Artificial intelligence (AI) Books

Book SynopsisTHE NEW YORK TIMES, USA TODAY, AND WALL STREET JOURNAL BESTSELLER "Kai-Fu Lee believes China will be the next tech-innovation superpower and in AI Superpowers: China, Silicon Valley, and the New World Order, he explains why.Trade ReviewA New York Times, Wall Street Journal, and USA Today Bestseller! Featured on CBS 60 Minutes Kai-Fu Lee named a Wired Icon, as part of Wired Magazine's 25th Anniversary Feature Publishers Weekly Fall 2018 Top 10 in Business & Economics Featured in the New York Times, the Wall Street Journal, the Washington Post, Wired, Financial Times, Bloomberg Businessweek, Business Insider, Forbes, and more. "After thirty years of pioneering work in artificial intelligence at Google China, Microsoft, Apple and other companies, Lee says he’s figured out the blueprint for humans to thrive in the coming decade of massive technological disruption: 'Let us choose to let machines be machines, and let humans be humans.'"—Forbes "Provocative."—Fortune "Kai-Fu Lee believes China will be the next tech-innovation superpower and in his new (and first) book, AI Superpowers: China, Silicon Valley, and the New World Order, he explains why. Taiwan-born Lee is perfectly positioned for the task."—New York Magazine "Both a provocative and readable distillation of the conventional wisdom on AI supremacy, as well as a challenge to it."—Financial Times "AI Superpowers: China, Silicon Valley, and the New World Order, by Kai-Fu Lee, about the ways that artificial intelligence is reshaping the world and the economic upheaval new technology will generate. We need to start thinking now about how to address these gigantic changes."—Senator Mark Warner, when asked about the best book he's read all year, Politico “Kai-Fu Lee's smart analysis on human-AI coexistence is clear-eyed and a must-read. We must look deep within ourselves for the values and wisdom to guide AI's development.” —Satya Nadella, CEO, Microsoft “In his brilliant book, Kai-Fu Lee applies his superpowers to predicting the disruptive shifts that will define the AI-powered future and proposes a revolutionary social contract that forges a new synergy between AI and the human heart.” —Marc Benioff, Chairman & CEO Salesforce “AI is surpassing human intelligence in more and more domains, transforming the planet. Kai-Fu Lee has been at the epicentre of the AI revolution for thirty years and has now written the definitive guide.” —Erik Brynjolfsson, professor, MIT, bestselling co-author of The Second Machine Age and Machine, Platform, Crowd “Kai-Fu Lee is at the forefront of the coming AI revolution, helping us transcend the limitations of thought, reach, and vision. This seminal book on AI is a must read for anyone serious about understanding the future of our species.” —Peter Diamandis, Executive Founder, Singularity University; bestselling author of Abundance and BOLD. “Truly one of the wisest and most surprising takes on AI. Kai-Fu Lee connects it with humans in a logical yet inspiring way. You’ll find this book illuminating and exciting in equal measure.” —Chris Anderson, Head of TED “In this riveting page-turner, one of the founding fathers of China’s AI industry tells the inside story of China’a rise as an AI superpower, and shares his inspiring recipe for us flourishing rather than floundering with AI.” —Prof. Max Tegmark, professor, MIT and bestselling author of Life 3.0: Being —

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Book SynopsisParmy Olson is a technology columnist with Bloomberg covering artificial intelligence, social media and tech regulation. She has written about the evolution of AI since 2016, when she covered Silicon Valley for Forbes magazine, before becoming a technology reporter for The Wall Street Journal.In 2024, she won the Financial Times and Schroders Business Book of the Year award for Supremacy. She is also the author of We Are Anonymous, an exposé of the eponymous hacker collective.

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Book SynopsisFor many decades, the proponents of `artificial intelligence'' have maintained that computers will soon be able to do everything that a human can do. In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating tour through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine.Oxford Landmark Science books are ''must-read'' classics of modern science writing which have crystallized big ideas, and shaped the way we think.Trade ReviewAn extraordinary masterpiece. * Adhemar Bultheel, European Mathmatical Society *Perhaps the most engaging and creative tour of modern physics that has ever been written * Sunday Times *A superb book... provocative and absorbing * Physics Today *A bold, brilliant, groundbreaking work... When Mr Penrose talks, scientists listen * New York Time Book Review *. . One cannot imagine a more revealing self-portrait than this enchanting, tantalising book... Roger Penrose reveals himself as an eloquent protagonist, not only of the wonders of mathematics, but also of the uniqueness of people. * Nature *I fail to see how anybody can remain unmoved by the book's central theme, which concerns the nature of human beings... His style is relaxed and entertaining, There are nuggets on almost every page. * Financial Times *Table of ContentsPrologue 1: Can a Computer Have a Mind? 2: Algorithms and Turing Machines 3: Mathematics and Reality 4: Truth, Proof, and Insight 5: The Classical World 6: Quantum Magic and Quantum Mystery 7: Cosmology and the Arrow of Time 8: In Search of Quantum Gravity 9: Real Brains and Model Brains 10: Where Lies the Physics of the Mind? Epilogue References Index

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Book SynopsisAI and Writing is an introduction to Generative Artificial Intelligence (GenAI) and its emergent role as a tool for academic, professional, civic, and personal writing. Sid Dobrin examines GenAI from two perspectives: the conceptual and the applied. The conceptual approach asks readers to consider the function of GenAI in their writing and to consider the ramifications of its use as a writing tool especially the ethical, social, and material issues it raises. The applied approach offers guidance to assist readers in using GenAI responsibly and authentically. In consideration of the rapid evolution of GenAI and the many unsettled questions about its utility, this book leaves room for readers to adapt to shifting technological and institutional contexts.This book is intended for composition and writing-intensive courses, and for any readers with a general or professional interest in the role of GenAI in writing. While it's primarily designed for first-year writing courses, it's also applicable to courses in advanced writing, professional writing, technical writing, business writing, and writing across the curriculum, as well as writing-intensive courses in other disciplines. In other words, it can be used in any course in which students are required to produce texts.Trade Review“AI and Writing is a practical, just-in-time guide for students—and teachers—on the uses and limitations of Generative AI in writing. Just as importantly, it will prompt critical reflection about AI's role in the future of writing and writing education. An invaluable read for anyone who wants to better understand GenAI and its impact on education.” — Naomi Mardock Uman, Metropolitan Community College“Sid Dobrin’s AI and Writing offers engaging insights that cut through contemporary anxiety surrounding generative AI in education. This book adeptly navigates both the speculative and the practical landscape of digital technologies, demystifying the complexities of generative AI and presenting actionable insights. Dobrin’s writing is framed by a philosophical and ethical lens that makes this book relevant, accessible, and essential reading for educators and learners alike.” — Clare Dyson, RMIT University Adobe Creative Campus“Sid Dobrin has spent his career pushing us to see what comes next for writing, and AI and Writing offers yet another exciting glimpse around the corner.… Dobrin’s book offers educators and students alike a vantage point from which to engage writing’s future.” — Jason Crider, Texas A&M University“Whether you are preparing students and colleagues for the new world of writing with AI or just trying to help them catch up to what has happened since the release of ChatGPT, Sid Dobrin offers an essential resource. The speed of change in AI and writing can be intimidating, but Dobrin deftly captures the enduring questions, illuminates a longer history than some might realize for these technologies, and sets an agenda for thoughtful engagement with AI rather than a reactionary response.” — William Hart-Davidson, Michigan State UniversityTable of ContentsI. Introduction: ChatGPT and the Generative Artificial Intelligence Surge Automated Writing: It's not Really New History of Writing Technologies and Cultural Panic II. What is Generative AI? (And for that matter, what is AI?) The Myths of Artificial Intelligence Artificial Intelligence Briefly Defined Generative Artificial Intelligence Briefly Defined How Does Generative Artificial Intelligence Work? What are our Assumptions and Expectations about GenAI? III. Generative AI and Academic Integrity (as well as professional, civic, and personal integrity) Generative AI and Integrity The Plagiarism Problem Spaces of Judgement IV. What is an Author? Generative AI and the Idea of Authorship Where is My Writing? Writing with Algorithms Does generative AI 'Write'? V. The Places of Generative AI Writing Academic Professional Civic Personal VI. Generative AI and Writing Processes Generative AI and Invention Generative AI and Drafting Generative AI and Revision VII. Generative AI and Creativity What is Art? What is original? Visual Rhetoric and GenAI Generative AI and Multimodal Writing VIII. Generative AI Best Practices

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Book SynopsisStuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California, Berkeley, where he is a Professor and former Chair of Computer Science, Director of the Centre for Human-Compatible AI, and holder of the SmithZadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in aTable of ContentsChapter I Artificial Intelligence Introduction What Is AI? The Foundations of Artificial Intelligence The History of Artificial Intelligence The State of the Art Risks and Benefits of AI SummaryBibliographical and Historical Notes Intelligent Agents Agents and Environments Good Behavior: The Concept of Rationality The Nature of Environments The Structure of Agents SummaryBibliographical and Historical Notes Chapter II Problem Solving Solving Problems by Searching Problem-Solving Agents Example Problems Search Algorithms Uninformed Search Strategies Informed (Heuristic) Search Strategies Heuristic Functions SummaryBibliographical and Historical Notes Search in Complex Environments Local Search and Optimization Problems Local Search in Continuous Spaces Search with Nondeterministic Actions Search in Partially Observable Environments Online Search Agents and Unknown Environments SummaryBibliographical and Historical Notes Constraint Satisfaction Problems Defining Constraint Satisfaction Problems Constraint Propagation: Inference in CSPs Backtracking Search for CSPs Local Search for CSPs The Structure of Problems SummaryBibliographical and Historical Notes Adversarial Search and Games Game Theory Optimal Decisions in Games Heuristic Alpha--Beta Tree Search Monte Carlo Tree Search Stochastic Games Partially Observable Games Limitations of Game Search Algorithms SummaryBibliographical and Historical Notes Chapter III Knowledge, Reasoning and Planning Logical Agents Knowledge-Based Agents The Wumpus World Logic Propositional Logic: A Very Simple Logic Propositional Theorem Proving Effective Propositional Model Checking Agents Based on Propositional Logic SummaryBibliographical and Historical Notes First-Order Logic Representation Revisited Syntax and Semantics of First-Order Logic Using First-Order Logic Knowledge Engineering in First-Order Logic SummaryBibliographical and Historical Notes Inference in First-Order Logic Propositional vs. First-Order Inference Unification and First-Order Inference Forward Chaining Backward Chaining Resolution SummaryBibliographical and Historical Notes Knowledge Representation Ontological Engineering Categories and Objects Events Mental Objects and Modal Logic for Categories Reasoning with Default Information SummaryBibliographical and Historical Notes Automated Planning Definition of Classical Planning Algorithms for Classical Planning Heuristics for Planning Hierarchical Planning Planning and Acting in Nondeterministic Domains Time, Schedules, and Resources Analysis of Planning Approaches SummaryBibliographical and Historical Notes Chapter IV Uncertain Knowledge and Reasoning Quantifying Uncertainty Acting under Uncertainty Basic Probability Notation Inference Using Full Joint Distributions Independence 12.5 Bayes' Rule and Its Use Naive Bayes Models The Wumpus World Revisited SummaryBibliographical and Historical Notes Probabilistic Reasoning Representing Knowledge in an Uncertain Domain The Semantics of Bayesian Networks Exact Inference in Bayesian Networks Approximate Inference for Bayesian Networks Causal Networks SummaryBibliographical and Historical Notes Probabilistic Reasoning over Time Time and Uncertainty Inference in Temporal Models Hidden Markov Models Kalman Filters Dynamic Bayesian Networks SummaryBibliographical and Historical Notes Making Simple Decisions Combining Beliefs and Desires under Uncertainty The Basis of Utility Theory Utility Functions Multiattribute Utility Functions Decision Networks The Value of Information Unknown Preferences SummaryBibliographical and Historical Notes Making Complex Decisions Sequential Decision Problems Algorithms for MDPs Bandit Problems Partially Observable MDPs Algorithms for Solving POMDPs SummaryBibliographical and Historical Notes Multiagent Decision Making Properties of Multiagent Environments Non-Cooperative Game Theory Cooperative Game Theory Making Collective Decisions SummaryBibliographical and Historical Notes Probabilistic Programming Relational Probability Models Open-Universe Probability Models Keeping Track of a Complex World Programs as Probability Models SummaryBibliographical and Historical Notes Chapter V Machine Learning Learning from Examples Forms of Leaming Supervised Learning . Learning Decision Trees . Model Selection and Optimization The Theory of Learning Linear Regression and Classification Nonparametric Models Ensemble Learning Developing Machine Learning Systen SummaryBibliographical and Historical Notes Knowledge in Learning A Logical Formulation of Learning Knowledge in Learning Exmplanation-Based Leaening Learning Using Relevance Information Inductive Logic Programming SummaryBibliographical and Historical Notes Learning Probabilistic Models Statistical Learning Learning with Complete Data Learning with Hidden Variables: The EM Algorithm SummaryBibliographical and Historical Notes Deep Learning Simple Feedforward Networks Computation Graphs for Deep Learning Convolutional Networks Learning Algorithms Generalization Recurrent Neural Networks Unsupervised Learning and Transfer Learning Applications SummaryBibliographical and Historical Notes Reinforcement Learning Learning from Rewards Passive Reinforcement Learning Active Reinforcement Learning Generalization in Reinforcement Learning Policy Search Apprenticeship and Inverse Reinforcement Leaming Applications of Reinforcement Learning SummaryBibliographical and Historical Notes Chapter VI Communicating, perceiving, and acting Natural Language Processing Language Models Grammar Parsing Augmented Grammars Complications of Real Natural Languagr Natural Language Tasks SummaryBibliographical and Historical Notes Deep Learning for Natural Language Processing Word Embeddings Recurrent Neural Networks for NLP Sequence-to-Sequence Models The Transformer Architecture Pretraining and Transfer Learning State of the art SummaryBibliographical and Historical Notes Robotics Robots Robot Hardware What kind of problem is robotics solving? Robotic Perception Planning and Control Planning Uncertain Movements Reinforcement Laming in Robotics Humans and Robots Alternative Robotic Frameworks Application Domains SummaryBibliographical and Historical Notes Computer Vision Introduction Image Formation Simple Image Features Classifying Images Detecting Objects The 3D World Using Computer Vision SummaryBibliographical and Historical Notes Chapter VII Conclusions Philosophy, Ethics, and Safety of Al The Limits of Al Can Machines Really Think? The Ethics of Al SummaryBibliographical and Historical Notes The Future of AI Al Components Al Architectures A Mathematical Background A.1 Complexity Analysis and O0 Notation A.2 Vectors, Matrices, and Linear Algebra A.3 Probability Distributions Bibliographical and Historical Notes B Notes on Languages and Algorithms B.1 Defining Languages with Backus-Naur Form (BNF) B.2 Describing Algorithms with Pseudocode B.3 Online Supplemental Material Bibliography Index

