| computer science and ai: The Computer Book Simson L Garfinkel, Rachel H. Grunspan, 2019-01-15 An illustrated journey through 250 milestones in computer science, from the ancient abacus to Boolean algebra, GPS, and social media. With 250 illustrated landmark inventions, publications, and events—encompassing everything from ancient record-keeping devices to the latest computing technologies—The Computer Book takes a chronological journey through the history and future of computer science. Two expert authors, with decades of experience working in computer research and innovation, explore topics including: the Sumerian abacus * the first spam message * Morse code * cryptography * early computers * Isaac Asimov’s laws of robotics * UNIX and early programming languages * movies * video games * mainframes * minis and micros * hacking * virtual reality * and more “What a delight! A fast trip through the computing landscape in the company of friendly tour guides who know the history.” —Harry Lewis, Gordon McKay Professor of Computer Science, Harvard University |
| computer science and ai: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| computer science and ai: Funding a Revolution National Research Council, Computer Science and Telecommunications Board, Committee on Innovations in Computing and Communications: Lessons from History, 1999-02-11 The past 50 years have witnessed a revolution in computing and related communications technologies. The contributions of industry and university researchers to this revolution are manifest; less widely recognized is the major role the federal government played in launching the computing revolution and sustaining its momentum. Funding a Revolution examines the history of computing since World War II to elucidate the federal government's role in funding computing research, supporting the education of computer scientists and engineers, and equipping university research labs. It reviews the economic rationale for government support of research, characterizes federal support for computing research, and summarizes key historical advances in which government-sponsored research played an important role. Funding a Revolution contains a series of case studies in relational databases, the Internet, theoretical computer science, artificial intelligence, and virtual reality that demonstrate the complex interactions among government, universities, and industry that have driven the field. It offers a series of lessons that identify factors contributing to the success of the nation's computing enterprise and the government's role within it. |
| computer science and ai: Introduction to Artificial Intelligence Wolfgang Ertel, 2018-01-18 This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material. |
| computer science and ai: Artificial Unintelligence Meredith Broussard, 2019-01-29 A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone. |
| computer science and ai: Epistemic Logic for AI and Computer Science J.-J. Ch. Meyer, John-Jules Ch Meyer, W. van der Hoek, 2004-03-25 A broad introduction to the subject; many exercises with full solutions are provided. |
| computer science and ai: The Quest for Artificial Intelligence Nils J. Nilsson, 2009-10-30 Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries. |
| computer science and ai: Artificial Intelligence David L. Poole, Alan K. Mackworth, 2017-09-25 Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains. |
| computer science and ai: The Myth of Artificial Intelligence Erik J. Larson, 2021-04-06 “Artificial intelligence has always inspired outlandish visions—that AI is going to destroy us, save us, or at the very least radically transform us. Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.” —John Horgan, author of The End of Science Many futurists insist that AI will soon achieve human levels of intelligence. From there, it will quickly eclipse the most gifted human mind. The Myth of Artificial Intelligence argues that such claims are just that: myths. We are not on the path to developing truly intelligent machines. We don’t even know where that path might be. Erik Larson charts a journey through the landscape of AI, from Alan Turing’s early work to today’s dominant models of machine learning. Since the beginning, AI researchers and enthusiasts have equated the reasoning approaches of AI with those of human intelligence. But this is a profound mistake. Even cutting-edge AI looks nothing like human intelligence. Modern AI is based on inductive reasoning: computers make statistical correlations to determine which answer is likely to be right, allowing software to, say, detect a particular face in an image. But human reasoning is entirely different. Humans do not correlate data sets; we make conjectures sensitive to context—the best guess, given our observations and what we already know about the world. We haven’t a clue how to program this kind of reasoning, known as abduction. Yet it is the heart of common sense. Larson argues that all this AI hype is bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we are to make real progress, we must abandon futuristic talk and learn to better appreciate the only true intelligence we know—our own. |
| computer science and ai: Human-Centered AI Ben Shneiderman, 2022 The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity. |
| computer science and ai: Introduction to Artificial Intelligence Mariusz Flasiński, 2016-08-31 In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader. |
| computer science and ai: Oxford Handbook of Ethics of AI Markus D. Dubber, Frank Pasquale, Sunit Das, 2020-06-30 This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term A.I. is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether human or A.I. |
