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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.
Will a computer ever compose a symphony, write a prize-winning novel, or paint a masterpiece? And if so, would we be able to tell the difference? As humans, we have an extraordinary ability to create works of art that elevate, expand and transform what it means to be alive. Yet in many other areas, new developments in AI are shaking up the status quo, as we find out how many of the tasks humans engage in can be done equally well, if not better, by machines. But can machines be creative? Will they soon be able to learn from the art that moves us, and understand what distinguishes it from the mundane? In The Creativity Code, Marcus du Sautoy examines the nature of creativity, as well as providing an essential guide into how algorithms work, and the mathematical rules underpinning them. He asks how much of our emotional response to art is a product of our brains reacting to pattern and structure, and exactly what it is to be creative in mathematics, art, language and music. Marcus finds out how long it might be before machines come up with something creative, and whether they might jolt us into being more imaginative in turn. The result is a fascinating and very different exploration into both AI and the essence of what it means to be human.
James Lovelock, creator of the Gaia hypothesis and the greatest environmental thinker of our time, has produced an astounding new theory about future of life on Earth. He argues that the anthropocene - the age in which humans acquired planetary-scale technologies - is, after 300 years, coming to an end. A new age - the novacene - has already begun. New beings will emerge from existing artificial intelligence systems. They will think 10,000 times faster than we do and they will regard us as we now regard plants - as desperately slow acting and thinking creatures. But this will not be the cruel, violent machine takeover of the planet imagined by sci-fi writers and film-makers. These hyper-intelligent beings will be as dependent on the health of the planet as we are. They will need the planetary cooling system of Gaia to defend them from the increasing heat of the sun as much as we do. And Gaia depends on organic life. We will be partners in this project. It is crucial, Lovelock argues, that the intelligence of Earth survives and prospers. He does not think there are intelligent aliens, so we are the only beings capable of understanding the cosmos. Maybe, he speculates, the novacene could even be the beginning of a process that will finally lead to intelligence suffusing the entire cosmos. At the age 100, James Lovelock has produced the most important and compelling work of his life.
Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it's possible to teach a machine to excel at human endeavors-such as drawing, composing music, and completing tasks-by generating an understanding of how its actions affect its environment. With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to apply the techniques to your own datasets. David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you'll learn how to make your models learn more efficiently and become more creative. Get a fundamental overview of deep learning Learn about libraries such as Keras and TensorFlow Discover how variational autoencoders work Get practical examples of generative adversarial networks (GANs) Understand how autoregressive generative models function Apply generative models within a reinforcement learning setting to accomplish tasks
Will a computer ever compose a symphony, write a prize-winning novel, or paint a masterpiece? And if so, would we be able to tell the difference?
As humans, we have an extraordinary ability to create works of art that elevate, expand and transform what it means to be alive.
Yet in many other areas, new developments in AI are shaking up the status quo, as we find out how many of the tasks humans engage in can be done equally well, if not better, by machines. But can machines be creative? Will they soon be able to learn from the art that moves us, and understand what distinguishes it from the mundane?
In The Creativity Code, Marcus du Sautoy examines the nature of creativity, as well as providing an essential guide into how algorithms work, and the mathematical rules underpinning them. He asks how much of our emotional response to art is a product of our brains reacting to pattern and structure, and exactly what it is to be creative in mathematics, art, language and music.
Marcus finds out how long it might be before machines come up with something creative, and whether they might jolt us into being more imaginative in turn. The result is a fascinating and very different exploration into both AI and the essence of what it means to be human.
Is Apple conscious?
Could a cyber–human system sense a potential terrorist attack?
Or make diagnosing a rare and little-known disease routine?
Computers are not replacing us: they are enhancing us. Different intelligences are joining together to do things we thought were impossible.
Whether it’s devising innovations to tackle climate change, helping job seekers and employers find one another, or identifying the outbreak of a serious disease, groups of humans and machines are already working together to solve all sorts of problems. And they will do a lot more.
The future will be like another world – a place where we’ll think differently. In many ways, we are already there.