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Book SynopsisTHE WAY HUMANS NAVIGATE THE WORLD IS ALTERING, FOREVER. THIS IS YOUR ESSENTIAL AI ROADMAP. AI is revolutionizing how we approach security, economics, order and even knowledge itself. It is changing how we experience reality, and our role within it. Three of our most accomplished and deep thinkers explore what this means for our present and our future, tackling the questions that will affect as all: What will it mean to be human? What are the key frontier risks? What AI ethics are we going to need? How is AI impacting politics, defence, medicine and education? 'Absolutely masterful . . . the book we all need' Fareed Zakaria 'A muscular contribution to one of the 21st century's most pressing debates' The Economist Henry Kissinger was the 56th Secretary of State and winner of the Nobel Peace Prize; Eric Schmidt, Google's former CEO, lead the company's growth for over a decade and Daniel Huttenlocher is dean of the MIT Schwarzman College of Computing.Trade ReviewIt should be read by anyone trying to make sense of geopolitics todayIt should be read by anyone trying to make sense of geopolitics todayA muscular contribution to one of the 21st century's most pressing debatesA muscular contribution to one of the 21st century's most pressing debatesWhat it does - and does brilliantly - is illuminate the new problem we have created for ourselvesWhat it does - and does brilliantly - is illuminate the new problem we have created for ourselvesAbsolutely masterful . . . the book we all needAbsolutely masterful . . . the book we all need

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Book SynopsisDrones are transforming warfare through the use of artificial intelligence, drone swarms, and surveillance—leading to competition between the US, China, Israel, and Iran. Who will be the next drone superpower?In the battle for the streets of Mosul in Iraq, drones in the hands of ISIS terrorists made life hell for the Iraq army and civilians. Today, defense companies are racing to develop the lasers, microwave weapons, and technology necessary for confronting the next drone threat. Seth J. Frantzman takes the reader from the midnight exercises with Israel’s elite drone warriors, to the CIA headquarters where new drone technology was once adopted in the 1990s to hunt Osama bin Laden. This rapidly expanding technology could be used to target nuclear power plants and pose a threat to civilian airports. In the Middle East, the US used a drone to kill Iranian arch-terrorist Qasem Soleimani, a key Iranian commander. Drones are transforming the battlefield from Syria to Libya and Yemen. For militaries and security agencies—the main users of expensive drones—the UAV market is expanding as well; there were more than 20,000 military drones in use by 2020. Once the province of only a few militaries, drones now being built in Turkey, China, Russia, and smaller countries like Taiwan may be joining the military drone market. It’s big business, too—$100 billion will be spent over the next decade on drones. Militaries may soon be spending more on drones than tanks, much as navies transitioned away from giant vulnerable battleships to more agile ships. The future wars will be fought with drones and won by whoever has the most sophisticated technology.Trade Review"A riveting account of one of the most significant developments in contemporary warfare—the evolution and proliferation of drones. Seth Frantzman provides a compelling description of this development and of the challenges facing the US and other countries as they grapple with the rapidly emerging threats posed by the new technology. He also conveys a sobering analysis about how this technology is transforming warfare and a convincing case for better defenses against drones in the hands of terrorists, non-state actors, and near-peer adversaries. This is a very important book." -- General David Petraeus, US Army (Ret.), former Commander of the Surge in Iraq, US Central Command, and Coalition and US Forces in Afghanistan, and former Director of the CIA"A fast-paced account of the pioneers behind today's military drones, in Drone Wars, author Seth J. Frantzman sheds a light on the shadowy world of military drones and how these new technologies are changing the modern battlefield. The global proliferation of drones and their incorporation by militaries and terror groups creates an urgency for developing and fielding defenses against drones and keeping up with countries and groups that may pose an increasing threat." -- Richard Kemp, former Commander of British forces in Afghanistan and led the international terrorism team at Britain's Joint Intelligence Committee

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Book SynopsisA Sunday Times Business Book of the Year.Scary Smart will teach you how to navigate the scary and inevitable intrusion of Artificial Intelligence, with an accessible blueprint for creating a harmonious future alongside AI. From Mo Gawdat, the former Chief Business Officer at Google [X] and bestselling author of Solve for Happy.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 GawdatArtificial 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, predict outcomes and even use sensors to see around physical and virtual corners. So why does AI frequently get it so wrong and cause harm?The answer is us: the human beings who write the code and teach AI to mimic our behaviour. 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.'No one ever regrets reading anything Mo Gawdat has written.' – Emma Gannon, author of The Multi-Hyphen Method and host of the podcast Ctrl Alt DeleteTrade ReviewMo Gawdat is my life guru. His writing, his ideas and his generosity in sharing them has changed my life for the better in so many ways. Everything he writes is an enlightening education in how to be human. -- Elizabeth DayMo is an exquisite writer and speaker with deep expertise of technology . . . This book will teach you how to navigate the scary and inevitable intrusion of AI as well as who really is in control. Us. -- Dr Rupy Aujla, MBBS, BSc, MRCGP, Founder of "The Doctor's Kitchen"A proactive and bold read that provides the shake that humans need to take back our agency over AI, and therefore the fate of the world as we see it. -- Dr Camilla Pang, author of Explaining Humans: What Science Can Teach Us About Life, Love and RelationshipsA brilliant mind . . . Mo takes us on a whirlwind exploration of the fast-approaching singularity, and offers a desperate last chance to have a say in the future of humanity. Read this book! -- Tim Ash, bestselling author of Unleash Your Primal BrainNo one ever regrets reading anything Mo Gawdat has written. -- Emma Gannon, Sunday Times bestselling author of The Multi-Hyphen Method and host of award-winning podcast Ctrl Alt DeleteScary Smart is unlike anything I’ve ever read . . . What Mo does is help us analyze what it means to be human, by looking at what can or cannot happen with the rise of artificial intelligence. -- Poppy Jamie, author and founder of Happy Not Perfect

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Book Synopsis'Vital reading. This is the book on artificial intelligence we need right now.' Mike Krieger, cofounder of InstagramArtificial 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.Trade ReviewEssential reading. Christian brings much needed clarity to a subject that is often talked about but little understood. * Tim O'Reilly, founder and CEO of O'Reilly Media *Balanced and meticulously researched * New Statesman *Superb * Hannah Fry, New Yorker *Vital reading. This is the book on artificial intelligence we need right now. Brian Christian takes us on a technically fluent (yet widely accessible) journey through the most important questions facing AI and humanity. * Mike Krieger, cofounder of Instagram *An abundantly researched and captivating book that explores the road humanity has taken to create a successor for itself-a road that's rich with surprising discoveries, unexpected obstacles, ingenious solutions and, increasingly, hard questions about the soul of our species. * Jaan Tallinn, cofounder of Skype and the Future of Life Institute *A fascinating, provocative, and insightful tour of all the ways that AI goes wrong and all the ways people are trying to fix it. Essential reading if you want to understand where our world is heading. * Stuart Russell, author of Human Compatible *A new field has emerged that responds to and scrutinizes the vast technological shifts represented by our modern, virtual, algorithmically defined world. In The Alignment Problem, Brian Christian masterfully surveys the 'AI fairness' community, introducing us to some of its historical roots in science, philosophy, and activism; and crucially, many of its quandaries and limitations. * Cathy O'Neil, bestselling author of Weapons of Math Destruction *A riveting and deeply complex look at artificial intelligence and the significant challenge in creating computer models that 'capture our norms and values'... Lay readers will find Christian's revealing study to be a helpful guide to an urgent problem in tech. * Publishers Weekly *A deeply enjoyable and meticulously researched account of how computer scientists and philosophers are defining the biggest question of our time: how will we create intelligent machines which will improve our lives rather than complicate or even destroy them? There's no better book than The Alignment Problem at spelling out the issues of governing AI safely. * James Barrat, bestselling author of Our Final Invention *Brian Christian is a fine writer and has produced a fascinating book. AI seems destined to become, for good or ill increasingly prominent in our lives. We should be grateful for this balanced and hype-free perspective on its scope and limits. * Martin Rees, Emeritus Professor of Cosmology and Astrophysics, University of Cambridge *Table of Contents0: Introduction 1: REPRESENTATION 2: FAIRNESS 3: TRANSPARENCY 4: REINFORCEMENT 5: SHAPING 6: CURIOSITY 7: IMITATION 8: INFERENCE 9: UNCERTAINTY 10: Conclusion

