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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.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. '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? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. 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. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
Your guide to science, from the Big Bang to AI Whether you wish to discover the basics of science or catch up on its latest developments, this short accessible guide is for you. YOUNIVERSE describes in simple terms the world you are inseparably a part of: what it is, how it works and your place in it - insofar as these things are known. The text has been vetted by 13 distinguished scientists. Journey now through time and space, a world of the unimaginably big and the inconceivably small - though the marvels of science. *PRAISE FOR YOUNIVERSE* 'This is a fine piece of work... very entertaining and informative... It should appeal and be useful to the generalist who wants an overview of science.' Sir Peter Ratcliffe, 2019 Nobel Prize winner and head of clinical research at the Francis Crick Institute
'...an essential and fascinating manual for every woman who wants to understand equality within an ever-changing, modern world.' Scarlett Curtis '...[this book] taught me more than any book has ever taught me about AI.' Chris Evans, Virgin Radio How To Talk To Robots, is your girls guide to Artificial Intelligence. Entrepreneur Tabitha Goldstaub welcomes you into the AI world with a warm embrace. She brilliantly breaks down the tech-bro barriers offering a straightforward introduction and makes clear the enormous benefits of understanding AI.. If your social feed defines your spending habits or you've downloaded the latest filter to see what you'll look like when you are old or now connect with your doctor using an app, have applied for a job online or used your phone to arrive at work in record time, AI is playing a part in how you live, work and play. We live in an era where machines are taught to learn and act without human intervention and there are infinite possibilities to their applications. The risk of these technologies biasing against you is real, and this book will give you tools to navigate the current and future developments consciously. As well as explaining the risks Tabitha lays out the awesome benefits AI can offer. From spotting disease to tailoring education and tackling climate change the potential rewards are life-changing. Starting with a potted history, Tabitha shines a light on the many unsung heroines since the rise of AI in the 1960s. In conversation with Karen Hao she simply demonstrates how the technology works (and sometimes doesn't work!) and interviews a cross-section of women who use AI in their work today including Jeanette Winterson, Sharmadean Reid, Martha Lane Fox and Hannah Fry. This book doesn't just present the challenge; Tabitha offers supportive practical advice and shares an extensive list of books, films, courses and more for further exploration. However it is that you identify with womanhood, wherever you are in life, and whatever you do, this technology is inescapable and now is your time to make sure AI works for you - and not you for it!
Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging - including compressed sensing, wavelets and optimization - in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches.
A revelatory guide to how technology is changing the world, from the creator of Exponential View 'Essential' Reid Hoffman, co-founder of LinkedIn 'Powerful' Hannah Fry, author of Hello World 'Brilliant' Robert Peston, ITV Political Editor and author of WTF We are entering the Exponential Age. Between faster computers, better software and bigger data, ours is the first era in human history in which technology is constantly accelerating. Azeem Azhar - writer, technologist, and creator of the acclaimed Exponential View newsletter - understands this shift better than anyone. Technology, he argues, is developing at an increasing, exponential rate. But human society - from our businesses to our political institutions - can only ever adapt at a slower, incremental pace. The result is an 'exponential gap', between the power of new technology and our ability to keep up. In Exponential, Azhar shows how this exponential gap can explain our society's most pressing problems - from established businesses' difficulty keeping up with digital platforms, to the sclerotic response of liberal democracies to fast-moving social problems. And he draws on cutting-edge social science to explain how to stop the exponential gap eroding our economies, our politics and our lives. Exponential technology is upending our society. This book explains how. __ 'Comprehensive but lively . . . An essential addition to the ongoing discourse about where remarkable new technologies can take us, and where we should be aiming to go. Highly recommended!' Reid Hoffman, co-founder of LinkedIn and author of Blitzscaling 'Read this book if you are interested in how we can design a more inclusive and sustainable system with a re-direction of technological change at its centre.' Mariana Mazzucato, UCL professor and author of The Value of Everything and Mission Economy 'A powerful argument . . . Azeem Azhar's writing is informative and accessible, and his prescient ideas are only going to become more important as time goes on.' Hannah Fry, BBC Radio 4 presenter and author of Hello World 'Azeem Azhar is one of the best-regarded thought leaders in the industry . . . He has a broad understanding of the ways technology can be used to solve our biggest problems, shape our society, and bridge cultural divides.' Daniel Ek, co-founder and CEO of Spotify 'Azeem Azhar is a globally recognised voice on technology and its impact. He has written a fascinating and important book, required reading for anyone seeking to understand the new economy and the massive global corporations that seek to dominate that economy.' Matthew Taylor, Chief Executive, Royal Society of Arts
This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.
