian filtering, used in spam filters for classifying documents based on word types and other featuresUsing decision trees not only to make predictions, but to model the way decisions are madePredicting numerical values rather than classifications to build price modelsSupport vector machines to match people in online dating sitesNon-negative matrix factorization to find the independent features in a datasetEvolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a gameEach 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
Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included.
Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs. He explains how to use algorithms for sorting, searching, numerical analysis, data compression, data encryption, common graph problems, and computational geometry. And he describes the relative efficiency of all implementations. The compression and encryption chapters not only give you working code for reasonably efficient solutions, they offer explanations of concepts in an approachable manner for people who never have had the time or expertise to study them in depth.
Anyone with a basic understanding of the C language can use this book. In order to provide maintainable and extendible code, an extra level of abstraction (such as pointers to functions) is used in examples where appropriate. Understanding that these techniques may be unfamiliar to some programmers, Loudon explains them clearly in the introductory chapters.
Contents include:PointersRecursionAnalysis of algorithmsData structures (lists, stacks, queues, sets, hash tables, trees, heaps, priority queues, graphs)Sorting and searchingNumerical methodsData compressionData encryptionGraph algorithmsGeometric algorithms
Semantic Web for the Working Ontologist transforms this information into the practical knowledge that programmers and subject domain experts need. Authors Allemang and Hendler begin with solutions to the basic problems, but don’t stop there: they demonstrate how to develop your own solutions to problems of increasing complexity and ensure that your skills will keep pace with the continued evolution of the Semantic Web.
• Provides practical information for all programmers and subject matter experts engaged in modeling data to fit the requirements of the Semantic Web.
• De-emphasizes algorithms and proofs, focusing instead on real-world problems, creative solutions, and highly illustrative examples.
• Presents detailed, ready-to-apply “recipes” for use in many specific situations.
• Shows how to create new recipes from RDF, RDFS, and OWL constructs.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
Samsung's announcement of the new ARTIK modules for IoT has generated tremendous interest in the developer market for wearable and other consumer or industrial devices. This book provides the perfect tutorial-based introduction to the ARTIK family of “Systems on Modules,” which integrate powerful microprocessors, memory, wireless connectivity, and enhanced security on to very small form factor boards.
With Beginning Samsung ARTIK as your guide, take the next steps to creating great solutions with an ARTIK.
What You'll Learn
Establish Wi-Fi connectivity with a wireless network
Upgrade the operating system and install additional software
Bring up Eclipse IDE and create a cross-compiler toolchain on Mac OS X
Cross-compile for the ARM processors in the ARTIK modules using Arduino IDE with libArduino to C
Use C to access the ARTIK hardware via a file based API
Use Node.js and Python inside the ARTIK moduleIntegrate applications with the Samsung SAMI data aggregation hub
Use Temboo to generate IoT software solutions that can be downloaded and compiled natively inside the ARTIK
Debug applications with software and hardware probesWho This Book Is For
Moderately experienced developers wanting to understand ARTIK and how to interact with it from within their own apps or web services.
Author Bob DuCharme has you writing simple queries right away before providing background on how SPARQL fits into RDF technologies. Using short examples that you can run yourself with open source software, you’ll learn how to update, add to, and delete data in RDF datasets.Get the big picture on RDF, linked data, and the semantic webUse SPARQL to find bad data and create new data from existing dataUse datatype metadata and functions in your queriesLearn techniques and tools to help your queries run more efficientlyUse RDF Schemas and OWL ontologies to extend the power of your queriesDiscover the roles that SPARQL can play in your applications
Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
This book is intended for Unity developers with a basic understanding of C# and the Unity editor. Whether you're looking to build your first game or are looking to expand your knowledge as a game programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation. It does not require any prior technical knowledge of how game AI works.What You Will LearnUnderstand the basic terminology and concepts in game AIImplement a basic finite state machine using state machine behaviors in Unity 5Create sensory systems for your AI with the most commonly used techniquesImplement an industry-standard path-finding system and a navigation mesh with the Unity 5 NavMesh featureBuild believable and highly-efficient artificial flocks and crowdsCreate a basic behavior tree to drive a character's actionsMake your characters more engaging by implementing fuzzy logic concepts in your AI's decision-makingTie all the concepts together with examples and guidesIn Detail
Unity 5 provides game and app developers with a variety of tools to implement artificial intelligence. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. Whether you are developing traditional, serious, educational, or any other kind of game, understanding how to apply artificial intelligence can take the fun-factor to the next level!
This book helps you break down artificial intelligence into simple concepts to give the reader a fundamental understanding of the topic to build upon. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts, and features related to game AI in Unity 5. Along the way, several tips and tricks are included to make the development of your own AI easier and more efficient.