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Book SynopsisThis book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.“Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton"With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." – Yann LeCun“This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua BengioTable of ContentsPreface 3 1 The Deep Learning Revolution 19 1.1 The Impact of Deep Learning . . . . . . . . . . . . . . . . . . . . 20 1.1.1 Medical diagnosis . . . . . . . . . . . . . . . . . . . . . . 20 1.1.2 Protein structure . . . . . . . . . . . . . . . . . . . . . . . 21 1.1.3 Image synthesis . . . . . . . . . . . . . . . . . . . . . . . . 22 1.1.4 Large language models . . . . . . . . . . . . . . . . . . . . 23 1.2 A Tutorial Example . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.2.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.2.2 Linear models . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.2.3 Error function . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.2.4 Model complexity . . . . . . . . . . . . . . . . . . . . . . 27 1.2.5 Regularization . . . . . . . . . . . . . . . . . . . . . . . . 30 1.2.6 Model selection . . . . . . . . . . . . . . . . . . . . . . . . 32 1.3 A Brief History of Machine Learning . . . . . . . . . . . . . . . . 34 1.3.1 Single-layer networks . . . . . . . . . . . . . . . . . . . . 35 1.3.2 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . 36 1.3.3 Deep networks . . . . . . . . . . . . . . . . . . . . . . . . 38 2 Probabilities 41 2.1 The Rules of Probability . . . . . . . . . . . . . . . . . . . . . . . 43 2.1.1 A medical screening example . . . . . . . . . . . . . . . . 43 2.1.2 The sum and product rules . . . . . . . . . . . . . . . . . . 44 2.1.3 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . 46 2.1.4 Medical screening revisited . . . . . . . . . . . . . . . . . 48 2.1.5 Prior and posterior probabilities . . . . . . . . . . . . . . . 49 2.1.6 Independent variables . . . . . . . . . . . . . . . . . . . . 49 2.2 Probability Densities . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.2.1 Example distributions . . . . . . . . . . . . . . . . . . . . 51 2.2.2 Expectations and covariances . . . . . . . . . . . . . . . . 52 2.3 The Gaussian Distribution . . . . . . . . . . . . . . . . . . . . . . 54 2.3.1 Mean and variance . . . . . . . . . . . . . . . . . . . . . . 55 2.3.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 55 2.3.3 Bias of maximum likelihood . . . . . . . . . . . . . . . . . 57 2.3.4 Linear regression . . . . . . . . . . . . . . . . . . . . . . . 58 2.4 Transformation of Densities . . . . . . . . . . . . . . . . . . . . . 60 2.4.1 Multivariate distributions . . . . . . . . . . . . . . . . . . . 62 2.5 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 64 2.5.1 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 2.5.2 Physics perspective . . . . . . . . . . . . . . . . . . . . . . 65 2.5.3 Differential entropy . . . . . . . . . . . . . . . . . . . . . . 67 2.5.4 Maximum entropy . . . . . . . . . . . . . . . . . . . . . . 68 2.5.5 Kullback–Leibler divergence . . . . . . . . . . . . . . . . . 69 2.5.6 Conditional entropy . . . . . . . . . . . . . . . . . . . . . 71 2.5.7 Mutual information . . . . . . . . . . . . . . . . . . . . . . 72 2.6 Bayesian Probabilities . . . . . . . . . . . . . . . . . . . . . . . . 72 2.6.1 Model parameters . . . . . . . . . . . . . . . . . . . . . . . 73 2.6.2 Regularization . . . . . . . . . . . . . . . . . . . . . . . . 74 2.6.3 Bayesian machine learning . . . . . . . . . . . . . . . . . . 75 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3 Standard Distributions 83 3.1 Discrete Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.1.1 Bernoulli distribution . . . . . . . . . . . . . . . . . . . . . 84 3.1.2 Binomial distribution . . . . . . . . . . . . . . . . . . . . . 85 3.1.3 Multinomial distribution . . . . . . . . . . . . . . . . . . . 86 3.2 The Multivariate Gaussian . . . . . . . . . . . . . . . . . . . . . . 88 3.2.1 Geometry of the Gaussian . . . . . . . . . . . . . . . . . . 89 3.2.2 Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 3.2.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.2.4 Conditional distribution . . . . . . . . . . . . . . . . . . . 94 3.2.5 Marginal distribution . . . . . . . . . . . . . . . . . . . . . 97 3.2.6 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . 99 3.2.7 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 102 3.2.8 Sequential estimation . . . . . . . . . . . . . . . . . . . . . 103 3.2.9 Mixtures of Gaussians . . . . . . . . . . . . . . . . . . . . 104 3.3 Periodic Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 107 3.3.1 Von Mises distribution . . . . . . . . . . . . . . . . . . . . 107 3.4 The Exponential Family . . . . . . . . . . . . . . . . . . . . . . . 112 3.4.1 Sufficient statistics . . . . . . . . . . . . . . . . . . . . . . 115 3.5 Nonparametric Methods . . . . . . . . . . . . . . . . . . . . . . . 116 3.5.1 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . 116 3.5.2 Kernel densities . . . . . . . . . . . . . . . . . . . . . . . . 118 3.5.3 Nearest-neighbours . . . . . . . . . . . . . . . . . . . . . . 121 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 4 Single-layer Networks: Regression 129 4.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 130 4.1.1 Basis functions . . . . . . . . . . . . . . . . . . . . . . . . 130 4.1.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 132 4.1.3 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 133 4.1.4 Geometry of least squares . . . . . . . . . . . . . . . . . . 135 4.1.5 Sequential learning . . . . . . . . . . . . . . . . . . . . . . 135 4.1.6 Regularized least squares . . . . . . . . . . . . . . . . . . . 136 4.1.7 Multiple outputs . . . . . . . . . . . . . . . . . . . . . . . 137 4.2 Decision theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 4.3 The Bias–Variance Trade-off . . . . . . . . . . . . . . . . . . . . . 141 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 5 Single-layer Networks: Classification 149 5.1 Discriminant Functions . . . . . . . . . . . . . . . . . . . . . . . . 150 5.1.1 Two classes . . . . . . . . . . . . . . . . . . . . . . . . . . 150 5.1.2 Multiple classes . . . . . . . . . . . . . . . . . . . . . . . . 152 5.1.3 1-of-K coding . . . . . . . . . . . . . . . . . . . . . . . . 153 5.1.4 Least squares for classification . . . . . . . . . . . . . . . . 154 5.2 Decision Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 5.2.1 Misclassification rate . . . . . . . . . . . . . . . . . . . . . 157 5.2.2 Expected loss . . . . . . . . . . . . . . . . . . . . . . . . . 158 5.2.3 The reject option . . . . . . . . . . . . . . . . . . . . . . . 160 5.2.4 Inference and decision . . . . . . . . . . . . . . . . . . . . 161 5.2.5 Classifier accuracy . . . . . . . . . . . . . . . . . . . . . . 165 5.2.6 ROC curve . . . . . . . . . . . . . . . . . . . . . . . . . . 166 5.3 Generative Classifiers . . . . . . . . . . . . . . . . . . . . . . . . 168 5.3.1 Continuous inputs . . . . . . . . . . . . . . . . . . . . . . 170 5.3.2 Maximum likelihood solution . . . . . . . . . . . . . . . . 171 5.3.3 Discrete features . . . . . . . . . . . . . . . . . . . . . . . 174 5.3.4 Exponential family . . . . . . . . . . . . . . . . . . . . . . 174 5.4 Discriminative Classifiers . . . . . . . . . . . . . . . . . . . . . . 175 5.4.1 Activation functions . . . . . . . . . . . . . . . . . . . . . 176 5.4.2 Fixed basis functions . . . . . . . . . . . . . . . . . . . . . 176 5.4.3 Logistic regression . . . . . . . . . . . . . . . . . . . . . . 177 5.4.4 Multi-class logistic regression . . . . . . . . . . . . . . . . 179 5.4.5 Probit regression . . . . . . . . . . . . . . . . . . . . . . . 181 5.4.6 Canonical link functions . . . . . . . . . . . . . . . . . . . 182 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 6 Deep Neural Networks 189 6.1 Limitations of Fixed Basis Functions . . . . . . . . . . . . . . . . 190 6.1.1 The curse of dimensionality . . . . . . . . . . . . . . . . . 190 6.1.2 High-dimensional spaces . . . . . . . . . . . . . . . . . . . 193 6.1.3 Data manifolds . . . . . . . . . . . . . . . . . . . . . . . . 194 6.1.4 Data-dependent basis functions . . . . . . . . . . . . . . . 196 6.2 Multilayer Networks . . . . . . . . . . . . . . . . . . . . . . . . . 198 6.2.1 Parameter matrices . . . . . . . . . . . . . . . . . . . . . . 199 6.2.2 Universal approximation . . . . . . . . . . . . . . . . . . . 199 6.2.3 Hidden unit activation functions . . . . . . . . . . . . . . . 200 6.2.4 Weight-space symmetries . . . . . . . . . . . . . . . . . . 203 6.3 Deep Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 6.3.1 Hierarchical representations . . . . . . . . . . . . . . . . . 205 6.3.2 Distributed representations . . . . . . . . . . . . . . . . . . 205 6.3.3 Representation learning . . . . . . . . . . . . . . . . . . . 206 6.3.4 Transfer learning . . . . . . . . . . . . . . . . . . . . . . . 207 6.3.5 Contrastive learning . . . . . . . . . . . . . . . . . . . . . 209 6.3.6 General network architectures . . . . . . . . . . . . . . . . 211 6.3.7 Tensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.4 Error Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.4.1 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.4.2 Binary classification . . . . . . . . . . . . . . . . . . . . . 214 6.4.3 multiclass classification . . . . . . . . . . . . . . . . . . . 215 6.5 Mixture Density Networks . . . . . . . . . . . . . . . . . . . . . . 216 6.5.1 Robot kinematics example . . . . . . . . . . . . . . . . . . 216 6.5.2 Conditional mixture distributions . . . . . . . . . . . . . . 217 6.5.3 Gradient optimization . . . . . . . . . . . . . . . . . . . . 219 6.5.4 Predictive distribution . . . . . . . . . . . . . . . . . . . . 220 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 7 Gradient Descent 227 7.1 Error Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 7.1.1 Local quadratic approximation . . . . . . . . . . . . . . . . 229 7.2 Gradient Descent Optimization . . . . . . . . . . . . . . . . . . . 231 7.2.1 Use of gradient information . . . . . . . . . . . . . . . . . 232 7.2.2 Batch gradient descent . . . . . . . . . . . . . . . . . . . . 232 7.2.3 Stochastic gradient descent . . . . . . . . . . . . . . . . . . 232 7.2.4 Mini-batches . . . . . . . . . . . . . . . . . . . . . . . . . 234 7.2.5 Parameter initialization . . . . . . . . . . . . . . . . . . . . 234 7.3 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 7.3.1 Momentum . . . . . . . . . . . . . . . . . . . . . . . . . . 238 7.3.2 Learning rate schedule . . . . . . . . . . . . . . . . . . . . 240 7.3.3 RMSProp and Adam . . . . . . . . . . . . . . . . . . . . . 241 7.4 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 7.4.1 Data normalization . . . . . . . . . . . . . . . . . . . . . . 244 7.4.2 Batch normalization . . . . . . . . . . . . . . . . . . . . . 245 7.4.3 Layer normalization . . . . . . . . . . . . . . . . . . . . . 247 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 8 Backpropagation 251 8.1 Evaluation of Gradients . . . . . . . . . . . . . . . . . . . . . . . 252 8.1.1 Single-layer networks . . . . . . . . . . . . . . . . . . . . 252 8.1.2 General feed-forward networks . . . . . . . . . . . . . . . 253 8.1.3 A simple example . . . . . . . . . . . . . . . . . . . . . . 256 8.1.4 Numerical differentiation . . . . . . . . . . . . . . . . . . . 257 8.1.5 The Jacobian matrix . . . . . . . . . . . . . . . . . . . . . 258 8.1.6 The Hessian matrix . . . . . . . . . . . . . . . . . . . . . . 260 8.2 Automatic Differentiation . . . . . . . . . . . . . . . . . . . . . . 262 8.2.1 Forward-mode automatic differentiation . . . . . . . . . . . 264 8.2.2 Reverse-mode automatic differentiation . . . . . . . . . . . 267 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 9 Regularization 271 9.1 Inductive Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 9.1.1 Inverse problems . . . . . . . . . . . . . . . . . . . . . . . 272 9.1.2 No free lunch theorem . . . . . . . . . . . . . . . . . . . . 273 9.1.3 Symmetry and invariance . . . . . . . . . . . . . . . . . . . 274 9.1.4 Equivariance . . . . . . . . . . . . . . . . . . . . . . . . . 277 9.2 Weight Decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 9.2.1 Consistent regularizers . . . . . . . . . . . . . . . . . . . . 280 9.2.2 Generalized weight decay . . . . . . . . . . . . . . . . . . 282 9.3 Learning Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 9.3.1 Early stopping . . . . . . . . . . . . . . . . . . . . . . . . 284 9.3.2 Double descent . . . . . . . . . . . . . . . . . . . . . . . . 286 9.4 Parameter Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . 288 9.4.1 Soft weight sharing . . . . . . . . . . . . . . . . . . . . . . 289 9.5 Residual Connections . . . . . . . . . . . . . . . . . . . . . . . . 292 9.6 Model Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 9.6.1 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 10 Convolutional Networks 305 10.1 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 10.1.1 Image data . . . . . . . . . . . . . . . . . . . . . . . . . . 307 10.2 Convolutional Filters . . . . . . . . . . . . . . . . . . . . . . . . . 308 10.2.1 Feature detectors . . . . . . . . . . . . . . . . . . . . . . . 308 10.2.2 Translation equivariance . . . . . . . . . . . . . . . . . . . 309 10.2.3 Padding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 10.2.4 Strided convolutions . . . . . . . . . . . . . . . . . . . . . 312 10.2.5 Multi-dimensional convolutions . . . . . . . . . . . . . . . 313 10.2.6 Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 10.2.7 Multilayer convolutions . . . . . . . . . . . . . . . . . . . 316 10.2.8 Example network architectures . . . . . . . . . . . . . . . . 317 10.3 Visualizing Trained CNNs . . . . . . . . . . . . . . . . . . . . . . 320 10.3.1 Visual cortex . . . . . . . . . . . . . . . . . . . . . . . . . 320 10.3.2 Visualizing trained filters . . . . . . . . . . . . . . . . . . . 321 10.3.3 Saliency maps . . . . . . . . . . . . . . . . . . . . . . . . 323 10.3.4 Adversarial attacks . . . . . . . . . . . . . . . . . . . . . . 324 10.3.5 Synthetic images . . . . . . . . . . . . . . . . . . . . . . . 326 10.4 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 10.4.1 Bounding boxes . . . . . . . . . . . . . . . . . . . . . . . 327 10.4.2 Intersection-over-union . . . . . . . . . . . . . . . . . . . . 328 10.4.3 Sliding windows . . . . . . . . . . . . . . . . . . . . . . . 329 10.4.4 Detection across scales . . . . . . . . . . . . . . . . . . . . 331 10.4.5 Non-max suppression . . . . . . . . . . . . . . . . . . . . . 332 10.4.6 Fast region CNNs . . . . . . . . . . . . . . . . . . . . . . . 332 10.5 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . 333 10.5.1 Convolutional segmentation . . . . . . . . . . . . . . . . . 333 10.5.2 Up-sampling . . . . . . . . . . . . . . . . . . . . . . . . . 334 10.5.3 Fully convolutional networks . . . . . . . . . . . . . . . . . 336 10.5.4 The U-net architecture . . . . . . . . . . . . . . . . . . . . 337 10.6 Style Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 11 Structured Distributions 343 11.1 Graphical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 11.1.1 Directed graphs . . . . . . . . . . . . . . . . . . . . . . . . 344 11.1.2 Factorization . . . . . . . . . . . . . . . . . . . . . . . . . 345 11.1.3 Discrete variables . . . . . . . . . . . . . . . . . . . . . . . 347 11.1.4 Gaussian variables . . . . . . . . . . . . . . . . . . . . . . 350 11.1.5 Binary classifier . . . . . . . . . . . . . . . . . . . . . . . 352 11.1.6 Parameters and observations . . . . . . . . . . . . . . . . . 352 11.1.7 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . 354 11.2 Conditional Independence . . . . . . . . . . . . . . . . . . . . . . 355 11.2.1 Three example graphs . . . . . . . . . . . . . . . . . . . . 356 11.2.2 Explaining away . . . . . . . . . . . . . . . . . . . . . . . 359 11.2.3 D-separation . . . . . . . . . . . . . . . . . . . . . . . . . 361 11.2.4 Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . 362 11.2.5 Generative models . . . . . . . . . . . . . . . . . . . . . . 364 11.2.6 Markov blanket . . . . . . . . . . . . . . . . . . . . . . . . 365 11.2.7 Graphs as filters . . . . . . . . . . . . . . . . . . . . . . . . 366 11.3 Sequence Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 11.3.1 Hidden variables . . . . . . . . . . . . . . . . . . . . . . . 370 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 12 Transformers 375 12.1 Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 12.1.1 Transformer processing . . . . . . . . . . . . . . . . . . . . 378 12.1.2 Attention coefficients . . . . . . . . . . . . . . . . . . . . . 379 12.1.3 Self-attention . . . . . . . . . . . . . . . . . . . . . . . . . 380 12.1.4 Network parameters . . . . . . . . . . . . . . . . . . . . . 381 12.1.5 Scaled self-attention . . . . . . . . . . . . . . . . . . . . . 384 12.1.6 Multi-head attention . . . . . . . . . . . . . . . . . . . . . 384 12.1.7 Transformer layers . . . . . . . . . . . . . . . . . . . . . . 386 12.1.8 Computational complexity . . . . . . . . . . . . . . . . . . 388 12.1.9 Positional encoding . . . . . . . . . . . . . . . . . . . . . . 389 12.2 Natural Language . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 12.2.1 Word embedding . . . . . . . . . . . . . . . . . . . . . . . 393 12.2.2 Tokenization . . . . . . . . . . . . . . . . . . . . . . . . . 395 12.2.3 Bag of words . . . . . . . . . . . . . . . . . . . . . . . . . 396 12.2.4 Autoregressive models . . . . . . . . . . . . . . . . . . . . 397 12.2.5 Recurrent neural networks . . . . . . . . . . . . . . . . . . 398 12.2.6 Backpropagation through time . . . . . . . . . . . . . . . . 399 12.3 Transformer Language Models . . . . . . . . . . . . . . . . . . . . 400 12.3.1 Decoder transformers . . . . . . . . . . . . . . . . . . . . . 401 12.3.2 Sampling strategies . . . . . . . . . . . . . . . . . . . . . . 404 12.3.3 Encoder transformers . . . . . . . . . . . . . . . . . . . . . 406 12.3.4 Sequence-to-sequence transformers . . . . . . . . . . . . . 408 12.3.5 Large language models . . . . . . . . . . . . . . . . . . . . 408 12.4 Multimodal Transformers . . . . . . . . . . . . . . . . . . . . . . 412 12.4.1 Vision transformers . . . . . . . . . . . . . . . . . . . . . . 413 12.4.2 Generative image transformers . . . . . . . . . . . . . . . . 414 12.4.3 Audio data . . . . . . . . . . . . . . . . . . . . . . . . . . 417 12.4.4 Text-to-speech . . . . . . . . . . . . . . . . . . . . . . . . 418 12.4.5 Vision and language transformers . . . . . . . . . . . . . . 420 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 13 Graph Neural Networks 425 13.1 Machine Learning on Graphs . . . . . . . . . . . . . . . . . . . . 427 13.1.1 Graph properties . . . . . . . . . . . . . . . . . . . . . . . 428 13.1.2 Adjacency matrix . . . . . . . . . . . . . . . . . . . . . . . 428 13.1.3 Permutation equivariance . . . . . . . . . . . . . . . . . . . 429 13.2 Neural Message-Passing . . . . . . . . . . . . . . . . . . . . . . . 430 13.2.1 Convolutional filters . . . . . . . . . . . . . . . . . . . . . 431 13.2.2 Graph convolutional networks . . . . . . . . . . . . . . . . 432 13.2.3 Aggregation operators . . . . . . . . . . . . . . . . . . . . 434 13.2.4 Update operators . . . . . . . . . . . . . . . . . . . . . . . 436 13.2.5 Node classification . . . . . . . . . . . . . . . . . . . . . . 437 13.2.6 Edge classification . . . . . . . . . . . . . . . . . . . . . . 438 13.2.7 Graph classification . . . . . . . . . . . . . . . . . . . . . . 438 13.3 General Graph Networks . . . . . . . . . . . . . . . . . . . . . . . 438 13.3.1 Graph attention networks . . . . . . . . . . . . . . . . . . . 439 13.3.2 Edge embeddings . . . . . . . . . . . . . . . . . . . . . . . 439 13.3.3 Graph embeddings . . . . . . . . . . . . . . . . . . . . . . 440 13.3.4 Over-smoothing . . . . . . . . . . . . . . . . . . . . . . . 440 13.3.5 Regularization . . . . . . . . . . . . . . . . . . . . . . . . 441 13.3.6 Geometric deep learning . . . . . . . . . . . . . . . . . . . 442 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 14 Sampling 447 14.1 Basic Sampling Algorithms . . . . . . . . . . . . . . . . . . . . . 448 14.1.1 Expectations . . . . . . . . . . . . . . . . . . . . . . . . . 448 14.1.2 Standard distributions . . . . . . . . . . . . . . . . . . . . 449 14.1.3 Rejection sampling . . . . . . . . . . . . . . . . . . . . . . 451 14.1.4 Adaptive rejection sampling . . . . . . . . . . . . . . . . . 453 14.1.5 Importance sampling . . . . . . . . . . . . . . . . . . . . . 455 14.1.6 Sampling-importance-resampling . . . . . . . . . . . . . . 457 14.2 Markov Chain Monte Carlo . . . . . . . . . . . . . . . . . . . . . 458 14.2.1 The Metropolis algorithm . . . . . . . . . . . . . . . . . . 459 14.2.2 Markov chains . . . . . . . . . . . . . . . . . . . . . . . . 460 14.2.3 The Metropolis–Hastings algorithm . . . . . . . . . . . . . 463 14.2.4 Gibbs sampling . . . . . . . . . . . . . . . . . . . . . . . . 464 14.2.5 Ancestral sampling . . . . . . . . . . . . . . . . . . . . . . 468 14.3 Langevin Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . 469 14.3.1 Energy-based models . . . . . . . . . . . . . . . . . . . . . 470 14.3.2 Maximizing the likelihood . . . . . . . . . . . . . . . . . . 471 14.3.3 Langevin dynamics . . . . . . . . . . . . . . . . . . . . . . 472 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 15 Discrete Latent Variables 477 15.1 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . 478 15.1.1 Image segmentation . . . . . . . . . . . . . . . . . . . . . 482 15.2 Mixtures of Gaussians . . . . . . . . . . . . . . . . . . . . . . . . 484 15.2.1 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 486 15.2.2 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 488 15.3 Expectation–Maximization Algorithm . . . . . . . . . . . . . . . . 492 15.3.1 Gaussian mixtures . . . . . . . . . . . . . . . . . . . . . . 496 15.3.2 Relation to K-means . . . . . . . . . . . . . . . . . . . . . 498 15.3.3 Mixtures of Bernoulli distributions . . . . . . . . . . . . . . 499 15.4 Evidence Lower Bound . . . . . . . . . . . . . . . . . . . . . . . 503 15.4.1 EM revisited . . . . . . . . . . . . . . . . . . . . . . . . . 504 15.4.2 Independent and identically distributed data . . . . . . . . . 506 15.4.3 Parameter priors . . . . . . . . . . . . . . . . . . . . . . . 507 15.4.4 Generalized EM . . . . . . . . . . . . . . . . . . . . . . . 507 15.4.5 Sequential EM . . . . . . . . . . . . . . . . . . . . . . . . 508 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 16 Continuous Latent Variables 513 16.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . 515 16.1.1 Maximum variance formulation . . . . . . . . . . . . . . . 515 16.1.2 Minimum-error formulation . . . . . . . . . . . . . . . . . 517 16.1.3 Data compression . . . . . . . . . . . . . . . . . . . . . . . 519 16.1.4 Data whitening . . . . . . . . . . . . . . . . . . . . . . . . 520 16.1.5 High-dimensional data . . . . . . . . . . . . . . . . . . . . 522 16.2 Probabilistic Latent Variables . . . . . . . . . . . . . . . . . . . . 524 16.2.1 Generative model . . . . . . . . . . . . . . . . . . . . . . . 524 16.2.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 525 16.2.3 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . 527 16.2.4 Factor analysis . . . . . . . . . . . . . . . . . . . . . . . . 531 16.2.5 Independent component analysis . . . . . . . . . . . . . . . 532 16.2.6 Kalman filters . . . . . . . . . . . . . . . . . . . . . . . . . 533 16.3 Evidence Lower Bound . . . . . . . . . . . . . . . . . . . . . . . 534 16.3.1 Expectation maximization . . . . . . . . . . . . . . . . . . 536 16.3.2 EM for PCA . . . . . . . . . . . . . . . . . . . . . . . . . 537 16.3.3 EM for factor analysis . . . . . . . . . . . . . . . . . . . . 538 16.4 Nonlinear Latent Variable Models . . . . . . . . . . . . . . . . . . 540 16.4.1 Nonlinear manifolds . . . . . . . . . . . . . . . . . . . . . 540 16.4.2 Likelihood function . . . . . . . . . . . . . . . . . . . . . . 542 16.4.3 Discrete data . . . . . . . . . . . . . . . . . . . . . . . . . 544 16.4.4 Four approaches to generative modelling . . . . . . . . . . 545 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 17 Generative Adversarial Networks 551 17.1 Adversarial Training . . . . . . . . . . . . . . . . . . . . . . . . . 552 17.1.1 Loss function . . . . . . . . . . . . . . . . . . . . . . . . . 553 17.1.2 GAN training in practice . . . . . . . . . . . . . . . . . . . 554 17.2 Image GANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 17.2.1 CycleGAN . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562 18 Normalizing Flows 565 18.1 Coupling Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 18.2 Autoregressive Flows . . . . . . . . . . . . . . . . . . . . . . . . . 570 18.3 Continuous Flows . . . . . . . . . . . . . . . . . . . . . . . . . . 572 18.3.1 Neural differential equations . . . . . . . . . . . . . . . . . 572 18.3.2 Neural ODE backpropagation . . . . . . . . . . . . . . . . 573 18.3.3 Neural ODE flows . . . . . . . . . . . . . . . . . . . . . . 575 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577 19 Autoencoders 581 19.1 Deterministic Autoencoders . . . . . . . . . . . . . . . . . . . . . 582 19.1.1 Linear autoencoders . . . . . . . . . . . . . . . . . . . . . 582 19.1.2 Deep autoencoders . . . . . . . . . . . . . . . . . . . . . . 583 19.1.3 Sparse autoencoders . . . . . . . . . . . . . . . . . . . . . 584 19.1.4 Denoising autoencoders . . . . . . . . . . . . . . . . . . . 585 19.1.5 Masked autoencoders . . . . . . . . . . . . . . . . . . . . . 585 19.2 Variational Autoencoders . . . . . . . . . . . . . . . . . . . . . . . 587 19.2.1 Amortized inference . . . . . . . . . . . . . . . . . . . . . 590 19.2.2 The reparameterization trick . . . . . . . . . . . . . . . . . 592 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596 20 Diffusion Models 599 20.1 Forward Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 20.1.1 Diffusion kernel . . . . . . . . . . . . . . . . . . . . . . . 601 20.1.2 Conditional distribution . . . . . . . . . . . . . . . . . . . 602 20.2 Reverse Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 20.2.1 Training the decoder . . . . . . . . . . . . . . . . . . . . . 605 20.2.2 Evidence lower bound . . . . . . . . . . . . . . . . . . . . 606 20.2.3 Rewriting the ELBO . . . . . . . . . . . . . . . . . . . . . 607 20.2.4 Predicting the noise . . . . . . . . . . . . . . . . . . . . . . 609 20.2.5 Generating new samples . . . . . . . . . . . . . . . . . . . 610 20.3 Score Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 20.3.1 Score loss function . . . . . . . . . . . . . . . . . . . . . . 613 20.3.2 Modified score loss . . . . . . . . . . . . . . . . . . . . . . 614 20.3.3 Noise variance . . . . . . . . . . . . . . . . . . . . . . . . 615 20.3.4 Stochastic differential equations . . . . . . . . . . . . . . . 616 20.4 Guided Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 20.4.1 Classifier guidance . . . . . . . . . . . . . . . . . . . . . . 618 20.4.2 Classifier-free guidance . . . . . . . . . . . . . . . . . . . 618 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 Appendix A Linear Algebra 627 A.1 Matrix Identities . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 A.2 Traces and Determinants . . . . . . . . . . . . . . . . . . . . . . . 628 A.3 Matrix Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . 629 A.4 Eigenvectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 Appendix B Calculus of Variations 635 Appendix C Lagrange Multipliers 639 Bibliography 643 Index 659