Technology is putting our humanity at risk to an unprecedented degree. This book is not for engineers who write the code or the policy makers who claim they can regulate it. This is a book for you. Because, believe it or not, you are the only one that can fix it. - Mo Gawdat Artificial intelligence is smarter than humans. It can process information at lightning speed and remain focused on specific tasks without distraction. AI can see into the future, predicting outcomes and even use sensors to see around physical and virtual corners. So why does AI frequently get it so wrong? The answer is us. Humans design the algorithms that define the way that AI works, and the processed information reflects an imperfect world. Does that mean we are doomed? In Scary Smart, Mo Gawdat, the internationally bestselling author of Solve for Happy, draws on his considerable expertise to answer this question and to show what we can all do now to teach ourselves and our machines how to live better. With more than thirty years' experience working at the cutting-edge of technology and his former role as chief business officer of Google [X], no one is better placed than Mo Gawdat to explain how the Artificial Intelligence of the future works. By 2049 AI will be a billion times more intelligent than humans. Scary Smart explains how to fix the current trajectory now, to make sure that the AI of the future can preserve our species. This book offers a blueprint, pointing the way to what we can do to safeguard ourselves, those we love and the planet itself.
'Wonderfully stimulating... will teach you to see around corners' -- Tim Harford 'A paean to cognitive agility and the elasticity of the imagination' -- The Economist 'A tightly written prescription for smart thinking' -- Financial Times The power of mental models to make better decisions We're always told that humans make bad decisions and that more data is better. But this is backwards: people are actually good at decisions because we use mental models and can envision new realities outside of data. Great outcomes don't depend so much on the final moment of choosing but on generating better alternatives to choose between. That's framing. It's a cognitive muscle we can strengthen to improve our lives, work and future -- to meet our moment of economic upheaval, social tensions and existential threats. Framers shows how.
'Briskly and breezily, 12 Bytes joins the dots in a neglected narrative of female scientists, visionaries and code-breakers' Observer Twelve eye-opening, mind-expanding and provocative essays from Sunday Times-bestselling author Jeanette Winterson Drawing on her years of thinking and reading about Artificial Intelligence in its bewildering manifestations, Jeanette Winterson looks to history, religion, myth, literature, politics and, of course, computer science, to help us understand the radical changes to the way we live and love that are happening now. With wit, compassion and curiosity, Winterson tackles AI's most interesting talking points, from the algorithms that data-dossier your whole life, to the weirdness of backing up your brain. 'Her writing engulfs you in lucid, fairytale-like realities that take you on gender-bending and time-warped explorations of religion, love, sex, and sexual identity.' Independent *A 'BOOKS OF 2021' PICK IN THE GUARDIAN, FINANCIAL TIMES AND EVENING STANDARD*
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
New and emerging technologies are reshaping justice systems and transforming the role of judges. The impacts vary according to how structural reforms take place and how courts adapt case management processes, online dispute resolution systems and justice apps. Significant shifts are also occurring with the development of more sophisticated forms of Artificial Intelligence that can support judicial work or even replace judges. These developments, together with shifts towards online court processes are explored in Judges, Technology and Artificial Intelligence. By considering how different jurisdictions are approaching current and future technological shifts and in particular by focusing on the different approaches in the US, UK, Australia and China and elsewhere, the author draws a rich comparative exploration of justice technology trends. Judicial commentary is considered as well as the growing scholarly discourse about these trends. Ethical and user centred design options are examined in the context of how responsive judges engage with supportive, replacement and disruptive technologies in courts. This book explores current issues regarding the responsiveness of the justice system in the pandemic era. In addition, how technology can respond and shift justice processes is a growing field of research, for judges, scholars, students and justice commentators. It provides a much-needed resource on an increasingly important topic.
