“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.
"Lucid, comprehensive, and unafraid...;an indispensable contribution to a long-running argument."--Los Angeles Times
Winner of the 2015 FT & McKinsey Business Book of the Year Award
Top Business Book of 2015 at Forbes
One of NBCNews.com 12 Notable Science and Technology Books of 2015
What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence is already well on its way to making "good jobs" obsolete: many paralegals, journalists, office workers, and even computer programmers are poised to be replaced by robots and smart software. As progress continues, blue and white collar jobs alike will evaporate, squeezing working- and middle-class families ever further. At the same time, households are under assault from exploding costs, especially from the two major industries-education and health care-that, so far, have not been transformed by information technology. The result could well be massive unemployment and inequality as well as the implosion of the consumer economy itself.
The past solutions to technological disruption, especially more training and education, aren't going to work. We must decide, now, whether the future will see broad-based prosperity or catastrophic levels of inequality and economic insecurity. Rise of the Robots is essential reading to understand what accelerating technology means for our economic prospects-not to mention those of our children-as well as for society as a whole.
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.
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.
If you’ve ever thought to yourself “there has to be a better way to do this,” while using CATIA V5, then know you're probably right. There probably is a better way to complete your tasks you just don't know what it is and you don't have time to read a boring, expensive, thousand page manual on every single CATIA feature. If so, then CATIA V5 Tips and Tricks is for you. No fluff, just CATIA best practices and time savers you can put to use right away. From taming the specification tree to sketching, managing large assemblies and drawings, CATIA V5 Tips and Tricks will save you time and help you avoid common stumbling blocks.
One thing these technologies can't do is answer the profound moral issues they raise. Who should be held accountable when they go wrong? What responsibility do we, as creators and users, have for the technologies we build? In A Dangerous Master, ethicist Wendell Wallach tackles such difficult questions with hard-earned authority, imploring both producers and consumers to face the moral ambiguities arising from our rapid technological growth. There is no doubt that scientific research and innovation are a source of promise and productivity, but, as Wallach, argues, technological development is at risk of becoming a juggernaut beyond human control. Examining the players, institutions, and values lobbying against meaningful regulation of everything from autonomous robots to designer drugs, A Dangerous Master proposes solutions for regaining control of our technological destiny.
Wallach's nuanced study offers both stark warnings and hope, navigating both the fears and hype surrounding technological innovations. An engaging, masterful analysis of the elements we must manage in our quest to survive as a species, A Dangerous Master forces us to confront the practical—and moral—purposes of our creations.
Benefits for readers: Functional breakdown and explanation of the typical features, capabilities, and components of a SCADA system; IT and cybersecurity technology and terminology overview and explanation; Industry-specific as well as generalized discussion of SCADA vulnerabilities and available remediation strategies; and Discussion of physical and electronic security issues and strategies
Suitable for the non-technical management level personnel as well as IT personnel without SCADA experience SCADA technology quietly operates in the background of critical utility and industrial facilities nationwide. This important tool efficiently manages utility assets, refineries and other critical industrial segments, but protecting SCADA networks from cyber attacks, hackers and even physical assault is becoming a test of will, cleverness and determination. Cybersecurity for Industrial SCADA provides a high-level overview of this unique technology, with an explanation of each market segment. Readers will understand the vital issues, and learn strategies for decreasing or eliminating system vulnerabilities.
Benefits for readers: Functional breakdown and explanation of the typical features, capabilities, and components of a SCADA system; IT and cybersecurity technology and terminology overview and explanation; Industry-specific as well as generalized discussion of SCADA vulnerabilities and available remediation strategies; and Discussion of physical and electronic security issues and strategies
Suitable for the non-technical management level personnel as well as IT personnel without SCADA experience
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.
Provides Basic Transferable Skills for Managing Electrical Power on Ships or on Land
This groundbreaking book is the first volume of its kind to illustrate optimization of all aspects of shipboard electrical power systems. Applying author Mukund Patel’s rare combination of industrial and educational work experiences and insight, it offers solutions to meet the increasing demand for large, fast, efficient, and reconfigurable ships to compete in international markets.
For 30 years, Professor Patel was an engineer for companies including General Electric, Lockheed Martin, and Westinghouse Electric, and in the past 15 years he has been an engineering professor at the U.S. Merchant Marine Academy. That varied experience helped him zero in on the specialized multidimensional knowledge an engineer requires—and that is what sets his book apart.
Compiles Critical, Hard-to-Find Information on Power System Design, Analysis, and Operation
The global shortage of power engineers is not deterring countries from heavily investing in construction of new power plants and grids. Consequent growth in university electrical power programs is satisfying the demand for engineers, but novice graduates require accelerated understanding and practical experience before entering the thriving maritime segment.