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Book SynopsisPolitical issues people care about such as racism, climate change, and democracy take on new urgency and meaning in the light of technological developments such as AI. How can we talk about the politics of AI while moving beyond mere warnings and easy accusations? This is the first accessible introduction to the political challenges related to AI. Using political philosophy as a unique lens through which to explore key debates in the area, the book shows how various political issues are already impacted by emerging AI technologies: from justice and discrimination to democracy and surveillance. Revealing the inherently political nature of technology, it offers a rich conceptual toolbox that can guide efforts to deal with the challenges raised by what turns out to be not only artificial intelligence but also artificial power. This timely and original book will appeal to students and scholars in philosophy of technology and political philosophy, as well as tech developers, innovation leaders, policy makers, and anyone interested in the impact of technology on society.​Trade Review“The disciplines of AI ethics and political philosophy focus on many of the same issues, but only rarely do we see the rich history of the latter discipline being used to make sense of the politics of AI. Coeckelbergh provides a welcome exception with this important book.”Henrik Skaug Sætra, Østfold University College “Artificial intelligence is fundamentally political, and this book illuminates why. It spans the debates about inequality, democracy, power, and posthumanism, and shows the importance of social and political theory to understanding AI.”Kate Crawford, author of Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence“Coeckelbergh[‘s] […] real focus is on showing a few thousand years’ worth of philosophical thought will not automatically become obsolete through feats of digital engineering.”Scott McLemee, Inside Higher EdTable of ContentsAcknowledgements 1 Introduction 2 Freedom: Manipulation by AI and Robot Slavery3 Equality and Justice: Bias and Discrimination by AI4 Democracy: Echo Chambers and Machine Totalitarianism 5 Power: Surveillance and (Self-)disciplining by Data6 What about Non-Humans? Environmental Politics and Posthumanism 7 Conclusion: Political TechnologiesReferencesIndex

