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| artificial intelligence history timeline: Artificial Intelligence Basics Tom Taulli, 2019-08-01 Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success. Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life. Google, Amazon, Facebook, and similar tech giants are far from the only organizations on which artificial intelligence has had—and will continue to have—an incredibly significant result. AI is the present and the future of your business as well as your home life. Strengthening your prowess on the subject will prove invaluable to your preparation for the future of tech, and Artificial Intelligence Basics is the indispensable guide that you’ve been seeking. What You Will Learn Study the core principles for AI approaches such as machine learning, deep learning, and NLP (Natural Language Processing)Discover the best practices to successfully implement AI by examining case studies including Uber, Facebook, Waymo, UiPath, and Stitch FixUnderstand how AI capabilities for robots can improve businessDeploy chatbots and Robotic Processing Automation (RPA) to save costs and improve customer serviceAvoid costly gotchasRecognize ethical concerns and other risk factors of using artificial intelligenceExamine the secular trends and how they may impact your business Who This Book Is For Readers without a technical background, such as managers, looking to understand AI to evaluate solutions. |
| artificial intelligence history timeline: Machines Who Think Pamela McCorduck, Cli Cfe, 2004-03-17 This book is a history of artificial intelligence, that audacious effort to duplicate in an artifact what we consider to be our most important property—our intelligence. It is an invitation for anybody with an interest in the future of the human race to participate in the inquiry. |
| artificial intelligence history timeline: The Cambridge Handbook of Artificial Intelligence Keith Frankish, William M. Ramsey, 2014-06-12 An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists. |
| artificial intelligence history timeline: The Singularity Is Near Ray Kurzweil, 2005-09-22 NEW YORK TIMES BESTSELLER • Celebrated futurist Ray Kurzweil, hailed by Bill Gates as “the best person I know at predicting the future of artificial intelligence,” presents an “elaborate, smart, and persuasive” (The Boston Globe) view of the future course of human development. “Artfully envisions a breathtakingly better world.”—Los Angeles Times “Startling in scope and bravado.”—Janet Maslin, The New York Times “An important book.”—The Philadelphia Inquirer At the onset of the twenty-first century, humanity stands on the verge of the most transforming and thrilling period in its history. It will be an era in which the very nature of what it means to be human will be both enriched and challenged as our species breaks the shackles of its genetic legacy and achieves inconceivable heights of intelligence, material progress, and longevity. While the social and philosophical ramifications of these changes will be profound, and the threats they pose considerable, The Singularity Is Near presents a radical and optimistic view of the coming age that is both a dramatic culmination of centuries of technological ingenuity and a genuinely inspiring vision of our ultimate destiny. |
| artificial intelligence history timeline: Artificial Intelligence for Big Data Anand Deshpande, Manish Kumar, 2018-05-22 Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. |
| artificial intelligence history timeline: Artificial Intelligence David Jefferis, 1999 Artificial Intelligence opens up the fantastic world of cutting edge robot technology to young readers from their appearance in early science fiction to their use today in communication, finance, entertainment, and the environment. The ethical pros and cons of technological advancement are considered and a helpful glossary explains scientific terms and concepts. |
| artificial intelligence history timeline: Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt, Antonello Pasini, Caren Marzban, 2008-11-28 How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods. |
| artificial intelligence history timeline: Leviathan Thomas Hobbes, 2012-10-03 Written during a moment in English history when the political and social structures were in flux and open to interpretation, Leviathan played an essential role in the development of the modern world. |
| artificial intelligence history timeline: 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. |
| artificial intelligence history timeline: Artificial Intelligence Safety and Security Roman V. Yampolskiy, 2018-07-27 The history of robotics and artificial intelligence in many ways is also the history of humanity’s attempts to control such technologies. From the Golem of Prague to the military robots of modernity, the debate continues as to what degree of independence such entities should have and how to make sure that they do not turn on us, its inventors. Numerous recent advancements in all aspects of research, development and deployment of intelligent systems are well publicized but safety and security issues related to AI are rarely addressed. This book is proposed to mitigate this fundamental problem. It is comprised of chapters from leading AI Safety researchers addressing different aspects of the AI control problem as it relates to the development of safe and secure artificial intelligence. The book is the first edited volume dedicated to addressing challenges of constructing safe and secure advanced machine intelligence. The chapters vary in length and technical content from broad interest opinion essays to highly formalized algorithmic approaches to specific problems. All chapters are self-contained and could be read in any order or skipped without a loss of comprehension. |
