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Now in its third edition, Principles of Business Information Systems has been fully updated with new cases, new questions and assignments and the latest technologies, whilst also retaining its comprehensive coverage of Information Systems issues.
This new international edition also boasts a wealth of real world examples from a broad range of countries and updated coverage of IT and technological issues, making it perfect for courses that prepare students for the modern corporate world.
It will soon be impossible to tell what is real and what is fake.
Recent advances in AI mean that by scanning images of a person (for example using Facebook), a powerful machine learning system can create new video images and place them in scenarios and situations which never actually happened. When combined with powerful voice AI, the results are utterly convincing.
So-called 'Deep Fakes' are not only a real threat for democracy but they take the manipulation of voters to new levels. They will also affect ordinary people. This crisis of misinformation we are facing has been dubbed the 'Infocalypse'.
Using her expertise from working in the field, Nina Schick reveals shocking examples of Deep Fakery and explains the dangerous political consequences of the Infocalypse, both in terms of national security and what it means for public trust in politics. She also unveils what it means for us as individuals, how Deep Fakes will be used to intimidate and to silence, for revenge and fraud, and how unprepared governments and tech companies are.
As a political advisor to select technology firms, Schick tells us what we need to do to prepare and protect ourselves. Too often we build the cool technology and ignore what bad guys can do with it before we start playing catch-up. But when it comes to Deep Fakes, we urgently need to be on the front foot.
Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: * a general functional optimization framework; * thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; * comparison of classical and neural-network based methods of approximate solution; * bounds to the errors of approximate solutions; * solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; * applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and * numerous, numerically detailed examples. The authors' diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
'A fascinating hybrid. Part freewheeling history of the rise of the modern autonomous vehicle, part intimate memoir from an insider who was on the front lines for much of that history, Autonomy will more than bring readers up to speed on one of today's most closely watched technologies' Brian Merchant, author of The One Device From the ultimate insider - a former General Motors executive and current advisor to the Google Self-Driving Car project - comes the definitive story of the race between Google, Tesla and Uber to create the driverless car. We stand on the brink of a technological revolution. In the near future, most of us will not own automobiles, but will travel instead in driverless electric vehicles summoned at the touch of an app. We will be liberated from driving, so that the time we spend in cars can be put to more productive use. We will prevent more than 90 percent of car crashes, provide freedom of mobility to the elderly and disabled and decrease our dependence on fossil fuels. Autonomy tells the story of the maverick engineers and computer experts who triggered the revolution. Lawrence Burns - long-time adviser to the Google self-driving car project (now Waymo) and former corporate vice president of research, development and planning at General Motors - provides the perfectly timed history of how we arrived at this point, in a character-driven and vivid account of the unlikely thinkers who accomplished what billion-dollar automakers never dared. Beginning at a 2004 off-road robot race across the Mojave Desert with a million-dollar purse and continuing up to the current stampede to develop driverless technology, Autonomy is a page-turning chronicle of the past, a diagnosis of the present and a prediction of the future - the ultimate guide to understanding the driverless car and to navigating the revolution it has sparked.
'Vital reading. This is the book on artificial intelligence we need right now.' Mike Krieger, cofounder of Instagram Artificial intelligence is rapidly dominating every aspect of our modern lives influencing the news we consume, whether we get a mortgage, and even which friends wish us happy birthday. But as algorithms make ever more decisions on our behalf, how do we ensure they do what we want? And fairly? This conundrum - dubbed 'The Alignment Problem' by experts - is the subject of this timely and important book. From the AI program which cheats at computer games to the sexist algorithm behind Google Translate, bestselling author Brian Christian explains how, as AI develops, we rapidly approach a collision between artificial intelligence and ethics. If we stand by, we face a future with unregulated algorithms that propagate our biases - and worse - violate our most sacred values. Urgent and fascinating, this is an accessible primer to the most important issue facing AI researchers today.
'Rana el Kaliouby's vision for how technology should work in parallel with empathy is bold, inspired and hopeful' Arianna Huffington, founder and CEO of Thrive Global 'This lucid and captivating book by a renowned pioneer of emotion-AI tackles one of the most pressing issues of our time: How can we ensure a future where this technology empowers rather than surveils and manipulates us?' Max Tegmark, professor of physics at Massachusetts Institute of Technology and author of Life 3.0 We are entering an empathy crisis. Most of our communication is conveyed through non-verbal cues - facial expressions, tone of voice, body language - nuances that are completely lost when we interact through our smartphones and other technology. The result is a digital universe that's emotion-blind - a society lacking in empathy. Rana el Kaliouby discovered this when she left Cairo, a newly-married, Muslim woman, to take up her place at Cambridge University to study computer science. Many thousands of miles from home, she began to develop systems to help her better connect with her family. She started to pioneer the new field of Emotional Intelligence (EI). She now runs her company, Affectiva (the industry-leader in this emerging field) that builds EI into our technology and develops systems that understand humans the way we understand one another. In a captivating memoir, Girl Decoded chronicles el Kaliouby's mission to humanise technology and what she learns about humanity along the way.