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Book SynopsisThe future of AI is here.The world is transfixed by the marvel (and possible menace) of ChatGPT and other generative AI tools. It's clear Gen AI will transform the business landscape, but when and how much remain to be seen. Meanwhile, your smartest competitors are already navigating the risks and reaping the rewards of these new technologies. They're experimenting with new business models around generating text, images, and code at astonishing speed. They're automating customer interactions in ways never before possible. And they're augmenting human creativity in order to innovate faster. How can you take advantage of generative AI and avoid having your business disrupted?Generative AI: The Insights You Need from Harvard Business Review will help you understand the potential of these new technologies, pick the right Gen AI projects, and reinvent your business for the new age of AI.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.

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Book SynopsisAI is ready for business. Are you ready for AI?From financial modeling and product design to performance management and hiring decisions, AI and machine learning are becoming everyday tools for managers at businesses of all sizes. But AI systems come with benefits and downsides—and if you can't make sense of them, you're not going to make the right decisions.Whether you need to get up to speed quickly or need a refresher, or you're working with an AI expert for the first time, the HBR Guide to AI Basics for Managers will give you the information and skills you need to succeed.You'll learn how to: Understand key AI terms and concepts Recognize which of your projects would benefit from AI Work more effectively with your data team Hire the right AI vendors and consultants Deal with ethical risks before they arise Scale AI across your organization Arm yourself with the advice you need to succeed on the job, with the most trusted brand in business. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges.

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Book SynopsisAI has unparalleled transformative potential to reshape society but without legal scrutiny, international oversight and public debate, we are sleepwalking into a future written by algorithms which encode regressive biases into our daily lives. As governments and corporations worldwide embrace AI technologies in pursuit of efficiency and profit, we are at risk of losing our common humanity: an attack that is as insidious as it is pervasive. Leading privacy expert Ivana Bartoletti exposes the reality behind the AI revolution, from the low-paid workers who train algorithms to recognise cancerous polyps, to the rise of data violence and the symbiotic relationship between AI and right-wing populism. Impassioned and timely, An Artificial Revolution is an essential primer to understand the intersection of technology and geopolitical forces shaping the future of civilisation, and the political response that will be required to ensure the protection of democracy and human rights.Trade ReviewReview: An Artificial Revolution ‘This is a great read, giving you enough information to perhaps inspire you to look into this further, or to just consider where your data is held and what it is being used for.’ http://independentbookreviews.co.uk/book/an-artificial-revolution/ -- Fiona Sharp * Independent Book Reviews *‘Books of the Year 2020’ ‘A great book for those interested in AI and power-dynamics.’ https://burleyfisherbooks.com/blogs/news/books-of-the-year-2020 -- Enya Nolan * Burley Fisher Books *‘An Interview with Ivana Bertoletti, Technical Director at Deloitte.’ ‘We cannot leave AI and its future to the technologists. AI is about power, and this is the time to ensure that power benefits us all. I wrote An Artificial Revolution because I wanted people to talk about AI at the kitchen table.’ https://www.trustinsoda.com/blog/an-interview-with-ivana-bartoletti-technical-director-at-deloitte--253495/ -- Alfie Rice * SODA *‘Modern democracy: Data, surveillance creep and more authoritarian regimes?’ ‘What are governments and corporations doing with the data they are collecting, and what is the ultimate end goal? As Ivana Bartoletti states in her recent book An Artificial Revolution On Power, Politics and AI, “Data is not neutral, and the fact that we collect a huge amount of it brings many challenges – not just from the standpoint of privacy but also from the standpoint of power dynamics”.’ https://www.orfonline.org/expert-speak/modern-democracy-data-surveillance-creep-and-more-authoritarian-regimes/ -- Oriana Medicott * Observer Research Foundation *

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Book SynopsisAI is primed to revolutionize the way we build applications, offering exciting new ways to solve problems, uncover insights, innovate new products, and provide better user experiences. Successful AI is based on a set of core algorithms that form a base of knowledge shared by all data scientists. Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, readers learn the concepts, terminology, and theory they need to effectively incorporate AI algorithms into their applications. Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Don’t worry if you aren’t a calculus wunderkind; you’ll need only the algebra you picked up in math class. • Use cases for different AI algorithms • How to encode problems and solutions using data structures • Intelligent search for game playing • Ant colony algorithms for path finding • Evolutionary algorithms for optimization problems For software developers with high school-level algebra and calculus skills.

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Book SynopsisWhile many companies have already begun their journey towards high automation and AI-powered operations, the landscape continues to shift rapidly. This fully revised and extended new edition explores the ever-evolving landscape of AI in business, towards the autonomous enterprise.Building on the success of the first edition, this book equips corporate decision-makers and IT managers with the knowledge to navigate this dynamic environment, including the latest developments in generative and agentic AI. This book explains the opportunities and risks that the autonomous enterprise presents and how best to navigate the shifting competitive landscape on their journey of change.This book is your guide to AI innovation, presenting key concepts in real world contexts, covering the art of the possible today and providing glimpses into the future of business.