The gripping inside story of the race to build conversationally capable computers Chat with the author: ask your Alexa device to `open the voice computing book' __________________ 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? __________________ `Brilliant and essential . . . You'll find insights and meaning on every page, and you'll keep turning them. This book is dynamite.' NICHOLAS THOMPSON, editor-in-chief of Wired `Conversational AI is a genuine paradigm shift in our experience with technology. Vlahos brings the whole story to life . . . A thoughtful and enjoyable read.' TOM GRUBER, co-creator of Siri `The baton of disruption has been passed from the smart phone to voice, and Vlahos helps make sense of this tectonic shift.' SCOTT GALLOWAY, author of The Four `Voice computing is revolutionising the way we interact with our devices. Talk to Me offers a road map showing how we got to this point and the opportunities and risks that lie ahead.' MARTIN FORD, author of The Rise of the Robots `James Vlahos has written an excellent book on how voice computing has become more and more of a growing presence in our everyday world.' RAY KURZWEIL, author of The Singularity Is Near
We live in a world increasingly ruled by technology; we seem as governed by technology as we do by laws and regulations. Frighteningly often, the influence of technology in and on our lives goes completely unchallenged by citizens and governments. We comfort ourselves with the soothing refrain that technology has no morals and can display no prejudice, and it's only the users of technology who distort certain aspects of it. But is this statement actually true? Dr Robert Smith thinks it is dangerously untrue in the modern era. Having worked in the field of artificial intelligence for over 30 years, Smith reveals the mounting evidence that the mechanical actors in our lives do indeed have, or at least express, morals: they're just not the morals of the progressive modern society that we imagined we were moving towards. Instead, as we are just beginning to see - in the US elections and Brexit to name but a few - there are increasing incidences of machine bigotry, greed and the crass manipulation of our basest instincts. It is easy to assume that these are the result of programmer prejudices or the product of dark forces manipulating the masses through the network of the Internet. But what if there is something more fundamental and explicitly mechanical at play, something inherent within technology itself? This book demonstrates how non-scientific ideas have been encoded deep into our technological infrastructure. Offering a rigorous, fresh perspective on how technology has brought us to this place, Rage Inside the Machine challenges the long-held assumption that technology is an apolitical and amoral force. Shedding light on little-known historical stories and investigating the complex connections between scientific philosophy, institutional prejudice and new technology, this book offers a new, honest and more truly scientific vision of ourselves.
How the history of technological revolutions can help us better understand economic and political polarization in the age of automation From the Industrial Revolution to the age of artificial intelligence, The Technology Trap takes a sweeping look at the history of technological progress and how it has radically shifted the distribution of economic and political power among society (TM)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 for large swaths of the population. Middle-income jobs withered, wages stagnated, the labor share of income fell, profits surged, and economic inequality skyrocketed. These trends, Frey documents, broadly mirror those in our current age of automation, which began with the Computer Revolution. Just as the Industrial Revolution eventually brought about extraordinary benefits for society, artificial intelligence systems have the potential to do the same. But Frey argues that this depends on how the short term is managed. In the nineteenth century, workers violently expressed their concerns over machines taking their jobs. The Luddite uprisings joined a long wave of machinery riots that swept across Europe and China. Today (TM)s despairing middle class has not resorted to physical force, but their frustration has led to rising populism and the increasing fragmentation of society. As middle-class jobs continue to come under pressure, there (TM)s no assurance that positive attitudes to technology will persist. The Industrial Revolution was a defining moment in history, but few grasped its enormous consequences at the time. 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.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird. Herbert Simon's classic and influential The Sciences of the Artificial declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. For this third edition, originally published in 1996, Simon added new material that takes into account advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. Simon won the Nobel Prize for Economics in 1978 for his research into the decision-making process within economic organizations and the Turing Award (considered by some the computer science equivalent to the Nobel) with Allen Newell in 1975 for contributions to artificial intelligence, the psychology of human cognition, and list processing. The Sciences of the Artificial distills the essence of Simon's thought accessibly and coherently. This reissue of the third edition makes a pioneering work available to a new audience.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: * Evaluation and Generalization in Interpretable Machine Learning * Explanation Methods in Deep Learning * Learning Functional Causal Models with Generative Neural Networks * Learning Interpreatable Rules for Multi-Label Classification * Structuring Neural Networks for More Explainable Predictions * Generating Post Hoc Rationales of Deep Visual Classification Decisions * Ensembling Visual Explanations * Explainable Deep Driving by Visualizing Causal Attention * Interdisciplinary Perspective on Algorithmic Job Candidate Search * Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions * Inherent Explainability Pattern Theory-based Video Event Interpretations
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.