Starting from covering the basic essential concepts to form a base for the later chapters in the book, you will learn to distinguish the state machine pattern along with implementing your own. This will be followed by learning how to implement a basic sensory system for your AI agent and coupling it with a finite state machine (FSM). Next you will be taught how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. Then you will learn how to implement simple flocks and crowd's dynamics, the key AI concepts. Then moving on you will learn how a behavior tree works and its implementation. Next you will learn adding layer of realism by combining fuzzy logic concepts with state machines. Lastly, you learn applying all the concepts in the book by combining them in a simple tank game.Style and approach
An easy-to-follow guide that is full of example implementations of the concepts and is accompanied by easy-to-understand demonstrations and explanations of the code and concepts.
The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methodsPerformance improvement techniques that work by transforming the input or output
The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling.Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challengesBuilds the reader's understanding with definitions, examples, theory, algorithms and complexity analysisSynthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics
Key features include:
Thorough treatment of the MSP430’s architecture and functionality along with detailed application-specific guidance Programming and the use of sensor technology to build an embedded system A learn-by-doing experience
With this book you will learn:
The basic theory for electronics design
- Analog circuits
- Digital logic
- Computer arithmetic
- Microcontroller programming
How to design and build a working robotAssembly language and C programming How to develop your own high-performance embedded systems application using an on-going robotics applicationTeaches how to develop your own high-performance embedded systems application using an on-going robotics applicationThorough treatment of the MSP430’s architecture and functionality along with detailed application-specific guidanceFocuses on electronics, programming and the use of sensor technology to build an embedded systemCovers assembly language and C programming
But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope.
Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype.
But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data.
Each chapter will cover a different technique in aspreadsheet so you can follow along:Mathematical optimization, including non-linear programming andgenetic algorithmsClustering via k-means, spherical k-means, and graphmodularityData mining in graphs, such as outlier detectionSupervised AI through logistic regression, ensemble models, andbag-of-words modelsForecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulationMoving from spreadsheets into the R programming language
You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
This year’s technical program presents both high quality and diversity, with contributions in well-established and evolving areas of research (Algeria, Brazil, China, Croatia, Czech Republic, Denmark, France, Germany, Ireland, Italy, Japan, Malaysia, Mexico, Poland, Portugal, Republic of Korea, Spain, Taiwan, Tunisia, Ukraine, United Kingdom), representing a truly “wide area network” of research activity. DCAI'14 Special Sessions have been a very useful tool in order to complement the regular program with new or emerging topics of particular interest to the participating community. Special Sessions that emphasize on multi-disciplinary and transversal aspects, such as AI-driven methods for Multimodal Networks and Processes Modeling and Multi-Agents Macroeconomics have been especially encouraged and welcome.
This symposium is organized by the Bioinformatics, Intelligent System and Educational Technology Research Group (http://bisite.usal.es/) of the University of Salamanca. The present edition was held in Salamanca, Spain, from 4th to 6th June 2014.
Chapters in Part A explain the significant influence of automation on our life, on individuals, organizations, and society, in economic terms and context, and impacts of precision, accuracy and reliability with automatic and automated equipment and operations. The theoretical and scientific knowledge about the human role in automation is covered in Part B from the human-oriented and human-centered aspects of automation to be applied and operated by humans, to the human role as supervisor and intelligent controller of automation systems and platforms. This part concludes with analysis and discussion on the limits of automation to the best of our current understanding. Covering automation design from theory to building automation machines, systems, and systems-of-systems , Part C explains the fundamental elements of mechatronics, sensors, robots, and other components useful for automation, and how they are combined with control and automation software, including models and techniques for automation software engineering, and the automation of the design process itself. Chapters in Part D cover the basic design requirements for the automation and illustrate examples of how the challenging issues can be solved for the deign and integration of automation with respect to its main purpose: Continuous and discrete processes and industries, design techniques, criteria and algorithms for flow lines, and integrated automation. Concluding this part is the design for safety of automation, and of automation for safety. The main aspects of automation management are covered by the chapters in Part E: Cost effectiveness and economic reasons for the design, feasibility analysis, implementation, rationalization, use, and maintenance of particular automation; performance and functionality measures and criteria. Related also are the issues of how to manage automatically and control maintenance, replacement, and upgrading. Part F, industrial automation, begins with explanation of machine tool automation, including various types of numerical control (NC), flexible, and precision machinery for production, manufacturing, and assembly, digital and virtual industrial production, to detailed design, guidelines and application of