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Book SynopsisTable of Contents CHAPTER 1 Describe Artificial Intelligence workloads and considerations CHAPTER 2 Describe fundamental principles of machine learning on Azure CHAPTER 3 Describe features of computer vision workloads on Azure CHAPTER 4 Describe features of Natural Language Processing (NLP) workloads on Azure CHAPTER 5 Describe features of conversational AI workloads on Azure

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Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Book SynopsisThis concise guide explains the history, theory, potential, application, and limitations of Artificial Intelligence. Boden shows how research into AI has shed light on the working of human and animal minds, and she considers the philosophical challenges AI raises: could programs ever be really intelligent, creative or even conscious?Trade ReviewReview from previous edition Boden's book is an excellent, accessible introduction even for the complete AI novice. * Mark Greener, Fortean Times *Boden, as an academic in the field of AI, really knows her stuff, and you get a clear understanding from her book of the various different kinds of AI, and their enduring limitations * Robert Colvile, The Spectator *A masterclass of a book * Barbara Kiser, Nature *Provides a usefully concise, basic grounding to topics without having to wade through a more voluminous tome. * Jonathan Cowie, Science Fact & Science Fiction Concatenation *Everything you need to know about Artificial Intelligence - a wonderful read. * Jack Copeland, Director of the Turing Archive for the History of Computing *Table of Contents1: What is Artificial Intelligence? 2: Generality as the Holy Grail 3: Language, Creativity, Emotion 4: Artificial Neural Networks 5: Robots and Artificial Life 6: But is it Intelligence, Really? 7: The Singularity Further Reading Index

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Book SynopsisExplores how the impact of code on life has become comparable to that of speech and writing: as language and code have grown entangled, the lines that once separated humans from machines, analog from digital, and old technologies from new ones have become blurred. The book gives us the tools necessary to make sense of these complex relationships.Trade Review"A deeply insightful and significant investigation of how the science and rhetorics of cybernetics have reshaped the boundaries of human identity." - Village Voice "In her important new book, N. Katherine Hayles... traces the evolution over the last half-century of a radical reconception of what it means to be human and, indeed, even of what it means to be alive, a reconception unleashed by the interplay of humans and intelligent machines." - Chicago Tribune"

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Book SynopsisComputing power has revolutionized the theory and practice of statistical inference. This book delivers a concentrated course in modern statistical thinking by tracking the revolution from classical theories to the large-scale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text.Trade Review'How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples.' Andrew Gelman, Columbia University, New York'This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. The book explains this 'why'; that is, it explains the purpose and progress of statistical research through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.' Rob Kass, Carnegie Mellon University, Pennsylvania'This is a terrific book. It gives a clear, accessible, and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of 'big data' within the framework of established statistical theory.' Alastair Young, Imperial College London'This is a guided tour of modern statistics that emphasizes the conceptual and computational advances of the last century. Authored by two masters of the field, it offers just the right mix of mathematical analysis and insightful commentary.' Hal Varian, Google'Efron and Hastie guide us through the maze of breakthrough statistical methodologies following the computing evolution: why they were developed, their properties, and how they are used. Highlighting their origins, the book helps us understand each method's roles in inference and/or prediction. The inference-prediction distinction maintained throughout the book is a welcome and important novelty in the landscape of statistics books.' Galit Shmueli, National Tsing Hua University'A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century.' Stephen Stigler, University of Chicago, and author of Seven Pillars of Statistical Wisdom'Computer Age Statistical Inference offers a refreshing view of modern statistics. Algorithmics are put on equal footing with intuition, properties, and the abstract arguments behind them. The methods covered are indispensable to practicing statistical analysts in today's big data and big computing landscape.' Robert Gramacy, University of Chicago Booth School of Business'Every aspiring data scientist should carefully study this book, use it as a reference, and carry it with them everywhere. The presentation through the two-and-a-half-century history of statistical inference provides insight into the development of the discipline, putting data science in its historical place.' Mark Girolami, Imperial College London'Efron and Hastie are two immensely talented and accomplished scholars who have managed to brilliantly weave the fiber of 250 years of statistical inference into the more recent historical mechanization of computing. This book provides the reader with a mid-level overview of the last 60-some years by detailing the nuances of a statistical community that, historically, has been self-segregated into camps of Bayes, frequentist, and Fisher yet in more recent years has been unified by advances in computing. What is left to be explored is the emergence of, and role that, big data theory will have in bridging the gap between data science and statistical methodology. Whatever the outcome, the authors provide a vision of high-speed computing having tremendous potential to enable the contributions of statistical inference toward methodologies that address both global and societal issues.' Rebecca Doerge, Carnegie Mellon University, Pennsylvania'In this book, two masters of modern statistics give an insightful tour of the intertwined worlds of statistics and computation. Through a series of important topics, Efron and Hastie illuminate how modern methods for predicting and understanding data are rooted in both statistical and computational thinking. They show how the rise of computational power has transformed traditional methods and questions, and how it has pointed us to new ways of thinking about statistics.' David Blei, Columbia University, New York'Absolutely brilliant. This beautifully written compendium reviews many big statistical ideas, including the authors' own. A must for anyone engaged creatively in statistics and the data sciences, for repeated use. Efron and Hastie demonstrate the ever-growing power of statistical reasoning, past, present, and future.' Carl Morris, Harvard University, Massachusetts'Computer Age Statistical Inference gives a lucid guide to modern statistical inference for estimation, hypothesis testing, and prediction. The book seamlessly integrates statistical thinking with computational thinking, while covering a broad range of powerful algorithms for learning from data. It is extraordinarily rare and valuable to have such a unified treatment of classical (and classic) statistical ideas and recent 'big data' and machine learning ideas. Accessible real-world examples and insightful remarks can be found throughout the book.' Joseph K. Blitzstein, Harvard University, Massachusetts'Among other things, it is an attempt to characterize the current state of statistics by identifying important tools in the context of their historical development. It also offers an enlightening series of illustrations of the interplay between computation and inference … This is an attractive book that invites browsing by anyone interested in statistics and its future directions.' Bill Satzer, Mathematical Association of America Reviews'My take on Computer Age Statistical Inference is that experienced statisticians will find it helpful to have such a compact summary of twentieth-century statistics, even if they occasionally disagree with the book's emphasis; students beginning the study of statistics will value the book as a guide to statistical inference that may offset the dangerously mind-numbing experience offered by most introductory statistics textbooks; and the rest of us non-experts interested in the details will enjoy hundreds of hours of pleasurable reading.' Joseph Rickert, RStudio (www.rstudio.com)'Efron and Hastie (both, Stanford Univ.) have superbly crafted a central text/reference book that presents a broad overview of modern statistics. The work examines major developments in computation from the late-20th and early-21st centuries, ranging from electronic computations to 'big data' analysis. Focusing primarily on the last six decades, the text thoroughly documents the progression within the discipline of statistics … This text is highly recommended for graduate libraries.' D. J. Gougeon, ChoiceTable of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First Century Topics: 15. Large-scale hypothesis testing and false discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Index.

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Book SynopsisWhat is AI? A dominant view describes it as the quest "to solve intelligence" - a solution supposedly to be found in the secret logic of the mind or in the deep physiology of the brain, such as in its complex neural networks. The Eye of the Master argues, to the contrary, that the inner code of AI is shaped not by the imitation of biological intelligence, but the intelligence of labour and social relations, as it is found in Babbage's "calculating engines" of the industrial age as well as in the recent algorithms for image recognition and surveillance. The idea that AI may one day become autonomous (or "sentient", as someone thought of Google's LaMDA) is pure fantasy. Computer algorithms have always imitated the form of social relations and the organisation of labour in their own inner structure and their purpose remains blind automation. The Eye of the Master urges a new literacy on AI for scientists, journalists and new generations of activists, who should recognise that the "mystery" of AI is just the automation of labour at the highest degree, not intelligence per se.Trade ReviewWe are surrounded by stories about AI threatening jobs, as if it were a power haunting labor from outside and above. The Eye of the Master radically challenges such a view. What Matteo Pasquinelli demonstrates is that labor is at root of the historical development of AI. Tales of expropriation and resistance, automation and struggle crisscross the pages of this passionate book, which is at same time an amazing academic achievement and a political weapon to rethink the politics of AI. -- Sandro Mezzadra, co-author of The Politics of OperationsIn this original and extremely timely book, Matteo Pasquinelli offers nothing less than a long-range history and critical analysis of a labour theory of automation and knowledge. He uses detailed studies both of the remarkable accounts of general intellect and the extractive and exploitative organisation of the industrial workplace produced in nineteenth-century British political economy and of the challenging developments of models of machine intelligence and computational systems developed in the mid-twentieth century United States to unlock the sources and meanings of the politics of artificial intelligence. The work shows how Marx's depiction of the development of the social individual under industrial capitalism provides indispensable resources for making sense now of what artificial intelligence means, and the forms of economic and political order that its embodiment of knowledge and control express. At a moment when apostles and prophets of machine intelligence proclaim both a utopian world of effortless control and a catastrophe of extinction, Pasquinelli's patient and clever work provides a crucial insight into the past and future of AI monopolies and their consequences. -- Simon Schaffer, author of Babbage’s Intelligence (1994) and OK computer (2001)Artificial Intelligence and its impact on society is on everyone's lips, but how was AI shaped by society in the first place? This amazing account of its emergence, starting with the evolution of labor division and automatization, is a must-read. Pasquinelli's book not only shows us where we came from but also how we might escape the problematic consequences of this evolution. -- Jürgen Renn, Director at the Max Planck Institute for the History of Science and Founding Director of the Max Planck Institute for Geoanthropology.Table of ContentsIntroduction: AI as Division of Labour1 The Material Tools of Algorithmic ThinkingPart ITHE INDUSTRIAL AGE2 Babbage and the Mechanisation of Mental Labour3 The Machinery Question4 The Origins of Marx's General Intellect5 The Abstraction of LabourPart IITHE INFORMATION AGE6 The Self-Organisation of the Cybernetic Mind7 The Automation of Pattern Recognition8 Hayek and the Epistemology of Connectionism9 Th e Invention of the PerceptronConclusion: The Automation of General Intelligence

