Revealing the unfolding story of Artificial Intelligence, Richard Susskind presents a short non-technical guide that challenges us to think differently about AI. Susskind brings AI out of computing laboratories, big tech companies, and start-ups - and into everyday life.In recent years, and certainly since the launch of ChatGPT, there has been massive public and professional interest in Artificial Intelligence. But people are confused about what AI is, what it can and cannot do, what is yet to come, and whether AI is good or bad for humanity and civilisation - whether it will provide solutions to mankind's major challenges or become our gravest existential threat. There is also confusion about how we should regulate AI and where we should draw moral boundaries on its use. In How To Think About AI, Richard Susskind draws on his experience of working on AI since the early 1980s. For Susskind, balancing the benefits and threats of artificial intelligence is the defining challenge of our age. He explores the history of AI and possible scenarios for its future. His views on AI are not always conventional. He positions ChatGPT and generative AI as no more than the latest chapter in the ongoing story of AI and claims we are still at the foothills of developments. He argues that to think responsibly about the impact of AI requires us to look well beyond todayâs technologies, suggesting that not-yet-invented technologies will have far greater impact on us in the 2030s than the tools we have today. This leads Susskind to discuss the possibility of conscious machines, magnificent new AI-enabled virtual worlds, and the impact of AI on the evolution of biological humans. Read more

None

A New York Times bestsellerSuperintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life. The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time. Read more

Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (AI). The 4th Edition brings readers up to date on the latest technologies,presents concepts in a more unified manner, and offers new or expanded coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI. Read more

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."Everything you really need to know in Machine Learning in a hundred pages. Read more

Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systemsKey FeaturesUnderstand the foundations and advanced techniques of building intelligent, autonomous AI agentsLearn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systemsExplore crucial aspects of trust, safety, and ethics in AI agent development and applicationsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents.Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.What you will learnMaster the core principles of GenAI and agentic systemsUnderstand how AI agents operate, reason, and adapt in dynamic environmentsEnable AI agents to analyze their own actions and improviseImplement systems where AI agents can leverage external tools and plan complex tasksApply methods to enhance transparency, accountability, and reliability in AIExplore real-world implementations of AI agents across industriesWho this book is forThis book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems.Table of ContentsFundamentals of Generative AIPrinciples of Agentic SystemsEssential Components of Intelligent AgentsReflection and Introspection in AgentsEnabling Tool Use and Planning in AgentsExploring the Coordinator, Worker, and Delegator ApproachEffective Agentic System Design TechniquesBuilding Trust in Generative AI SystemsManaging Safety and Ethical ConsiderationsCommon Use Cases and ApplicationsConclusion and Future Outlook Read more

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. Read more

None

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. Read more

None

The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI. Read more

The AI arms race isn't coming—it's already here. Is your organization going to survive?AI is the most transformative technology in history, yet it's creating massive anxiety and frustration for CEOs, business leaders, and consultants everywhere.Does this sound like you? Everyone using artificial intelligence in your organization is self-taught. There's no common understanding of how to execute. No best practices or frameworks. No way to share or scale AI expertise. You know your employees need upskilling, but you don't know where to start.Your team needs to start using AI effectively and efficiently if you're going to remain relevant in the next 12-24 months. Meanwhile, competitors that truly master enterprise AI adoption aren't just surviving—they're thriving by creating new value instead of simply reducing costs.In this step-by-step guide to AI transformation, John Munsell reveals how to:✓ Transform your organization into an AI-first powerhouse - even if most of your team is AI-resistant.✓ Implement powerful, proven AI adoption frameworks that align everyone from the C-suite to frontline workers.✓ Create scalable AI prompts that deliver exceptional results every time - without requiring technical expertise.✓ Build a culture where knowledge sharing accelerates adoption across all departments.What separates AI winners from the rest?Drawing from thousands of hours working with hundreds of businesses, John Munsell reveals the missing link that transforms scattered AI efforts into organization-wide AI acceleration: proven frameworks and processes that anyone can understand, implement, and scale.Currently, 86% of workers feel inadequately trained in AI use, yet 79% of leaders know they need AI to stay competitive.This book offers a step-by-step blueprint to help leaders, managers, and professionals integrate AI seamlessly across all departments, creating a workforce that embraces AI instead of fearing it.Why This Book?For Business Leaders: Develop an AI transformation roadmap that aligns with long-term growth.For Managers & Teams: Learn how to implement AI solutions without disrupting workflows.For Educators & Professionals: Gain practical knowledge to future-proof your career in an AI-driven world.Inside INGRAIN AI, you'll discover:The AI Transformation Roadmap - The fast-track way to establish an AI-first culture by focusing on the four pillars of AI Adoption: Shared Language, Empowering Governance, Scalable Skills, and Collaborative LearningThe AI Strategy Canvas™ - A revolutionary framework that transforms how your entire organization thinks about and implements artificial intelligenceScalable Prompt Engineering™ - A practical methodology that turns prompting into a scalable process for consistently getting excellence out of AIThis isn't just another book about why AI matters. This is an actual blueprint for transforming your organization into one where artificial intelligence isn't just a tool but a fundamental part of how everyone thinks and works.Whether you're a CEO plotting your company's future, a department head striving for efficiency, or a professional trying to gain AI expertise, INGRAIN AI provides the proven tools and frameworks to compress time, protect jobs, and build an organization that thrives in the age of AI.FREE BONUS RESOURCESWhen you purchase this book, you'll get free access to valuable tools and resources, including a complete prompt database containing all prompts taught in this book - allowing you to implement these strategies immediately in your business.The gap between AI leaders and followers is widening daily. Don't get left behind. Read more