Ideal for readers with limited electrical experience, wide-ranging coverage includes power system basics, power generation, electrical machines, power distribution, batteries, and marine industry standards. This book is an invaluable tool for engineers working on ships, as well as in ports, industrial power plants, refineries, and other similar environments.
The pervasiveness and range of capabilities of today’s mobile devices have enabled a wide spectrum of mobile applications that are transforming our daily lives, from smartphones equipped with GPS to integrated mobile sensors that acquire physiological data. Human Activity Recognition: Using Wearable Sensors and Smartphones focuses on the automatic identification of human activities from pervasive wearable sensors—a crucial component for health monitoring and also applicable to other areas, such as entertainment and tactical operations.
Developed from the authors’ nearly four years of rigorous research in the field, the book covers the theory, fundamentals, and applications of human activity recognition (HAR). The authors examine how machine learning and pattern recognition tools help determine a user’s activity during a certain period of time. They propose two systems for performing HAR: Centinela, an offline server-oriented HAR system, and Vigilante, a completely mobile real-time activity recognition system. The book also provides a practical guide to the development of activity recognition applications in the Android framework.
Now in its sixth edition, this book gives an introduction into the latest version of engineering software STEP 7 (basic version) . It describes elements and applications of text-oriented programming languages statement list (STL) and structured control language (SCL) for use with both SIMATIC S7-300 and SIMATIC S7-400, including the new applications with PROFINET and for communication over industrial Ethernet. It is aimed at all users of SIMATIC S7 controllers.
First-time users are introduced to the field of programmable controllers, while advanced users learn about specific applications of the SIMATIC S7 automation system.
All programming examples found in the book - and even a few extra examples - are available at the download area of the publisher's website.
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.
As the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems.
Learn from the Creators of the RapidMiner Software
Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com.
Understand Each Stage of the Data Mining Process
The book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining.
Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics
Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.
The book takes a holistic approach to thinking about the complex systems, providing a deeper understanding of the underlying nature of the system and the vocabulary of systems engineering. The authors give you working knowledge of the processes used to design, build, test, operate, and maintain the systems that we depend on every day. They break down the systems engineering life cycle, describing in the simplest terms what should be done along the development process.
Although there are many facets of systems engineering, it can be explained as focusing on addressing why a system is needed, what the system must do, and then how the system will accomplish the task over the entire life of the system—in that order. This fundamental review covers the processes from beginning to end, in plain language, giving you an overview of systems engineering that you can translate into your work in any field.
Power Electronics for Renewable and Distributed Energy Systems takes an integrative approach; discussing power electronic converters topologies, controls and integration that are specific to the renewable and distributed energy system applications.
An overview of power electronic technologies is followed by the introduction of various renewable and distributed energy resources that includes photovoltaics, wind, small hydroelectric, fuel cells, microturbines and variable speed generation. Energy storage systems such as battery and fast response storage systems are discussed along with application-specific examples. After setting forth the fundamentals, the chapters focus on more complex topics such as modular power electronics, microgrids and smart grids for integrating renewable and distributed energy. Emerging topics such as advanced electric vehicles and distributed control paradigm for power system control are discussed in the last two chapters.
With contributions from subject matter experts, the diagrams and detailed examples provided in each chapter make Power Electronics for Renewable and Distributed Energy Systems a sourcebook for electrical engineers and consultants working to deploy various renewable and distributed energy systems and can serve as a comprehensive guide for the upper-level undergraduates and graduate students across the globe.
This book treats physically-based as well as experimentally-refined engine models for gasoline and diesel engines and uses them to exemplify the design of various advanced control systems. The procedures, from measurements through simulation to calibration on test benches, are systematically described and demonstrated. The treatment spans not only the stationary but also the dynamic behavior of engines. Several new control regimens are detailed, such as multivariable feedforward and feedback control based on nonlinear net models, combustion pressure and HCCI control. Many new results with signal and process model-based fault diagnosis are used to show how on-board fault diagnosis can be considerably improved.
The book is directed at advanced students working in control, electrical, mechanical and mechatronic engineering and will also be useful for practicing engineers in the field of engine and automotive engineering.
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.
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.
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
“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.
The book covers systems and systems engineering, systems methods, models, and analytical techniques as well as systems management and control methods. It discusses systems concepts, emphasizing system life cycle, and includes coverage of systems design processes and the major activities involved. It offers hands-on exercises after each chapter, giving students a solid understanding of system requirements, and uses a software package (CORE) to introduce the requirement management process.
Designed for readers with a wide range of backgrounds, the book enables students to learn about systems and systems engineering, and, more specifically, to be able to use and apply the models and methods in the systems engineering field. The author has integrated feedback from students with materials used in teaching for many years, making the book especially approachable to non-engineering students with no prior exposure to this subject. Engineering students, on the other hand, will also benefit from the clear, concise coverage this book provides as well as the relevant analysis models and techniques.