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Book SynopsisDr. Qinghua Lu is a principal research scientist and leads the Responsible AI science team at CSIRO's Data61. She received her PhD from University of New South Wales in 2013. Her current research interests include responsible AI, software engineering for AI/GAI, and software architecture. She has published 150+ papers in premier international journals and conferences. Her recent paper titled Towards a Roadmap on Software Engineering for Responsible AI received the ACM Distinguished Paper Award. Dr. Lu is part of the OECD.AI's trustworthy AI metrics project team. She also serves a member of Australia's National AI Centre Responsible AI at Scale think tank. She is the winner of the 2023 APAC Women in AI Trailblazer Award.   Dr./Prof. Liming Zhu is a Research Director at CSIRO's Data61 and a conjoint full professor at the University of New South Wales (UNSW). He is the chairperson of Standards Australia's blockchain committee and conTable of Contents Preface.. . . . . . . . . . . . . . . . . xv About the Author.. . . . . . . . . . . . . . xix Part I Background and Introduction. . . . . . . . . . . . .1 1 Introduction to Responsible AI. . . . . . . . . 3 What Is Responsible AI?. . . . . . . . . . . . 4 What Is AI?. . . . . . . . . . . . . . 6 Developing AI Responsibly: Who Is Responsible for Putting the “Responsible” into AI?.. . . . . . . . . . . . 8 About This Book.. . . . . . . . . . . . . 9 How to Read This Book.. . . . . . . . . . . . 11 2 Operationalizing Responsible AI: A Thought Experiment—Robbie the Robot.. . . . . . . . 13 A Thought Experiment—Robbie the Robot.. . . . . . . . 13 Summary. . . . . . . . . . . . . . 22 Part II Responsible AI Pattern Catalogue. . . . . . . . . . .  23 3 Overview of the Responsible AI Pattern Catalogue. . . . . 25 The Key Concepts.. . . . . . . . . . . . . 25 Why Is Responsible AI Different?. . . . . . . . . . 30 A Pattern-Oriented Approach for Responsible AI.. . . . . . . 32 4 Multi-Level Governance Patterns for Responsible AI.. . . . 39 Industry-Level Governance Patterns. . . . . . . . . 42 Organization-Level Governance Patterns.. . . . . . . . 56 Team-Level Governance Patterns.. . . . . . . . . . 72 Summary. . . . . . . . . . . . . . 85 5 Process Patterns for Trustworthy Development Processes. . . 87 Requirements.. . . . . . . . . . . . . 88 Design. . . . . . . . . . . . . . . 96 Implementation.. . . . . . . . . . . . . 105 Testing. . . . . . . . . . . . . . . 110 Operations. . . . . . . . . . . . . . 114 Summary. . . . . . . . . . . . . . 120 6 Product Patterns for Responsible-AI-by-Design.. . . . . 121 Product Pattern Collection Overview.. . . . . . . . . 122 Supply Chain Patterns. . . . . . . . . . . . 123 System Patterns. . . . . . . . . . . . . 134 Operation Infrastructure Patterns. . . . . . . . . 141 Summary. . . . . . . . . . . . . . 158 7 Pattern-Oriented Reference Architecture for Responsible-AI-by-Design. . . . . . . . . 159 Architectural Principles for Designing AI Systems. . . . . . 160 Pattern-Oriented Reference Architecture.. . . . . . . . 161 Summary. . . . . . . . . . . . . . 165 8 Principle-Specific Techniques for Responsible AI.. . . . . 167 Fairness.. . . . . . . . . . . . . . 167 Privacy. . . . . . . . . . . . . . . 172 Explainability. . . . . . . . . . . . . 178 Summary. . . . . . . . . . . . . . 182 Part III Case Studies. . . . . . . . . . . . . . . 183 9 Risk-Based AI Governance in Telstra. . . . . . . 185 Policy and Awareness.. . . . . . . . . . . . 186 Assessing Risk.. . . . . . . . . . . . . 188 Learnings from Practice. . . . . . . . . . . 192 Future Work. . . . . . . . . . . . . . 195 10 Reejig: The World’s First Independently Audited Ethical Talent AI.. . . . . . . . . . . 197 How Is AI Being Used in Talent?.. . . . . . . . . . 198 What Does Bias in Talent AI Look Like?.. . . . . . . . 200 Regulating Talent AI Is a Global Issue.. . . . . . . . . 201 Reejig’s Approach to Ethical Talent AI. . . . . . . . . 202 How Ethical AI Evaluation Is Done: A Case Study in Reejig’s World-First Independently Audited Ethical Talent AI. . . . . . . . 204 Overview.. . . . . . . . . . . . . 204 Project Overview. . . . . . . . . . . . . 206 The Ethical AI Framework Used for the Audit.. . . . . . . 207 The Benefits of Ethical Talent AI.. . . . . . . . . . 210 Reejig’s Outlook on the Future of Ethical Talent AI.. . . . . . 211 11 Diversity and Inclusion in Artificial Intelligence.. . . . . 213 Importance of Diversity and Inclusion in AI.. . . . . . . 215 Definition of Diversity and Inclusion in Artificial Intelligence. . . . 216 Guidelines for Diversity and Inclusion in Artificial Intelligence. . . . 219 Conclusion.. . . . . . . . . . . . . . 234 Part IV Looking to the Future. . . . . . . . . . . . . 237 12 The Future of Responsible AI.. . . . . . . . . 239 Regulation. . . . . . . . . . . . . . 241 Education.. . . . . . . . . . . . . . 242 Standards.. . . . . . . . . . . . . . 244 Tools.. . . . . . . . . . . . . . . 245 Public Awareness.. . . . . . . . . . . . 246 Final Remarks.. . . . . . . . . . . . . 246 Part V Appendix. . . . . . . . . . . . . . . . 249 9780138073923, TOC, 11/7/2023

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Book SynopsisAn in-depth assessment of innovations in military information technology informs hypothetical outcomes for artificial intelligence adaptations In the coming decades, artificial intelligence (AI) could revolutionize the way humans wage war. The military organizations that best innovate and adapt to this AI revolution will likely gain significant advantages over their rivals. To this end, great powers such as the United States, China, and Russia are already investing in novel sensing, reasoning, and learning technologies that will alter how militaries plan and fight. The resulting transformation could fundamentally change the character of war. In Information in War, Benjamin Jensen, Christopher Whyte, and Scott Cuomo provide a deeper understanding of the AI revolution by exploring the relationship between information, organizational dynamics, and military power. The authors analyze how militaries adjust to new information communication technology historically to identify opportunities, risks, and obstacles that will almost certainly confront modern defense organizations as they pursue AI pathways to the future. Information in War builds on these historical cases to frame four alternative future scenarios exploring what the AI revolution could look like in the US military by 2040.Trade ReviewJensen, Whyte, and Cuomo’s thought-provoking book is less about the promise of the military uses of AI and more about why that promise may not be realized. * Foreign Affairs *The authors, coming from different institutional backgrounds, have written a short book that is more than the sum of its parts. * Choice *Table of ContentsList of IllustrationsPreface1. Will Artificial Intelligence Change War?2. An Information Theory of Military Innovation3. The Uncertain Rise of Radar4. Creating the First Computerized Battle Network5. The Revolution in Military Affairs6. The Global Battle Network7. Using the Past to Chart Alternative FuturesBibliographyIndexAbout the Authors

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Book Synopsis'This colourful page-turner puts artificial intelligence into a human perspective . . . Metz explains this transformative technology and makes the quest thrilling.' Walter Isaacson, author of Steve Jobs____________________________________________________This is the inside story of a small group of mavericks, eccentrics and geniuses who turned Artificial Intelligence from a fringe enthusiasm into a transformative technology. It's the story of how that technology became big business, creating vast fortunes and sparking intense rivalries. And it's the story of breakneck advances that will shape our lives for many decades to come - both for good and for ill. ________________________________________________'One day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story - the Genesis for an age of sentient machines.' Brad Stone, author of The Everything Store and The Upstarts'A ringside seat at what may turn out to be the pivotal episode in human history . . . easy and fun to read . . . undeniably charming.' ForbesTrade ReviewIn Genius Makers, Cade Metz delivers the definitive take on how AI technology came to be and what its arrival will mean for us humans. The book relies on tireless reporting and delightful writing to bring to life one of the most surprising and important stories of our time. If you want to read one book to understand AI, this is the one. -- Ashlee Vance, New York Times bestselling author of ELON MUSKThis colourful page-turner puts artificial intelligence into a human perspective. Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling. -- Walter Isaacson, #1 New York Times bestselling author of LEONARDO DA VINCI, STEVE JOBS and THE INNOVATORSCade Metz has produced an enthralling narrative of the advance of artificial intelligence. He describes the key personalities, the seminal meetings and the crucial breakthroughs with his customary eye for detail, building them into a dramatic history of this era-defining technology. -- Kai-Fu Lee, author of AI SUPERPOWERSThis is the inside story of how AI entered Google, Facebook, and the rest of high tech. It is also the story of how Silicon Valley and its mega-bucks infiltrated AI and changed its course. Chock full of behind-the-scenes anecdotes and wry humour - we learn the true tale of the technology that is transforming humanity. -- Oren Etzioni, chief executive, Allen Institute for Artificial IntelligenceOne day soon, when computers are safely driving our roads and speaking to us in complete sentences, we'll look back at Cade Metz's elegant, sweeping Genius Makers as their birth story - the Genesis for an age of sentient machines. -- Brad Stone, author of THE EVERYTHING STORE and THE UPSTARTS

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Book SynopsisThis cutting-edge guide not only shows how AI is transforming our careers, lives, businesses, and more, but also provides easy, actionable steps to make AI work for us. In this groundbreaking book, celebrated professor, entrepreneur, author, and podcaster Marc Beckman explores the transformative power of artificial intelligence (AI) and how it’s poised to enhance and transform all aspects of society—revolutionizing our careers, enriching our family lives, and bringing our communities closer together. From business and advertising, to medicine, to warfare, to politics—Beckman meticulously explores the different areas where we’ll soon feel AI’s transformative impact. But that’s only half of it. Throughout this book, he also provides the specific steps readers can take now to make sure these coming changes work for them.   From the workplace to the home, AI is poised to reshape the way we approach our professional and personal lives. Beckman uses this book to make the case that AI will free up valuable time and energy, allowing individuals to focus on more creative and meaningful work, but also that AI will create possibilities for engagement that were unthinkable just a generation ago. He shows that with AI as our co-pilot, we’ll unlock new opportunities for growth, innovation, and collaboration—all of which will lead to more fulfilling and rewarding careers. Beckman illustrates how AI will strengthen family bonds and improve the quality of our home lives too, changing everything from how we educate our kids to how we stay connected on social media. And as AI becomes more integrated into our cities and towns, it will play a crucial role in fostering a sense of community and belonging; through AI-powered platforms, Beckman shows how we will collaborate on projects, share resources, and support one another in times of need.   This thought-provoking and essential book is a definitive guide to the many ways in which AI will transform our lives for the better . . . but also surprise us, delight us, force us to (re)consider how we interact with one another, and make us question what exactly counts as “human.” Join Marc Beckman on this exciting journey as he explores the near-endless possibilities of a world powered and transformed by artificial intelligence. It’s an Age of Imagination . . . where the only limit is your own mind.

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Book SynopsisHow smart machines are transforming us all — and what we should do about it. The smart-machines revolution is re-shaping our lives and our societies. Here, Nigel Shadbolt, one of Britain’s leading authorities on artificial intelligence, and Roger Hampson dispel terror, confusion, and misconception. They argue that it is human stupidity, not artificial intelligence, that should concern us. Lucid, well-informed, and deeply human, The Digital Ape offers a unique approach to some of the biggest questions about our future.Trade Review‘[W]e should be grateful to Sir Nigel Shadbolt and Roger Hampson for pausing for breath and helping us to think through the true significance of our latest technological developments.’ * Financial Times *‘Numbed by dire warnings of technological Armageddon? Computer scientist Nigel Shadbolt and economist Roger Hampson dispel the miasma with this superb survey of the landscape we “digital apes” have wrought.’ -- Barbara Kiser * Nature *'Nigel Shadbolt is one of the most fascinating and important scientists alive today.' -- Professor Jim Al-Khalili'There has never been a more important time to discuss what it means to be human, in the past, now, and in the future. This is a book for anyone interested in getting behind the headlines and understanding how technology is impacting our world. The writers are two masters in their field who are not only erudite but immensely humane and compassionate.' -- Martha Lane Fox'This is a brilliantly readable, genuinely cutting-edge book that is also often very entertaining. Of all the recent studies of automation and AI, The Digital Ape stands head and shoulders above the rest. Shadbolt and Hampson have written a landmark book.' * Andrew Keen, author of How to Fix the Future and The Internet is Not the Answer *‘Rich in ideas and insights, the book is especially strong on our growing personal relationships with Alexa and other robots … An upbeat — even reassuring — take on what will be an AI-saturated future.’ STARRED REVIEW * Kirkus Reviews *‘All explore the relationship between the human animal and what might be its most momentous creation yet: artificial intelligence … In a series of wide-ranging chapters, the authors argue that human beings are not just distinguished by their ability to use tools but also largely shaped by it.’ * Weekend Australian *‘[An] interdisciplinary approach comes over in The Digital Ape, which has arresting sentences.’ * Computer Weekly *

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Book SynopsisSir Anthony Seldon, the prominent political biographer and leading educationalist, addresses one of the high-stakes issues that will influence our future: the role of artificial intelligence and its impact on education.The use of AI promises an altogether new way of educating, offering learners from all backgrounds widespread access to personalised tuition and digital educational materials from across the world. Educational institutions across the world have been impacted by the COVID-19 pandemic and many have migrated, at least temporarily, to online platforms. The debate about how to deliver knowledge has never been more relevant.Many countries have an excellent education system with their schools and universities excellent, but tailored to the twentieth century. The mass teaching methods of the third revolution era have failed to conquer enduring problems of inequity and lack of individualised learning. AI is disrupting the way we live, work and interact with the environment, and we cannot stop it changing our schools and universities. But we have time albeit not for long to shape this revolution. It will not be a panacea, and if we are not quick, it will start to replace what makes us human being creative, having beliefs, and loving others.This book, presented in considerably updated and extended second edition, is a call to educators everywhere to open their eyes to what is coming. If we do so, then the future will be shaped by us for the common interests of humanity but if we don't, then it will be imposed, and we will all lose.This book has the potential to impel change in our education system which is so badly in need of reform. The new reconsidered version in the wake of the COVID pandemic serves to emphasize even more strongly the role AI can play in education and how its use is being accelerated.' Lord Clement-Jones CBE