The Future of Creative Work provides a unique overview of the changing nature of creative work, examining how digital developments and the rise of intangible capital are causing an upheaval in the social institutions of work. It offers a profound insight into how this technological and social evolution will affect creative professions. Expert international contributors explore how robotics, artificial intelligence, blockchain, global digital platforms and autonomous systems will shape the design, production and consumption of culture. Taking a multidisciplinary approach incorporating creative industries studies, business, education and economics, the book analyses the technological drivers of disruption in the world of creative work. Chapters reveal how these changes will create new axes of power and inequality in the global sphere of creative work, predicting that conventional creative professions will be challenged and different species of creative work will evolve as a result. By charting the impact of digital and technological developments, The Future of Creative Work challenges traditional views of creative work, careers and education. This book will be a valuable resource for students and researchers undertaking creative industries studies. Its discussion of the application of creative careers across the economy will also be beneficial for scholars and practitioners interested in business, economics, and advertising and marketing studies.
The sequel to THE LOOP: dark, twisty and completely unputdownable ... Praise for THE LOOP: 'A terrifying and sinister look into the future that will leave your jaw on the floor.' KASS MORGAN, New York Times bestselling author of THE 100 'If you like dystopia, sci-fi or adventure books, then you will inhale this novel.' ANNA DAY, author of THE FANDOM 'Fans of The Hunger Games and The Maze Runner should look no further ... Thrilling and terrifying in equal measure.' OBSERVER Luka is in prison again - but this time it's worse. He's in the Block, a place where reality and simulation start to blur. But an audacious breakout reunites Luka and his friends at last. Hiding out in the heart of the destroyed city, Luka realises the scale of their mission to defeat all-powerful AI, Happy. How can they stay hidden, let alone win the war? Old friends and new - including annoyingly cheerful companion drone, Apple-Moth - hold the key to their slim chance of victory ... The sequel to acclaimed debut THE LOOP: Prison Break meets 1984 in this cutting-edge sci-fi thriller series. Film/TV rights optioned by Lime Pictures and Black Mirror producer Louise Sutton. Book 3 coming in 2022!
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.
Peter, a brilliant scientist, is told that he will lose everything he
Autonomous systems are on the frontiers of Artificial Intelligence (AI) research, and they are slowly finding their business applications. Driven mostly by Reinforcement Learning (RL) methods (one of the most difficult, but also the most promising modern AI algorithms), autonomous systems help create self-learning and self-optimising systems, ranging from simple game-playing agents to robots able to efficiently act in completely new environments. Based on in-depth study of more than 100 projects, Andrzej Wodecki explores RL as a key component of modern digital technologies, its real-life applications to activities in a value chain and the ways in which it impacts different industries. Artificial Intelligence in Management will help project leaders, decision makers and investors evaluate new autonomous projects and will serve as an inspiring guide for future research.
Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Woodrow Barfield and Ugo Pagallo present a succinct introduction to the legal issues related to the design and use of artificial intelligence (AI). Exploring human rights, constitutional law, data protection, criminal law, tort law, and intellectual property law, they consider the laws of a number of jurisdictions including the US, the European Union, Japan, and China, making reference to case law and statutes. Key features include: a critical insight into human rights and constitutional law issues which may be affected by the use of AI discussion of the concept of legal personhood and how the law might respond as AI evolves in intelligence an introduction to current laws and statutes which apply to AI and an identification of the areas where future challenges to the law may arise. This Advanced Introduction is ideal for law and social science students with an interest in how the law applies to AI. It also provides a useful entry point for legal practitioners seeking an understanding of this emerging field.
'Du Sautoy's discussion of computer creativity is fascinating' Observer CAN MACHINES BE CREATIVE? In The Creativity Code, Marcus du Sautoy examines the nature of creativity, asking 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. Exploring how long it might be before machines compose a symphony or paint a masterpiece, and whether they might jolt us into being more imaginative in turn, The Creativity Code is a fascinating and very different exploration into the essence of what it means to be human.
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
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.
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