| artificial intelligence history timeline: 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. |
| artificial intelligence history timeline: Robot Hans P. Moravec, 1999 In this compelling book, Hans Moravec predicts that machines will attain human levels of intelligence by the year 2040, and that by 2050, they will surpass us. But even though Moravec predicts the end of the domination by human beings, his is not a bleak vision. Far from railing against a future in which machines rule the world, Moravec embraces it, taking the startling view that intelligent robots will actually be our evolutionary heirs. Intelligent machines, which will grow from us, learn our skills, and share our goals and values, can be viewed as children of our minds. And since they are our children, we will want them to outdistance us. In fact, in a bid for immortality, many of our descendants will choose to transform into ex humans, as they upload themselves into advanced computers. This provocative new book, the highly anticipated follow-up to his bestselling volume Mind Children, charts the trajectory of robotics in breathtaking detail. A must read for artificial intelligence, technology, and computer enthusiasts, Moravec's freewheeling but informed speculations present a future far different than we ever dared imagine. |
| artificial intelligence history timeline: 50 Years of Artificial Intelligence Max Lungarella, 2007-12-10 This Festschrift volume, published in celebration of the 50th Anniversary of Artificial Intelligence, includes 34 refereed papers written by leading researchers in the field of Artificial Intelligence. The papers were carefully selected from the invited lectures given at the 50th Anniversary Summit of AI, held at the Centro Stefano Franscini, Monte Verità, Ascona, Switzerland, July 9-14, 2006. The summit provided a venue for discussions on a broad range of topics. |
| artificial intelligence history timeline: Artificial Intelligence in Medicine David Riaño, Szymon Wilk, Annette ten Teije, 2019-06-19 This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning. |
| artificial intelligence history timeline: 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 |
| artificial intelligence history timeline: AI 2041 Kai-Fu Lee, Chen Qiufan, 2024-03-05 How will AI change our world within twenty years? A pioneering technologist and acclaimed writer team up for a “dazzling” (The New York Times) look at the future that “brims with intriguing insights” (Financial Times). This edition includes a new foreword by Kai-Fu Lee. A BEST BOOK OF THE YEAR: The Wall Street Journal, The Washington Post, Financial Times Long before the advent of ChatGPT, Kai-Fu Lee and Chen Qiufan understood the enormous potential of artificial intelligence to transform our daily lives. But even as the world wakes up to the power of AI, many of us still fail to grasp the big picture. Chatbots and large language models are only the beginning. In this “inspired collaboration” (The Wall Street Journal), Lee and Chen join forces to imagine our world in 2041 and how it will be shaped by AI. In ten gripping, globe-spanning short stories and accompanying commentary, their book introduces readers to an array of eye-opening settings and characters grappling with the new abundance and potential harms of AI technologies like deep learning, mixed reality, robotics, artificial general intelligence, and autonomous weapons. |
| artificial intelligence history timeline: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| artificial intelligence history timeline: Machines Like Me Ian McEwan, 2019-04-23 From the Booker Prize winner and bestselling author of Atonement—”a sharply intelligent novel of ideas” (The New York Times) that asks whether a machine can understand the human heart, or whether we are the ones who lack understanding. Set in an uncanny alternative 1982 London—where Britain has lost the Falklands War, Margaret Thatcher battles Tony Benn for power, and Alan Turing achieves a breakthrough in artificial intelligence—Machines Like Me powerfully portrays two lovers who will be tested beyond their understanding. Charlie, drifting through life and dodging full-time employment, is in love with Miranda, a bright student who lives with a terrible secret. When Charlie comes into money, he buys Adam, one of the first generation of synthetic humans. With Miranda's assistance, he codesigns Adam's personality. The near-perfect human that emerges is beautiful, strong, and smart—and a love triangle soon forms. Ian McEwan's subversive, gripping novel poses fundamental questions: What makes us human—our outward deeds or our inner lives? Could a machine understand the human heart? This provocative and thrilling tale warns against the power to invent things beyond our control. Don’t miss Ian McEwan’s new novel, Lessons, coming in September! |