| computer science and ai: AI and Humanity Illah Reza Nourbakhsh, Jennifer Keating, 2020-03-10 An examination of the implications for society of rapidly advancing artificial intelligence systems, combining a humanities perspective with technical analysis; includes exercises and discussion questions. AI and Humanity provides an analytical framing and a common language for understanding the effects of technological advances in artificial intelligence on society. Coauthored by a computer scientist and a scholar of literature and cultural studies, it is unique in combining a humanities perspective with technical analysis, using the tools of literary explication to examine the societal impact of AI systems. It explores the historical development of these technologies, moving from the apparently benign Roomba to the considerably more sinister semi-autonomous weapon system Harpy. The book is driven by an exploration of the cultural and etymological roots of a series of keywords relevant to both AI and society. Works examined range from Narrative of the Life of Frederick Douglass, given a close reading for its themes of literacy and agency, to Simon Head's critique of the effects of surveillance and automation on the Amazon labor force in Mindless. Originally developed as a textbook for an interdisciplinary humanities-science course at Carnegie Mellon, AI & Humanity offers discussion questions, exercises (including journal writing and concept mapping), and reading lists. A companion website provides updated resources and a portal to a video archive of interviews with AI scientists, sociologists, literary theorists, and others. |
| computer science and ai: Computer Science and Artificial Intelligence National Research Council, Commission on Physical Sciences, Mathematics, and Applications, Naval Studies Board, Panel on Computer Science and Artificial Intelligence, 1997-06-10 The focus of this report is on artificial intelligence (AI) and human-computer interface (HCI) technology. Observations, conclusions, and recommendations regarding AI and HCI are presented in terms of six grand challenge areas which serve to identify key scientific and engineering issues and opportunities. Chapter 1 presents the panel's definitions of these and related terms. Chapter 2 presents the panel's general observations and recommendations regarding AI and HCI. Finally, Chapter 3 discusses computer science, AI, and HCI in terms of the six selected grand challenge areas and three time horizons, that is, short term (within the next 2 years), midterm (2 to 6 years), and long term (more than 6 years from now) and presents additional recommendations in these areas. |
| computer science and ai: AI Margaret A. Boden, 2016-05-19 The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible. |
| computer science and ai: Introduction to Artificial Intelligence Eugene Charniak, Drew V. McDermott, 1985 |
| computer science and ai: Artificial Intelligence Melanie Mitchell, 2019-10-15 Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all. |
| computer science and ai: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| computer science and ai: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| computer science and ai: An Introduction to Communication and Artificial Intelligence David J. Gunkel, 2020-01-07 Communication and artificial intelligence (AI) are closely related. It is communication – particularly interpersonal conversational interaction – that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic. |
| computer science and ai: Artificial Intelligence Margaret A. Boden, 2018-08-13 The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. In this Very Short Introduction , Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable. |
| computer science and ai: A Citizen's Guide to Artificial Intelligence John Zerilli, 2021-02-23 A concise but informative overview of AI ethics and policy. Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring home-owners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, voters in liberal democracies? This book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues including transparency, bias, liability, privacy, and regulation. |
| computer science and ai: Advanced Artificial Intelligence Zhongzhi Shi, 2011-03-04 Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel. |
| computer science and ai: Artificial Intelligence: A New Synthesis Nils J. Nilsson, 1998-04-17 Intelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches. - An evolutionary approach provides a unifying theme - Thorough coverage of important AI ideas, old and new - Frequent use of examples and illustrative diagrams - Extensive coverage of machine learning methods throughout the text - Citations to over 500 references - Comprehensive index |