Perspectives from philosophy, psychology religious studies, economics, and law on the possible future of robot-human sexual relationships. Sexbots are coming. Given the pace of technological advances, it is inevitable that realistic robots specifically designed for people's sexual gratification will be developed in the not-too-distant future. Despite popular culture's fascination with the topic, and the emergence of the much-publicized Campaign Against Sex Robots, there has been little academic research on the social, philosophical, moral, and legal implications of robot sex. This book fills the gap, offering perspectives from philosophy, psychology, religious studies, economics, and law on the possible future of robot-human sexual relationships. Contributors discuss what a sex robot is, if they exist, why we should take the issue seriously, and what it means to "have sex" with a robot. They make the case for developing sex robots, arguing for their beneficial nature, and the case against it, on religious and moral grounds; they consider the subject from the robot's perspective, addressing such issues as consent and agency; and they ask whether it is possible for a human to form a mutually satisfying, loving relationship with a robot. Finally, they speculate about the future of human-robot sexual interaction, considering the social acceptability of sex robots and the possible effect on society. Contributors Marina Adshade, Thomas Arnold, Julie Carpenter, John Danaher, Brian Earp, Lily Eva Frank, Joshua Goldstein, Michael Hauskeller, Noreen Herzfeld, Neil McArthur, Mark Migotti, Sven Nyholm, Ezio di Nucci, Steve Petersen, Anders Sandberg, Matthias Scheutz, Litska Strikwerda, Nicole Wyatt
A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data. This book helps technologists understand cognitive computing's underlying technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches based on accumulated evidence, rather than reprogramming. Detailed case examples from the financial, healthcare, and manufacturing walk readers step-by-step through the design and testing of cognitive systems, and expert perspectives from organizations such as Cleveland Clinic, Memorial Sloan-Kettering, as well as commercial vendors that are creating solutions. These organizations provide insight into the real-world implementation of cognitive computing systems. The IBM Watson cognitive computing platform is described in a detailed chapter because of its significance in helping to define this emerging market. In addition, the book includes implementations of emerging projects from Qualcomm, Hitachi, Google and Amazon. Today's cognitive computing solutions build on established concepts from artificial intelligence, natural language processing, ontologies, and leverage advances in big data management and analytics. They foreshadow an intelligent infrastructure that enables a new generation of customer and context-aware smart applications in all industries. Cognitive Computing is a comprehensive guide to the subject, providing both the theoretical and practical guidance technologists need. * Discover how cognitive computing evolved from promise to reality * Learn the elements that make up a cognitive computing system * Understand the groundbreaking hardware and software technologies behind cognitive computing * Learn to evaluate your own application portfolio to find the best candidates for pilot projects * Leverage cognitive computing capabilities to transform the organization Cognitive systems are rightly being hailed as the new era of computing. Learn how these technologies enable emerging firms to compete with entrenched giants, and forward-thinking established firms to disrupt their industries. Professionals who currently work with big data and analytics will see how cognitive computing builds on their foundation, and creates new opportunities. Cognitive Computing provides complete guidance to this new level of human-machine interaction.
This tutorial reference serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook for graduate students while simultaneously articulating important and technically challenging topics. Whereas most available books are graph theory-centric, this text introduces techniques arising from graph theory and expands to include other different models in its discussion on network science, regression, and algebraic topology. Links are included to the sample data and codes used in generating the book's results and figures, helping to empower methodological understanding in a manner immediately usable to both researchers and students.
Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze-remember when it seemed plausible that IBM's Watson could cure cancer?-to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the "moonshot" (curing cancer, or synthesizing all investment knowledge); look for the "low-hanging fruit" to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed-important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning ("analytics on steroids"), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review.
'Wonderful ... Illuminating ... Fun to read' Daniel Kahneman, author of Thinking, Fast and Slow A pioneer of artificial intelligence shows how the study of causality revolutionized science and the world 'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer and carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut through a century of confusion and placed cause and effect on a firm scientific basis. Now, Pearl and science journalist Dana Mackenzie explain causal thinking to general readers for the first time, showing how it allows us to explore the world that is and the worlds that could have been. It is the essence of human and artificial intelligence. And just as Pearl's discoveries have enabled machines to think better, The Book of Why explains how we can think better.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
How to educate the next generation of college students to invent, to create, and to discover-filling needs that even the most sophisticated robot cannot. Driverless cars are hitting the road, powered by artificial intelligence. Robots can climb stairs, open doors, win Jeopardy, analyze stocks, work in factories, find parking spaces, advise oncologists. In the past, automation was considered a threat to low-skilled labor. Now, many high-skilled functions, including interpreting medical images, doing legal research, and analyzing data, are within the skill sets of machines. How can higher education prepare students for their professional lives when professions themselves are disappearing? In Robot-Proof, Northeastern University president Joseph Aoun proposes a way to educate the next generation of college students to invent, to create, and to discover-to fill needs in society that even the most sophisticated artificial intelligence agent cannot. A "robot-proof" education, Aoun argues, is not concerned solely with topping up students' minds with high-octane facts. Rather, it calibrates them with a creative mindset and the mental elasticity to invent, discover, or create something valuable to society-a scientific proof, a hip-hop recording, a web comic, a cure for cancer. Aoun lays out the framework for a new discipline, humanics, which builds on our innate strengths and prepares students to compete in a labor market in which smart machines work alongside human professionals. The new literacies of Aoun's humanics are data literacy, technological literacy, and human literacy. Students will need data literacy to manage the flow of big data, and technological literacy to know how their machines work, but human literacy-the humanities, communication, and design-to function as a human being. Life-long learning opportunities will support their ability to adapt to change. The only certainty about the future is change. Higher education based on the new literacies of humanics can equip students for living and working through change.
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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