| artificial intelligence history timeline: For a meaningful artificial intelligence Cédric Villani, Yann Bonnet, marc schoenauer, charly berthet, francois levin, anne charlotte cornut, Bertrand Rondepierre, 2018-03-28 |
| artificial intelligence history timeline: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
| artificial intelligence history timeline: Thinking Machines Luke Dormehl, 2017-03-07 A fascinating look at Artificial Intelligence, from its humble Cold War beginnings to the dazzling future that is just around the corner. When most of us think about Artificial Intelligence, our minds go straight to cyborgs, robots, and sci-fi thrillers where machines take over the world. But the truth is that Artificial Intelligence is already among us. It exists in our smartphones, fitness trackers, and refrigerators that tell us when the milk will expire. In some ways, the future people dreamed of at the World's Fair in the 1960s is already here. We're teaching our machines how to think like humans, and they're learning at an incredible rate. In Thinking Machines, technology journalist Luke Dormehl takes you through the history of AI and how it makes up the foundations of the machines that think for us today. Furthermore, Dormehl speculates on the incredible--and possibly terrifying--future that's much closer than many would imagine. This remarkable book will invite you to marvel at what now seems commonplace and to dream about a future in which the scope of humanity may need to broaden itself to include intelligent machines. |
| artificial intelligence history timeline: The Future Computed , 2018 |
| artificial intelligence history timeline: 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. |
| artificial intelligence history timeline: 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. |
| artificial intelligence history timeline: 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. |
| artificial intelligence history timeline: 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. |
| artificial intelligence history timeline: Neural Networks and Analog Computation Hava T. Siegelmann, 2012-12-06 The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students. |
| artificial intelligence history timeline: The Computer and the Brain John Von Neumann, 2000-01-01 This book represents the views of one of the greatest mathematicians of the twentieth century on the analogies between computing machines and the living human brain. John von Neumann concludes that the brain operates in part digitally, in part analogically, but uses a peculiar statistical language unlike that employed in the operation of man-made computers. This edition includes a new foreword by two eminent figures in the fields of philosophy, neuroscience, and consciousness. |
| artificial intelligence history timeline: Pattern Classification Richard O. Duda, Peter E. Hart, David G. Stork, 2012-11-09 The first edition, published in 1973, has become a classicreference in the field. Now with the second edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment. |
| artificial intelligence history timeline: The Perceptron Frank Rosenblatt, 1958 |
| artificial intelligence history timeline: The Last Duel Eric Jager, 2005-09-13 NEW YORK TIMES BESTSELLER • NOW A MAJOR MOTION PICTURE • “A taut page-turner with all the hallmarks of a good historical thriller.”—Orlando Sentinel The gripping true story of the duel to end all duels in medieval France as a resolute knight defends his wife’s honor against the man she accuses of a heinous crime In the midst of the devastating Hundred Years’ War between France and England, Jean de Carrouges, a Norman knight fresh from combat in Scotland, returns home to yet another deadly threat. His wife, Marguerite, has accused squire Jacques Le Gris of rape. A deadlocked court decrees a trial by combat between the two men that will also leave Marguerite’s fate in the balance. For if her husband loses the duel, she will be put to death as a false accuser. While enemy troops pillage the land, and rebellion and plague threaten the lives of all, Carrouges and Le Gris meet in full armor on a walled field in Paris. What follows is the final duel ever authorized by the Parlement of Paris, a fierce fight with lance, sword, and dagger before a massive crowd that includes the teenage King Charles VI, during which both combatants are wounded—but only one fatally. Based on extensive research in Normandy and Paris, The Last Duel brings to life a colorful, turbulent age and three unforgettable characters caught in a fatal triangle of crime, scandal, and revenge. The Last Duel is at once a moving human drama, a captivating true crime story, and an engrossing work of historical intrigue with themes that echo powerfully centuries later. |