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 Aurelien Geron 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 nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
The next great technological disruption is coming.
The titans of Silicon Valley are racing to build the last, best computer that the world will ever need. They know that whoever successfully creates it will revolutionise our relationship with technology – and make billions of dollars in the process. They call it conversational AI.
Computers that can speak and think like humans do may seem like the stuff of science fiction, but they are rapidly moving towards reality. In Talk to Me, veteran tech journalist James Vlahos meets the researchers at Google, Amazon and Apple who are leading the way to a voice computing revolution. He explores how voice tech will transform every sector of society: handing untold new powers to businesses, upending traditional notions of privacy, revolutionising access to information, and fundamentally altering the way we understand human consciousness. And he even tries to understand the significance of the revolution firsthand – by building a chatbot version of his terminally ill father.
Vlahos’s research leads him to one fundamental question: What happens when our computers become as articulate, compassionate, and creative as we are?
How the history of technological revolutions can help us better understand economic and political polarization in the age of automation The Technology Trap is a sweeping account of the history of technological progress and how it has radically shifted the distribution of economic and political power among society's members. As Carl Benedikt Frey shows, the Industrial Revolution created unprecedented wealth and prosperity over the long run, but the immediate consequences of mechanization were devastating. Middle-income jobs withered, wages stagnated, the labor share of income fell, profits surged, and economic inequality skyrocketed. These trends broadly mirror those in our current age of automation. But, just as the Industrial Revolution eventually brought about extraordinary benefits for society, artificial intelligence systems have the potential to do the same. The Technology Trap demonstrates that in the midst of another technological revolution, the lessons of the past can help us to more effectively face the present.
'Absorbing, mind-enlarging, studded with insights ... This could have significant real-world results' Sunday Times Humanity's greatest feat is our incredible ability to learn. Even in their first year, infants acquire language, visual and social knowledge at a rate that surpasses the best supercomputers. But how, exactly, do our brains learn? In How We Learn, leading neuroscientist Stanislas Dehaene delves into the psychological, neuronal, synaptic and molecular mechanisms of learning. Drawing on case studies of children who learned despite huge difficulty and trauma, he explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood. We can all enhance our learning and memory at any age and 'learn to learn' by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. How We Learn finds the boundary of computer science, neurobiology, cognitive psychology and education to explain how learning really works and how to make the best use of the brain's learning algorithms - and even improve them - in our schools and universities as well as in everyday life.
Why couldn't Michael Jordan, master athlete that he was, hit a baseball? Why can't modern robotics come close to replicating the dexterity of a five-year-old? Why do good quarterbacks always seem to know where their receivers are? In this deeply researched book, Sports and Business reporter Zach Schonbrun explores what actually drives human movement and its spectacular potential. The groundbreaking work of two neuroscientists in Major League Baseball is only the beginning. Schonbrun traces the fascinating history of motor research and details how new investigations in the brain are helping explain the extraordinary skills of talented performers like Stephen Curry, Tom Brady, Serena Williams, and Lionel Messi; as well as musical virtuosos, dancers, rock climbers, race-car drivers, and more. Whether it is timing a 95-mph fastball or reaching for a coffee mug, movement requires extraordinary computation that many take for granted - until now. The Performance Cortex ushers in a new way of thinking about the athletic gifts we strain to see in our cavernous arenas. It's not about the million-dollar arm anymore. It's about the million-dollar brain.
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull resumes until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called "artificial intelligence." They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian's riveting account, we meet the alignment problem's "first-responders," and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they-and we-succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity's biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture-and finds a story by turns harrowing and hopeful.
Human and Machine Hearing is the first book to comprehensively describe how human hearing works and how to build machines to analyze sounds in the same way that people do. Drawing on over thirty-five years of experience in analyzing hearing and building systems, Richard F. Lyon explains how we can now build machines with close-to-human abilities in speech, music, and other sound-understanding domains. He explains human hearing in terms of engineering concepts, and describes how to incorporate those concepts into machines for a wide range of modern applications. The details of this approach are presented at an accessible level, to bring a diverse range of readers, from neuroscience to engineering, to a common technical understanding. The description of hearing as signal-processing algorithms is supported by corresponding open-source code, for which the book serves as motivating documentation.
Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data or anything in between this guide makes it easier to understand and implement machine learning seamlessly. * Grasp how day-to-day activities are powered by machine learning * Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis * Learn to code in R using R Studio * Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
Knowledge representation is a key area of modern AI, underlying the development of semantic networks. Description logics are languages that represent knowledge in a structured and formally well-understood way: they are the cornerstone of the Semantic Web. This is the first textbook describing this importan new topic and will be suitable for courses aimed at advanced undergraduate and beginning graduate students, or for self-study. It assumes only a basic knowledge of computer science concepts. After generla introducitons motivating and overviewing the subject, the authors describe a simple DL and how it works and can be used, utilizing a running example that recurs through the book. Methods of reasoning and their implementation and complexity are examined, Finally, the authors provide a non-trivial DL knowledge base and use it to illsutrate featues that have been introduced: this base is available for free online access in a form usable by modern ontology editors.
The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
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.
In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power. Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee's opinion, probably not. But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.
Finite-state methods are the most efficient mechanisms for analysing textual and symbolic data, providing elegant solutions for an immense number of practical problems in computational linguistics and computer science. This book for graduate students and researchers gives a complete coverage of the field, starting from a conceptual introduction and building to advanced topics and applications. The central finite-state technologies are introduced with mathematical rigour, ranging from simple finite-state automata to transducers and bimachines as 'input-output' devices. Special attention is given to the rich possibilities of simplifying, transforming and combining finite-state devices. All algorithms presented are accompanied by full correctness proofs and executable source code in a new programming language, C(M), which focuses on transparency of steps and simplicity of code. Thus, by enabling readers to obtain a deep formal understanding of the subject and to put finite-state methods to real use, this book closes the gap between theory and practice.
Get on board the next massive marketing revolution AI for Marketing and Product Innovation offers creatives and marketing professionals a non-tech guide to artificial intelligence (AI) and machine learning (ML)--twin technologies that stand poised to revolutionize the way we sell. The future is here, and we are in the thick of it; AI and ML are already in our lives every day, whether we know it or not. The technology continues to evolve and grow, but the capabilities that make these tools world-changing for marketers are already here--whether we use them or not. This book helps you lean into the curve and take advantage of AI's unparalleled and rapidly expanding power. More than a simple primer on the technology, this book goes beyond the "what" to show you the "how" How do we use AI and ML in ways that speak to the human spirit? How to we translate cold technological innovation into creative tools that forge deep human connections? Written by a team of experts at the intersection of neuroscience, technology, and marketing, this book shows you the ins and outs of these groundbreaking technological tools. Understand AI and ML technology in layman's terms Harness the twin technologies unparalleled power to transform marketing Learn which skills and resources you need to use AI and ML effectively Employ AI and ML in ways that resonate meaningfully with customers Learn practical examples of how to reinvest product innovation, brand building, targeted marketing and media measurement to connect with people and enhance ROI Discover the true impact of AI and ML from real-world examples, and learn the thinking, best practices, and metrics you need to capture this lightning and take the next massive leap in the evolution of customer connection. AI for Marketing and Product Innovation shows you everything you need to know to get on board.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. '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.
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Have some evil fun inside your head! This wickedly inventive guide offers 19 build-it-yourself projects featuring high-tech devices that can map, manipulate, and even improve the greatest computer on earth-the human brain. Every project inside Mind Performance Projects for the Evil Genius is perfectly safe and explores cutting-edge concepts, such as brain wave mapping, lucid dream control, and hypnosis. Using easy-to-find parts and tools, this do-it-yourself book offers a wide variety of brain-bending bio hacks you can accomplish on your own. You'll find detailed guidelines, parameters, schematics, code, and customization tips for each project in the book. The only limit is your imagination! Mind Performance Projects for the Evil Genius: Features step-by-step instructions, complete with helpful illustrations Allows you to customize each project for your purposes Discusses the underlying principles behind the projects Removes the frustration factor-all required parts are listed, along with sources Build these and other lid-flipping gadgets: Biofeedback device Reaction speedometer Body temperature monitor Heart rate monitor Lie detector White noise generator Waking reality tester Audio dream director Lucid dream mask Alpha meditation goggles Clairvoyance tester Visual hypnosis aid Color therapy device Synchro brain machine
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