| computer science and ai: Teaching AI Michelle Zimmerman, 2018-12-15 Get the tools, resources and insights you need to explore artificial intelligence in the classroom and explore what students need to know about living in a world with AI. For many, artificial intelligence, or AI, may seem like science fiction, or inherently overwhelming. The reality is that AI is already being applied in industry and, for many of us, in our daily lives as well. A better understanding of AI can help you make informed decisions in the classroom that will impact the future of your students. Drawing from a broad variety of expert voices from countries including Australia, Japan, and South Africa, as well as educators from around the world and underrepresented student voices, this book explores some of the ways AI can improve education. These include educating learners about AI, teaching them about living in a world where they will be surrounded by AI and helping educators understand how they can use AI to augment human ability. Each chapter offers activities and questions to help you deepen your understanding, try out new concepts and reflect on the information presented. Links to media artifacts from trusted sources will help make your learning experience more dynamic while also providing additional resources to use in your classroom. This book: • Offers a unique approach to the topic, with chapter opening scenes, case studies, and featured student voices. • Discusses a variety of ways to teach students about AI, through design thinking, project-based learning and STEM connections. • Includes lesson ideas, activities and tools for exploring AI with your students. • Includes references to films and other media you can use in class to start discussions on AI or inspire design thinking and STEM projects. In Teaching AI, you’ll learn what AI is, how it works and how to use it to better prepare students in a world with increased human-computer interaction. |
| computer science and ai: Deploying Machine Learning Robbie Allen, 2019-05 Increasingly, business leaders and managers recognize that machine learning offers their companies immense opportunities for competitive advantage. But most discussions of machine learning are intensely technical or academic, and don't offer practical information leaders can use to identify, evaluate, plan, or manage projects. Deploying Machine Learning fills that gap, helping them clarify exactly how machine learning can help them, and collaborate with technologists to actually apply it successfully. You'll learn: What machine learning is, how it compares to big data and artificial intelligence, and why it's suddenly so important What machine learning can do for you: solutions for computer vision, natural language processing, prediction, and more How to use machine learning to solve real business problems -- from reducing costs through improving decision-making and introducing new products Separating hype from reality: identifying pitfalls, limitations, and misconceptions upfront Knowing enough about the technology to work effectively with your technical team Getting the data right: sourcing, collection, governance, security, and culture Solving harder problems: exploring deep learning and other advanced techniques Understanding today's machine learning software and hardware ecosystem Evaluating potential projects, and addressing workforce concerns Staffing your project, acquiring the right tools, and building a workable project plan Interpreting results -- and building an organization that can increasingly learn from data Using machine learning responsibly and ethically Preparing for tomorrow's advances The authors conclude with five chapter-length case studies: image, text, and video analysis, chatbots, and prediction applications. For each, they don't just present results: they also illuminate the process the company undertook, and the pitfalls it overcame along the way. |
| computer science and ai: The Essence of Artificial Intelligence Alison Cawsey, 1998 A concise, practical introduction to artificial intelligence, this title starts with the fundamentals of knowledge representation, inference, expert systems, natural language processing, machine learning, neural networks, agents, robots, and much more. Examples and algorithms are presented throughout, and the book includes a complete glossary. |
| computer science and ai: Artificial Intelligence Methods For Software Engineering Meir Kalech, Rui Abreu, Mark Last, 2021-06-15 Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s) |
| computer science and ai: Real Analysis and Probability R. M. Dudley, 2018-02-01 Written by one of the best-known probabilists in the world this text offers a clear and modern presentation of modern probability theory and an exposition of the interplay between the properties of metric spaces and those of probability measures. This text is the first at this level to include discussions of the subadditive ergodic theorems, metrics for convergence in laws and the Borel isomorphism theory. The proofs for the theorems are consistently brief and clear and each chapter concludes with a set of historical notes and references. This book should be of interest to students taking degree courses in real analysis and/or probability theory. |
| computer science and ai: Common Sense, the Turing Test, and the Quest for Real AI Hector J. Levesque, 2017 What kind of AI? -- The big puzzle -- Knowledge and behavior -- Making it and faking it -- Learning with and without experience -- Book smarts and street smarts -- The long tail and the limits to training -- Symbols and symbol processing -- Knowledge-based systems -- AI technology |
| computer science and ai: Gödel, Escher, Bach Douglas R. Hofstadter, 2000 'What is a self and how can a self come out of inanimate matter?' This is the riddle that drove Douglas Hofstadter to write this extraordinary book. In order to impart his original and personal view on the core mystery of human existence - our intangible sensation of 'I'-ness - Hofstadter defines the playful yet seemingly paradoxical notion of 'strange loop', and explicates this idea using analogies from many disciplines. |
| computer science and ai: On Intelligence Jeff Hawkins, Sandra Blakeslee, 2007-04-01 From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself. Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness. In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways. Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity. |