| artificial intelligence history timeline: I Have No Mouth & I Must Scream Harlan Ellison, 2014-04-29 Seven stunning stories of speculative fiction by the author of A Boy and His Dog. In a post-apocalyptic world, four men and one woman are all that remain of the human race, brought to near extinction by an artificial intelligence. Programmed to wage war on behalf of its creators, the AI became self-aware and turned against humanity. The five survivors are prisoners, kept alive and subjected to brutal torture by the hateful and sadistic machine in an endless cycle of violence. This story and six more groundbreaking and inventive tales that probe the depths of mortal experience prove why Grand Master of Science Fiction Harlan Ellison has earned the many accolades to his credit and remains one of the most original voices in American literature. I Have No Mouth and I Must Scream also includes “Big Sam Was My Friend,” “Eyes of Dust,” “World of the Myth,” “Lonelyache,” Hugo Award finalist “Delusion for a Dragon Slayer,” and Hugo and Nebula Award finalist “Pretty Maggie Moneyeyes.” |
| artificial intelligence history timeline: R. U. R. Karel Capek, 1923 |
| artificial intelligence history timeline: Gods and Robots Adrienne Mayor, 2020-04-21 Traces the story of how ancient cultures envisioned artificial life, automata, self-moving devices and human enhancements, sharing insights into how the mythologies of the past related to and shaped ancient machine innovations. |
| artificial intelligence history timeline: Investigating Explanation-Based Learning Gerald DeJong, 2012-12-06 Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism. |
| artificial intelligence history timeline: Beyond AI J. Storrs Hall, Ph.D, 2009-09-25 With a 30-year career in artificial intelligence (AI) and computer science, Hall reviews the history of AI, predicting the probable achievements in the near future and provides an intriguing glimpse into the astonishing possibilities and dilemmas on the horizon. |
| artificial intelligence history timeline: Outsmarting IQ David Perkins, 1995-03-01 Since the turn of the century, the idea that intellectual capacity is fixed has been generally accepted. But increasingly, psychologists, educators, and others have come to challenge this premise. Outsmarting IQ reveals how earlier discoveries about IQ, together with recent research, show that intelligence is not genetically fixed. Intelligence can be taught. David Perkins, renowned for his research on thinking, learning, and education, identifies three distinct kinds of intelligence: the fixed neurological intelligence linked to IQ tests; the specialized knowledge and experience that individuals acquire over time; and reflective intelligence, the ability to become aware of one's mental habits and transcend limited patterns of thinking. Although all of these forms of intelligence function simultaneously, it is reflective intelligence, Perkins shows, that affords the best opportunity to amplify human intellect. This is the kind of intelligence that helps us to make wise personal decisions, solve challenging technical problems, find creative ideas, and learn complex topics in mathematics, the sciences, management, and other areas. It is the kind of intelligence most needed in an increasingly competitive and complicated world. Using his own pathbreaking research at Harvard and a rich array of other sources, Perkins paints a compelling picture of the skills and attitudes underlying learnable intelligence. He identifies typical pitfalls in multiple perspectives, and neglecting evidence. He reveals the underlying mechanisms of intelligent behavior. And he explores new frontiers in the development of intelligence in education, business, and other settings. This book will be of interest to people who have a personal or professional stake in increasing their intellectual skills, to those who look toward better education and a more thoughtful society, and not least to those who follow today's heated debates about the nature of intelligence. |
| artificial intelligence history timeline: The Age of Intelligent Machines Ray Kurzweil, 1992 Comparing the human brain with so-called artificial intelligence, the author probes past, present, and future attempts to create machine intelligence |
| artificial intelligence history timeline: Impact of Artificial Intelligence on Organizational Transformation S. Balamurugan, Sonal Pathak, Anupriya Jain, Sachin Gupta, Sachin Sharma, Sonia Duggal, 2022-01-20 IMPACT OF ARTIFICIAL INTELLIGENCE ON ORGANIZATIONAL TRANSFORMATION Discusses the impact of AI on organizational transformation which is a mix of computational techniques and management practices, with in-depth analysis about the role of automation & data management, and strategic management in relation to human capital, procurement & production, finance, and marketing. The impact of AI in restructuring organizational processes is a combination of management practices and computational technology. This book covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms. Audience Readers include those working in artificial intelligence, business management studies, technology engineers, senior executives, and human resource managers in all types of business. |
| artificial intelligence history timeline: A Biologist’s Guide to Artificial Intelligence Ambreen Hamadani, Nazir A Ganai, Hamadani Henna, J Bashir, 2024-03-15 A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning Equips with new data mining strategies an easy interface into the world of Artificial Intelligence Enables researchers to enhance their own sphere of researching Artificial Intelligence |
The brief history of artificial intelligence: the world has changed ...