When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field.At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique. Read more

Mathematics Handbook for Science and Engineering is a comprehensive handbook for scientists, engineers, teachers and students at universities. The book presents in a lucid and accessible form classical areas of mathematics like algebra, geometry and analysis and also areas of current interest like discrete mathematics, probability, statistics, optimization and numerical analysis. It concentrates on definitions, results, formulas, graphs and tables and emphasizes concepts and methods with applications in technology and science.For the fifth edition the chapter on Optimization has been enlarged and the chapters on Probability Theory and Statstics have been carefully revised. Read more

Learn to research, plan, design, and test the UX of AI-powered productsUnlock the future of design with UX for AI―your indispensable guide to not only surviving but thriving in a world powered by artificial intelligence. Whether you're a seasoned UX designer or a budding design student, this book offers a lifeline for navigating the new normal, ensuring you stay relevant, valuable, and indispensable to your organization. In UX for AI: A Framework for Designing AI-Driven Products, Greg Nudelman―a seasoned UX designer and AI strategist―delivers a battle-tested framework that helps you keep your edge, thrive in your design job, and seize the opportunities AI brings to the table. Drawing on insights from 35 real-world AI projects and acknowledging the hard truth that 85% of AI initiatives fail, this book equips you with the practical skills you need to reverse those odds. You'll gain powerful tools to research, plan, design, and test user experiences that seamlessly integrate human-AI interactions. From practical design techniques to proven user research methods, this is the essential guide for anyone determined to create AI products that not only succeed but set new standards of value and impact. Inside the book:Hands-on exercises: Build your confidence and skills with practice UX design tasks like Digital Twin and Value Matrix, which you can immediately apply to your own AI projects.Common AI patterns and best practices: Explore design strategies for LLMs (Large Language Models), search engines, copilots, and more.Proven user research strategies: Learn how to uncover user needs and behaviors in this brave new world of AI-powered design.Real-world case studies: See how simple, practical UX approaches have prevented multimillion-dollar failures and unlocked unprecedented value.Perfect for any UX designer working with AI-enabled and AI-driven products, UX for AI is also a must-read resource for designers-in-training and design students with an interest in artificial intelligence and contemporary design. Read more

Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. TuringMathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing’s own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41. Read more

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index Read more

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges. Read more

Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs).First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the availableinformation about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems.Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting.Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions. Read more

In a world where technology is rapidly evolving, the fusion of project management and artificial intelligence stands at the forefront of innovation. The AI Revolution in Project Management delves deep into the transformative power of generative AI tools that promise to reshape industries, and revolutionize how we manage projects.Whether you're looking to build dynamic teams using AI, choose a project development approach, or monitor project performance, this book has got you covered. Each chapter provides insightful narratives and includes a supplemental Technical Guide that provides tips on using the AI technology. With case studies and prompts, the dialogues showcase AI in action, from stakeholder engagement to risk management. Dive in with experts who’ve spent countless hours using these AI tools in project scenarios to offer a transparent view into generative AI-driven project management.In this book you'll learn:How to create prompts that generate meaningful and actionable insights tailored for your projectsWhen to use AI to enhance decision-making, super-charge productivity, and elevate overall project efficiencyWhich generative AI models and plug-ins to use for specific project scenarios, ensuring seamless integration and maximum efficiency"AI is not just a buzzword; it’s a tool reshaping how we manage projects and engage with stakeholders."- From the Foreward by Ricardo Viana Vargas, Ph.D.Ricardo is an experienced leader in global operations, project management, business transformation, and crisis management. As founder and managing director of Macrosolutions, a consulting firm with international operations in energy, infrastructure, IT, oil, and finance, he managed more than $20 billion in international projects in the past 25 years.UpdateAs AI products continue to evolve, information published in this book may change. Please note that as of February 2024, there is a name change for Bing Chat and Bard Chat.Microsoft Bing Chat is now Copilot: https://copilot.microsoft.com/.Google Bard is now Gemini: https://gemini.google.com/. Read more