| computer science and ai: The Master Algorithm Pedro Domingos, 2015-09-22 Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible. |
| computer science and ai: Artificial Intelligence: A Systems Approach M. Tim Jones, 2008-12-26 This book offers students and AI programmers a new perspective on the study of artificial intelligence concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input & reduction as well as data output (i.e., algorithm usage). Because traditional AI concepts such as pattern recognition, numerical optimization and data mining are now simply types of algorithms, a different approach is needed. This “sensor / algorithm / effecter” approach grounds the algorithms with an environment, helps students and AI practitioners to better understand them, and subsequently, how to apply them. The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more. A CD-ROM with simulations, code, and figures accompanies the book. |
| computer science and ai: The Promise of Artificial Intelligence Brian Cantwell Smith, 2019-10-08 An argument that—despite dramatic advances in the field—artificial intelligence is nowhere near developing systems that are genuinely intelligent. In this provocative book, Brian Cantwell Smith argues that artificial intelligence is nowhere near developing systems that are genuinely intelligent. Second wave AI, machine learning, even visions of third-wave AI: none will lead to human-level intelligence and judgment, which have been honed over millennia. Recent advances in AI may be of epochal significance, but human intelligence is of a different order than even the most powerful calculative ability enabled by new computational capacities. Smith calls this AI ability “reckoning,” and argues that it does not lead to full human judgment—dispassionate, deliberative thought grounded in ethical commitment and responsible action. Taking judgment as the ultimate goal of intelligence, Smith examines the history of AI from its first-wave origins (“good old-fashioned AI,” or GOFAI) to such celebrated second-wave approaches as machine learning, paying particular attention to recent advances that have led to excitement, anxiety, and debate. He considers each AI technology's underlying assumptions, the conceptions of intelligence targeted at each stage, and the successes achieved so far. Smith unpacks the notion of intelligence itself—what sort humans have, and what sort AI aims at. Smith worries that, impressed by AI's reckoning prowess, we will shift our expectations of human intelligence. What we should do, he argues, is learn to use AI for the reckoning tasks at which it excels while we strengthen our commitment to judgment, ethics, and the world. |
| computer science and ai: The Artist in the Machine Arthur I. Miller, 2019-10-01 An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans. Today's computers are composing music that sounds “more Bach than Bach,” turning photographs into paintings in the style of Van Gogh's Starry Night, and even writing screenplays. But are computers truly creative—or are they merely tools to be used by musicians, artists, and writers? In this book, Arthur I. Miller takes us on a tour of creativity in the age of machines. Miller, an authority on creativity, identifies the key factors essential to the creative process, from “the need for introspection” to “the ability to discover the key problem.” He talks to people on the cutting edge of artificial intelligence, encountering computers that mimic the brain and machines that have defeated champions in chess, Jeopardy!, and Go. In the central part of the book, Miller explores the riches of computer-created art, introducing us to artists and computer scientists who have, among much else, unleashed an artificial neural network to create a nightmarish, multi-eyed dog-cat; taught AI to imagine; developed a robot that paints; created algorithms for poetry; and produced the world's first computer-composed musical, Beyond the Fence, staged by Android Lloyd Webber and friends. But, Miller writes, in order to be truly creative, machines will need to step into the world. He probes the nature of consciousness and speaks to researchers trying to develop emotions and consciousness in computers. Miller argues that computers can already be as creative as humans—and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature. |
| computer science and ai: Programming Collective Intelligence Toby Segaran, 2007-08-16 Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details. -- Dan Russell, Google Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths. -- Tim Wolters, CTO, Collective Intellect |
| computer science and ai: Human Compatible Stuart Jonathan Russell, 2019 A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines. |
| computer science and ai: Artificial Intelligence for Human Computer Interaction: A Modern Approach Yang Li, Otmar Hilliges, 2021-11-04 This edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address. It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation. |
| computer science and ai: Analyzing Future Applications of AI, Sensors, and Robotics in Society Thomas Heinrich Musiolik, Adrian David Cheok, 2020 This book explores the future challenges and hidden potentials of the application of artificial intelligence, sensors, and robotics in society-- |
Computer - Wikipedia
A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can …
Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic computers and their design, constituent …
What is a Computer?