Strengthening the analytic flavor of the book, this Second Edition:Features a new chapter on analytic combinatorics and new sections on advanced applications of generating functions Demonstrates powerful techniques that do not require the residue theorem or complex integration Adds new exercises to all chapters, significantly extending coverage of the given topics
Introduction to Enumerative and Analytic Combinatorics, Second Edition makes combinatorics more accessible, increasing interest in this rapidly expanding field.
Outstanding Academic Title of the Year, Choice magazine, American Library Association.
On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented.
For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.
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
As part of Totally Integrated Automation (TIA) Portal, the engineering software STEP 7 Basic offers a newly developed user interface, which is matched to intuitive operation. The functionality comprises all interests concerning automation: From configuring the controllers via programming in the IEC languages LAD (ladder diagram), FBD (function block diagram) and SCL (structured control language) up to program testing.
The book presents all of the hardware components of the automation system S7-1200, as well as its configuration and parameterization. A profound introduction into STEP 7 Basic V11 illustrates the basics of programming and trouble shooting. Beginners learn the basics of automation with SIMATIC S7-1200 and advanced users of S7-200 and S7-300 receive the knowledge required to work with the new PLC.
Users of STEP 7 Professional V12 will easily get along with the descriptions based on the V11. With start of V12, the screens of the technology functions might differ slightly from the V11.
“Smart, wide-ranging, [and] nontechnical.”
—Los Angeles Times
“Anyone who wants to understand what's coming must read this fascinating book.”
—Martin Ford, New York Times bestselling author of Rise of the Robots
In the year 2014, Google fired a shot heard all the way to Detroit. Google's newest driverless car had no steering wheel and no brakes. The message was clear: cars of the future will be born fully autonomous, with no human driver needed. In the coming decade, self-driving cars will hit the streets, rearranging established industries and reshaping cities, giving us new choices in where we live and how we work and play.
In this book, Hod Lipson and Melba Kurman offer readers insight into the risks and benefits of driverless cars and a lucid and engaging explanation of the enabling technology. Recent advances in software and robotics are toppling long-standing technological barriers that for decades have confined self-driving cars to the realm of fantasy. A new kind of artificial intelligence software called deep learning gives cars rapid and accurate visual perception. Human drivers can relax and take their eyes off the road.
When human drivers let intelligent software take the wheel, driverless cars will offer billions of people all over the world a safer, cleaner, and more convenient mode of transportation. Although the technology is nearly ready, car companies and policy makers may not be. The authors make a compelling case for why government, industry, and consumers need to work together to make the development of driverless cars our society's next “Apollo moment.”
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
The Handbook of Applied Cryptography provides a treatment that is multifunctional:
It serves as an introduction to the more practical aspects of both conventional and public-key cryptography
It is a valuable source of the latest techniques and algorithms for the serious practitioner
It provides an integrated treatment of the field, while still presenting each major topic as a self-contained unit
It provides a mathematical treatment to accompany practical discussions
It contains enough abstraction to be a valuable reference for theoreticians while containing enough detail to actually allow implementation of the algorithms discussed
Now in its third printing, this is the definitive cryptography reference that the novice as well as experienced developers, designers, researchers, engineers, computer scientists, and mathematicians alike will use.
The second edition of Model Predictive Control provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. Model Predictive Control demonstrates that a powerful technique does not always require complex control algorithms.
The text features material on the following subjects:
• general MPC elements and algorithms;
• commercial MPC schemes;
• generalized predictive control
• multivariable, robust, constrained nonlinear and hybrid MPC;
• fast methods for MPC implementation;
All of the material is thoroughly updated for the second edition with the chapters on nonlinear MPC, MPC and hybrid systems and MPC implementation being entirely new. Many new exercises and examples have also have also been added throughout and MATLAB® programs to aid in their solution can be downloaded from the authors' website. The text is an excellent aid for graduate and advanced undergraduate students and will also be of use to researchers and industrial practitioners wishing to keep abreast of a fast-moving field.
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.
Modelling and Control of Mini-Flying Machines is an exposition of models developed for various types of mini-aircraft:planar Vertical Take-off and Landing aircraft; helicopters; quadrotor mini-rotorcraft; other fixed-wing aircraft; blimps;
for each of which it propounds: detailed models derived from Euler-Lagrange methods; appropriate nonlinear control strategies and convergence properties; real-time experimental comparisons of the performance of control algorithms; review of the principal sensors, on-board electronics, real-time architecture and communications systems for mini-flying machine control, including discussion of their performance; detailed explanation of the use of the Kalman filter to flying machine localization.
To researchers and students in nonlinear control and its applications Modelling and Control of Mini-Flying Machines provides valuable insights to the application of real-time nonlinear techniques in an always challenging area.