automation in the principal industries, from aerospace and automotive to semi-conductor, mining, food, paper and wood industries. Chapters are also devoted to the design, control and operation of functions common to all industrial automation. Infrastructures and service automation are covered in Part G and it is explained how automation is designed, selected, integrated, justified and applied, its challenges and emerging trends in those areas and in the construction of structures, roads and bridges; of smart buildings, smart roads and intelligent vehicles; cleaning of surfaces, tunnels and sewers; land, air, and space transportation; information, knowledge, learning, training, and library services; and in sports and entertainment. Automation in medical and healthcare systems is covered in Part H and shows the exponential penetration and main contributions of automation to the health and medical well being of individuals and societies. First, the scientific and theoretical foundations of control and automation in biological and biomedical systems and mechanisms are explained, then specific areas are described and analyzed. Available, proven, and emerging automation techniques in healthcare delivery and elimination of hospital and other medical errors are also addressed. Finally, Part I, Home, Office, and Enterprise Automation is about functional automation areas at home, in the office, and in general enterprises, including multi-enterprise networks. Chapters also cover the automation theories, techniques and practice, design, operation, challenges and emerging trends in education and learning, banking, commerce. An important dimension of the material compiled for this part is that it is useful for all other functional areas of automation. The concluding part of this Springer Handbook contains figures and tables with statistical information and summaries about automation applications and impacts in four main areas: industrial automation, service automation, healthcare automation, and financial and e-commerce automation. A rich list of associations and of periodical publications around the world that focus on automation in its variety of related fields is also included for the benefit of readers worldwide.
Throughout the 94 chapters, divided into ten main parts, with 124 tables, 1005 figures, the 168 co-authors present proven knowledge, original analysis, best practices and authoritative expertise.
Plenty of case studies, creative examples and unique illustrations, covering topics of automation from the basics and fundamentals to advanced techniques, cases and theories will serve the readers and benefit the students and researchers, engineers and managers, inventors, investors and developers.
This book is for programmers and artists who want to expand their knowledge of Game AI in relation to Unreal Engine 4. You are recommended to have some experience of exploring Unreal Engine 4 prior to this book because we jump straight into Game AI.What You Will LearnUnderstand the fundamental components of Game AI within Unreal Engine 4Skillfully introduce Game AI within Unreal Engine 4Configure, customize, and assign Navigation and AI components to your pawnCreate, debug, and analyze Game AI behaviorDesign responsive Game AI using the Behavior Tree methodologyCreate smart objects designed to interact with AIUtilize advanced AI features within your project to maximize the user experienceIn Detail
Unreal Engine is a powerful game development engine that provides rich functionalities to create 2D and 3D games. Developers have the opportunity to build cross-platform mobile and desktop games from scratch. This book will show you how to apply artificial intelligence (AI) techniques to your Unreal project using blueprints as your scripting language. You will start with an introduction to AI, and learn how it is applied to gaming. Then you'll jump right in and create a simple AI bot and apply basic behaviors to allow it to move randomly. As you progress, you'll find out how to implement randomness and probability traits. Using NavMesh, you will impart navigation components such as character movement, MoveTo nodes, settings, and world objects, and implement Behavior Trees. At the end of the book, you will troubleshoot any issues that might crop up while building the game.Style and approach
This easy-to-follow project-based guide throws you directly into the excitement of Game AI in an approachable and comprehensive manner.
The ever increasing speed of advances in technology and neuroscience, coupled with the creation of super computers and enhanced body parts and artificial limbs, is paving the way for a merger of both human and machine.
Devices which were once worn on the body are now being implanted into the body, and as a result, a class of true cyborgs, who are displaying a range of skills beyond those of normal humans-beings, are being created. There are cyborgs which can see colour by hearing sound, others have the ability to detect magnetic fields, some are equipped with telephoto lenses to aid their vision or implanted computers to monitor their heart, and some use thought to communicate with a computer or to manipulate a robotic arm. This is not science-fiction, these are developments that are really happening now, and will continue to develop in the future. However, a range of legal and policy questions has arisen alongside this rise of artificial intelligence.
Cyber-Humans provides a deep and unique perspective on the technological future of humanity, and describes how law and policy will be particularly relevant in creating a fair and equal society and protecting the liberties of different life forms which will emerge in the 21st century.
Dr Woodrow (Woody) Barfield previously headed up the Sensory Engineering Laboratory, holding the position of Industrial and Systems Engineering Professor at the University of Washington. His research revolves around the design and use of wearable computers and augmented reality systems and holds both JD and LLM degrees in intellectual property law and policy. He has published over 350 articles and major presentations in the areas of computer science, engineering and law. He currently lives in Chapel Hill, NC, USA.
- Compact modal logic reference
- Computational approaches fully discussed
- Contemporary applications of modal logic covered in depth