Feb 6, 2025 · What is a Computer? A computer is a programmable device that stores, retrieves, and processes data. The term "computer" was originally given to humans (human computers) …
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Apr 7, 2025 · A computer is an electronic device that processes, stores, and executes instructions to perform tasks. It includes key components such as the CPU (Central Processing Unit), RAM …
Computer Basics: What is a Computer? - GCFGlobal.org
What is a computer? A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. You may already know that you can use a …
What is a Computer? (Definition & Meaning) - Webopedia
Oct 9, 2024 · A computer is a programmable machine that responds to specific instructions and uses hardware and software to perform tasks. Different types of computers, including …
Computer - Simple English Wikipedia, the free encyclopedia
A computer is a machine that uses electronics to input, process, store, and output data. Data is information such as numbers, words, and lists. Input of data means to read information from a …
Laptop & Desktop Computers - Staples
Buy the computer that fits your exact needs. Choose from laptops, desktops PCs, notebooks, and accessories. Invest in a quality computer for work or personal use.
What is Computer? Definition, Characteristics and Classification
Aug 7, 2024 · A computer is an electronic device wherein we need to input raw data to be processed with a set of programs to produce a desirable output. Computers have the ability to …
Computer - Wikipedia
A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can perform …
Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic computers and their design, constituent parts, …
What is a Computer?
Feb 6, 2025 · What is a Computer? A computer is a programmable device that stores, retrieves, and processes data. The term "computer" was originally given to humans (human computers) who …
Micro Center - Computer & Electronics Retailer - Shop Now
Shop Micro Center for electronics, PCs, laptops, Apple products, and much more. Enjoy in-store pickup, top deals, and expert same-day tech support.
What is a Computer? - GeeksforGeeks
Apr 7, 2025 · A computer is an electronic device that processes, stores, and executes instructions to perform tasks. It includes key components such as the CPU (Central Processing Unit), RAM …
Computer Basics: What is a Computer? - GCFGlobal.org
What is a computer? A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. You may already know that you can use a …
What is a Computer? (Definition & Meaning) - Webopedia
Oct 9, 2024 · A computer is a programmable machine that responds to specific instructions and uses hardware and software to perform tasks. Different types of computers, including desktops, …
Computer - Simple English Wikipedia, the free encyclopedia
A computer is a machine that uses electronics to input, process, store, and output data. Data is information such as numbers, words, and lists. Input of data means to read information from a …
Laptop & Desktop Computers - Staples
Buy the computer that fits your exact needs. Choose from laptops, desktops PCs, notebooks, and accessories. Invest in a quality computer for work or personal use.
What is Computer? Definition, Characteristics and Classification
Aug 7, 2024 · A computer is an electronic device wherein we need to input raw data to be processed with a set of programs to produce a desirable output. Computers have the ability to …