Dec 6, 2022 · Since the early days of this history, some computer scientists have strived to make machines as intelligent as humans. The next timeline shows some of the notable artificial …
AI timelines: What do experts in artificial intelligence expect for the ...
Feb 7, 2023 · AI timelines: What do experts in artificial intelligence expect for the future? Many AI experts believe there is a real chance that human-level artificial intelligence will be developed …
Artificial Intelligence - Our World in Data
The brief history of artificial intelligence: The world has changed fast – what might be next? Despite their brief history, computers and AI have fundamentally changed what we see, what …
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Feb 22, 2023 · The big visualization offers a long-term perspective on the history of technology. 1 The timeline begins at the center of the spiral. The first use of stone tools, 3.4 million years …
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Mar 29, 2023 · Artificial intelligence (AI) technology has steadily become more powerful over the course of the last decades, and in recent years, it has entered our world in many different …
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Apr 18, 2025 · “Data Page: Number of articles - All”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial …
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May 31, 2025 · Domain of notable artificial intelligence systems, by year of publication Describes the specific area, application, or field in which an AI system is designed to operate. An AI …
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Dec 15, 2022 · What is at stake as artificial intelligence becomes more powerful? All major technological innovations lead to a range of positive and negative consequences. For AI, the …
Annual global corporate investment in artificial intelligence, by type
“Data Page: Annual global corporate investment in artificial intelligence, by type”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser …
Max Roser - Our World in Data
The brief history of artificial intelligence: the world has changed fast — what might be next? Despite their brief history, computers and AI have fundamentally changed what we see, what …
The brief history of artificial intelligence: the world has changed ...
Dec 6, 2022 · Since the early days of this history, some computer scientists have strived to make machines as intelligent as humans. The next timeline shows some of the notable artificial …
AI timelines: What do experts in artificial intelligence expect for …
Feb 7, 2023 · AI timelines: What do experts in artificial intelligence expect for the future? Many AI experts believe there is a real chance that human-level artificial intelligence will be developed …
Artificial Intelligence - Our World in Data
The brief history of artificial intelligence: The world has changed fast – what might be next? Despite their brief history, computers and AI have fundamentally changed what we see, what …
The long-run perspective on technological change - Our World in …
Feb 22, 2023 · The big visualization offers a long-term perspective on the history of technology. 1 The timeline begins at the center of the spiral. The first use of stone tools, 3.4 million years …
Artificial intelligence has advanced despite having few resources ...
Mar 29, 2023 · Artificial intelligence (AI) technology has steadily become more powerful over the course of the last decades, and in recent years, it has entered our world in many different …
Annual scholarly publications on artificial intelligence
Apr 18, 2025 · “Data Page: Number of articles - All”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial …
Domain of notable artificial intelligence systems, by year of ...
May 31, 2025 · Domain of notable artificial intelligence systems, by year of publication Describes the specific area, application, or field in which an AI system is designed to operate. An AI …
Artificial intelligence is transforming our world — it is on all of us ...
Dec 15, 2022 · What is at stake as artificial intelligence becomes more powerful? All major technological innovations lead to a range of positive and negative consequences. For AI, the …
Annual global corporate investment in artificial intelligence, by type
“Data Page: Annual global corporate investment in artificial intelligence, by type”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser …
Max Roser - Our World in Data
The brief history of artificial intelligence: the world has changed fast — what might be next? Despite their brief history, computers and AI have fundamentally changed what we see, what …