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Book SynopsisDisruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines can help you prepare.Artificial intelligence (AI) has impacted many industries around the world—banking and finance, pharmaceuticals, automotive, medical technology, manufacturing, and retail. But it has only just begun its odyssey toward cheaper, better, and faster predictions that drive strategic business decisions. When prediction is taken to the max, industries transform, and with such transformation comes disruption.What is at the root of this? In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it. The rise of AI is shifting prediction from humans to machines, relieving people from this cognitive load while increasing the speed and accuracy of decisions.This sets the stage for a flourishing of new decisions and has profound implications for system-level innovation. Redesigning systems of interdependent decisions takes time—many industries are in the quiet before the storm—but when these new systems emerge, they can be disruptive on a global scale. Decision-making confers power. In industry, power confers profits; in society, power confers control. This process will have winners and losers, and the authors show how businesses can leverage opportunities, as well as protect their positions.Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policymaker on how to make the coming AI disruptions work for you rather than against you.Trade Review"Highly accessible, cleverly written [with] great ideas for practically implementing AI across a system." — Dialogue"A must for anyone with an interest in how the world may look in future." — Institute of Leadership and Management Edge magazineNamed one of the 10 Best Business Books of 2022 by ForbesA Toronto Star Bestseller"This jauntily written and thought-provoking book sketches out how this new economic revolution might unfold." — Financial Times"…a timely and insightful follow up to Prediction Machines." — Engineering and Technology Magazine, The Institution of Engineering and Technology"It's an interesting argument, and the book that Gans and his co-authors have published makes a strong case for developing system-level AI applications in organizations and institutions…" — ForbesAdvance Praise for Power and Prediction:"This is a book that leaders of all types of organizations should read. It explains the enormous size of the AI opportunity and the challenges in getting there." — Dominic Barton, Chair, Rio Tinto; former Global Managing Partner, McKinsey & Company"AI may be to the twenty-first century what electricity was to the twentieth. This is the best book yet that considers what it will mean for all who participate in our economy." — Lawrence H. Summers, Charles W. Eliot University Professor and former president, Harvard University; former secretary, US Treasury; and former chief economist, World Bank"AI will surely displace jobs and disrupt industries in the decades to come. The system-level changes that are on the horizon are excitingly discussed in this book." — Vinod Khosla, founder, Khosla Ventures; cofounder, Sun Microsystems"Power and Prediction is a hugely thought-provoking and inspiring primer on how to shape strategy and design organizations in the age of AI." — Heather Reisman, founder and CEO, Indigo Books and Music"We're told AI will be the most important thing humanity ever works on, yet it feels abstract and niche in its current impact on the world. This book is a must-read for anyone who wants to peek around the corner into AI's future." — Shivon Zilis, Director of Operations and Special Projects, Neuralink; former project director, Tesla"Nobody provides more insight into the fundamental economics of AI and what AI truly enables than Agrawal, Gans, and Goldfarb." — Tiff Macklem, governor, Bank of Canada"Agrawal, Gans, and Goldfarb have done it again! Their new book, Power and Prediction, is destined to become the definitive guide to understanding how and why AI is transforming the economy." — Erik Brynjolfsson, Jerry Yang and Akiko Yamazaki Professor, Stanford University; Director, Stanford Digital Economy Lab; coauthor, The Second Machine Age"Whether we like it or not, artificial intelligence is set to influence every aspect of our lives. How can we make sure that individuals, companies, and organizations benefit from it rather than waste time and resources dealing with unintended consequences? This readable book provides an excellent introduction, emphasizing how AI can improve what we do by providing better predictions and helping reorganize systems." — Daron Acemoglu, Elizabeth and James Killian Professor of Economics, MIT; author, When Nations Fail"Power and Prediction is an important book not only for economists who model the impact of artificial intelligence and entrepreneurs who want to maximize its benefits but also for social scientists and public policy analysts. The authors put prediction problems squarely within the systems and the rules in which they operate to help us understand what will work and why. Along the way, they shine a new light on the importance of systems and rules. A must read for everyone in the public as well as the private sector." — Janice Gross Stein, Professor of Political Science, Munk School, University of Toronto

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Book SynopsisA Fortune magazine journalist draws on his expertise and extensive contacts among the companies and scientists at the forefront of artificial intelligence to offer dramatic predictions of AI’s impact over the next decade, from reshaping our economy and the way we work, learn, and create to unknitting our social fabric, jeopardizing our democracy, and fundamentally altering the way we think.Within the next five years, Jeremy Kahn predicts, AI will disrupt almost every industry and enterprise, with vastly increased efficiency and productivity. It will restructure the workforce, making AI copilots a must for every knowledge worker. It will revamp education, meaning children around the world can have personal, portable tutors. It will revolutionize health care, making individualized, targeted pharmaceuticals more affordable. It will compel us to reimagine how we make art, compose music, and write and publish books. The potential of generative AI to extend our sk

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Book SynopsisTable of ContentsIntroduction: The enterprise capabilities that turn digital and AI into a source of ongoing competitive advantage 1 Section One Creating the Transformation Roadmap: A business-led roadmap is the blueprint for a successful digital and AI transformation 15 Chapter 1 Get your top team inspired and aligned 19 Chapter 2 Choose the right transformation "bite size" 25 Chapter 3 Have business leaders define what's possible 33 Chapter 4 Figure out what resources you need to achieve what you want 43 Chapter 5 Build capabilities for now and the next decade 49 Chapter 6 The digital roadmap is a contract for your C-suite 57 Chapter 7 The ultimate corporate team sport 61 Getting Ready -- Section One 67 Section Two Building Your Talent Bench: Creating an environment where digital talent thrives 69 Chapter 8 Core versus noncore capabilities -- strategic talent planning 71 Chapter 9 The talent team that can build your digital team 83 Chapter 10 Hiring digital talent when they're actually interviewing you 87 Chapter 11 Recognize distinctive technologists 101 Chapter 12 Fostering craftsmanship excellence 107 Getting Ready -- Section Two 115 Section Three Adopting a New Operating Model: Rearchitecting your organization and governance to be fast and flexible 117 Chapter 13 From doing agile to being agile 119 Chapter 14 Operating models that support hundreds of agile pods 131 Chapter 15 Professionalize product management 149 Chapter 16 Customer experience design: The magic ingredient 159 Getting Ready -- Section Three 167 Section Four Technology for Speed and Distributed Innovation: Building a technology environment that empowers the entire organization to digitally innovate 169 Chapter 17 Decoupled architecture for development flexibility and operational scalability 173 Chapter 18 A more surgical and value-backed approach to cloud 185 Chapter 19 Engineering practices for speed and high-quality code 193 Chapter 20 The tools to make your developers highly productive 207 Chapter 21 Delivering production-grade digital solutions 215 Chapter 22 Build in security and automation from the start 221 Chapter 23 MLOps so AI can scale 227 Getting Ready -- Section Four 235 Section Five Embedding Data Everywhere: What it takes to make data easy to consume across the organization 237 Chapter 24 Determine what data matters 239 Chapter 25 Data products: The reusable building blocks for scaling 247 Chapter 26 Data architecture, or the system of data "pipes" 259 Chapter 27 Organize to get the most from your data 273 Getting Ready -- Section Five 285 Section Six The Keys to Unlock Adoption and Scaling: How to both get users to adopt your digital solutions and scale those solutions across the enterprise 287 Chapter 28 Nail user adoption and underlying business model changes 291 Chapter 29 Design solutions for easy replication and reuse 301 Chapter 30 Ensuring impact by tracking what matters 313 Chapter 31 Managing risk and building digital trust 329 Chapter 32 So, what about culture? 335 Getting Ready -- Section Six 345 Section Seven Transformation Journey Stories: An exploration of how three companies have driven successful digital and AI transformations 347 Chapter 33 Freeport-McMoRan turns data into value 349 Chapter 34 DBS -- A multinational bank becomes a digital bank 357 Chapter 35 The future of play takes shape at the LEGO Group 365 Acknowledgments 373 Index 377

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Book Synopsis''I can''t think of a better way to learn about artificial intelligence, and I''ve never had so much fun along the way'' Adam Grant, New York Times bestselling author of Originals and Option B AI is the technology of the future, but how does it actually work? A hilarious, transporting look under the hood of the technology that''s changing the world - and why it''s dumber than we thinkYou Look Like a Thing and I Love You is one of the best pickup lines ever . . . according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She makes silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans - all to understand the technology that governs so much of our human lives. We rely on AI every day for recommendations, for rust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really . . . and how does it solve problems, understand humans, and drive self-driving cars?This hilarious introduction to the most interesting science of our time, shows us how these programs learn, fail, and adapt - and how they reflect both the best and the worst of humanity.Trade ReviewIf you're terrified that artificial intelligence is going to take over the world soon, you clearly haven't asked a computer to write pickup lines, name pets, or do anything else social or creative. Janelle Shane has, and she's the perfect tour guide to explain what machine learning can and can't do - and how it's already affecting your life. I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way -- Adam Grant * New York Times bestselling author of ORIGINALS *If you're worried about what AI is doing to the world, this book may not exactly reassure you, but it will definitely equip you with greater understanding in a highly readable manner. Shane's sense of humor and enthusiasm for her topic shine through. Recommended for anyone who wants to better understand the strengths and limitations of artificial intelligence, but also for anyone who likes watching computers fail hilariously -- Gretchen McCulloch * New York Times bestselling author of Because Internet *Few recent innovations are so revolutionary as machine learning - and none are so poorly understood by the public, pundits, and policy makers. In You Look Like a Thing and I Love You, Janelle Shane delivers a fun, common-sense guide to the technology that's shaping our future -- William Poundstone * author of Are You Smart Enough to Work at Google? *While everyone else is making questionable predictions about the future of AI, Janelle Shane cuts through the fog by telling you how AI actually works. And, even better: she makes it fun! -- Zach Weinersmith, creator of Saturday Morning Breakfast Cereal * New York Times bestselling author of Soonish *Machine learning algorithms are becoming more entrenched in our everyday lives, but they're far from perfect. Janelle Shane takes readers on a light-hearted adventure into the world of machine learning in the wild, examining what these algorithms are really learning - and what they're misunderstanding completely. If you're interested in learning about machine learning and artificial intelligence, trying to understand our robot overlords, or just love weird and interesting science, you can't miss this book -- David Ha, Lead Researcher, Google BrainThis book is scary, not because of how smart AI is, but how weird and too often dysfunctional. If Janelle Shane is a real person, and not some kind of writing robot, she demonstrates the superiority of natural intelligence in the task of making a technical topic irresistibly funny and compelling -- Roy Peter Clark * author of Writing Tools *Janelle Shane's goofy experiments with AI reveal a lot about the future. This book will make you laugh, but you'll also get a crash course in how AI works-and why it's not quite ready to take over the world. A delightful way to learn about the technology that's poised to change our lives -- Annalee Newitz * author of Future of Another Timeline *Janelle Shane has hit the trifecta - the most hilarious, educational, and overall best explainer of artificial intelligence ever written (and drawn) -- Eric Topol * author of Deep Medicine *You Look Like a Thing And I Love You is an incredibly accessible, informative, and (this is equally-important) hilarious look at how the AIs deciding things around us operate. They're not magic, and they're not even that mysterious - but in Janelle Shane's hands, they're hysterical -- Ryan North * New York Times bestselling author of How to Invent Everything *Janelle Shane makes the kind of neural networks that go viral. Her quirky creations autonomously stumble and grumble ... the output of her networks is typically silly and charming in equal measure * Slate *Janelle Shane is quickly becoming the internet's neural network queen * Nerdist *An accessible primer ... illustrated with charming cartoons, oddball case studies (self-driving cars in Australia were confused by kangaroos), and wry observations about the often-hilarious failures of artificial intelligence to comprehend human contexts * Publishers Weekly *Creative and hilarious * New York Post *Janelle Shane of A.I. Weirdness is awesome * BoingBoing *

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