Modeling of individual beliefs is essential to the computer understanding of natural languages. Phenomena at all levels -- syntactic, semantic, and pragmatic -- cannot be fully analyzed in the absence of models of a hearer and of the hearer's model of other believers. The heart of this text is the presentation of an artificial intelligence (AI) program intended to simulate certain aspects of a human believer. This book provides a prolog program, Viewgen, that maintains belief structures about the world and other believers, and is able to ascribe beliefs to others without direct evidence by using a form of default reasoning. The authors contend that a plausible model such as this can -- in the best cognitive science tradition -- shed light on the long-standing philosophical problem of what belief is. The issues presented here will be of considerable interest to an informed general reader as well as those with a background in any of the disciplines that make up what is now called cognitive science: philosophy, linguistics, psychology, neuropsychology, and also AI itself.

AN INTRIGUING AND BEAUTIFUL ILLUSTRATED EDITION OF YORICK WILKS' INSIDERS GUIDE TO AI The benefits and potential nightmares of artificial intelligence have long inspired science-fiction writers, but in a world where most of us carry a portable device that knows infinitely more than we do, these are increasingly real concerns. Artificial Intelligence is the essential companion to this unsettling science. It surveys the history, philosophies, and current state of AI, and asks key questions such as how should robots think, and can machines really learn? With subjects ranging from the World Wide Web to artificial companions, the book considers such ethical issues as automated warfare, internet surveillance, and fake news, speculating on just how far robots will infiltrate our future. This illustrated edition combines compelling photographs with 'circuit-board' infographics to lift the curtain on the hidden world of AI.

Modeling of individual beliefs is essential to the computer understanding of natural languages. Phenomena at all levels -- syntactic, semantic, and pragmatic -- cannot be fully analyzed in the absence of models of a hearer and of the hearer's model of other believers. The heart of this text is the presentation of an artificial intelligence (AI) program intended to simulate certain aspects of a human believer. This book provides a prolog program, Viewgen, that maintains belief structures about the world and other believers, and is able to ascribe beliefs to others without direct evidence by using a form of default reasoning. The authors contend that a plausible model such as this can -- in the best cognitive science tradition -- shed light on the long-standing philosophical problem of what belief is. The issues presented here will be of considerable interest to an informed general reader as well as those with a background in any of the disciplines that make up what is now called cognitive science: philosophy, linguistics, psychology, neuropsychology, and also AI itself.

Accompanying continued industrial production and sales of artificial intelligence and expert systems is the risk that difficult and resistant theoretical problems and issues will be ignored. The participants at the Third Tinlap Workshop, whose contributions are contained in Theoretical Issues in Natural Language Processing, remove that risk. They discuss and promote theoretical research on natural language processing, examinations of solutions to current problems, development of new theories, and representations of published literature on the subject. Discussions among these theoreticians in artificial intelligence, logic, psychology, philosophy, and linguistics draw a comprehensive, up-to-date picture of the natural language processing field.

Accompanying continued industrial production and sales of artificial intelligence and expert systems is the risk that difficult and resistant theoretical problems and issues will be ignored. The participants at the Third Tinlap Workshop, whose contributions are contained in Theoretical Issues in Natural Language Processing, remove that risk. They discuss and promote theoretical research on natural language processing, examinations of solutions to current problems, development of new theories, and representations of published literature on the subject. Discussions among these theoreticians in artificial intelligence, logic, psychology, philosophy, and linguistics draw a comprehensive, up-to-date picture of the natural language processing field.

Machine Conversationsis a collection of some of the best research available in the practical arts of machine conversation. The book describes various attempts to create practical and flexible machine conversation - ways of talking to computers in an unrestricted version of English or some other language. While this book employs and advances the theory of dialogue and its linguistic underpinnings, the emphasis is on practice, both in university research laboratories and in company research and development. Since the focus is on the task and on the performance, this book provides some of the first-rate work taking place in industry, quite apart from the academic tradition. It also reveals striking and relevant facts about the tone of machine conversations and closely evaluates what users require. Machine Conversations is an excellent reference for researchers interested in computational linguistics, cognitive science, natural language processing, artificial intelligence, human computer interfaces and machine learning.

A history of machine translation (MT) from the point of view of a major writer and innovator in the field is the subject of this book. It details the deep differences between rival groups on how best to do MT, and presents a global perspective covering historical and contemporary systems in Europe, the US and Japan. The author considers MT as a fundamental part of Artificial Intelligence and the ultimate test-bed for all computational linguistics.

Machine Translation (MT) is both an engineering technology and a measure of all things to do with languages and computersa "whenever a new theory of language or linguistics is offered, an important criteria for its success is whether or not it will improve machine translation.

This book presents a history of machine translation (MT) from the point of view of a major writer and innovator in the subject. It describes and contrasts a range of approaches to the challenges and problems of this remarkable technology by means of a combination of historic papers along with commentaries to update their significance, both at the time of their writing and now. This volume chronicles the evolution of conflicting approaches to MT towards a somewhat skeptical consensus on future progress. Also included is a discussion of the most recent developments in the field and prospects for the future, which have been much changed by the arrival of the World Wide Web.

Machine Conversationsis a collection of some of the best research available in the practical arts of machine conversation. The book describes various attempts to create practical and flexible machine conversation - ways of talking to computers in an unrestricted version of English or some other language. While this book employs and advances the theory of dialogue and its linguistic underpinnings, the emphasis is on practice, both in university research laboratories and in company research and development. Since the focus is on the task and on the performance, this book provides some of the first-rate work taking place in industry, quite apart from the academic tradition. It also reveals striking and relevant facts about the tone of machine conversations and closely evaluates what users require. Machine Conversations is an excellent reference for researchers interested in computational linguistics, cognitive science, natural language processing, artificial intelligence, human computer interfaces and machine learning.

Artificial intelligence has long been a mainstay of science fiction and increasingly it feels as if AI is entering our everyday lives, with technology like Apple’s Siri now prominent, and self-driving cars almost upon us.

But what do we actually mean when we talk about ‘AI’? Are the sentient machines of 2001 or The Matrix a real possibility or will real-world artificial intelligence look and feel very different? What has it done for us so far? And what technologies could it yield in the future?

AI expert Yorick Wilks takes a journey through the history of artificial intelligence up to the present day, examining its origins, controversies and achievements, as well as looking into just how it works. He also considers the future, assessing whether these technologies could menace our way of life, but also how we are all likely to benefit from AI applications in the years to come.

Entertaining, enlightening, and keenly argued, this is the essential one-stop guide to the AI debate.

Natural Language Processing as a Foundation of the Semantic Web argues that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web, in several different ways, and whether its advocates realise this or not. Chiefly, it argues, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels based on lower level empirical computations over usage. The claim being made is definitely not logic-bad, NLP-good in any simple-minded way, but that the SW will be a fascinating interaction of these two methodologies, like the WWW (which, as the authors explain, has been a fruitful field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite resource description framework (RDF) knowledge stores for the SW from existing WWW (unstructured) text databases, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. It is also assumed here that, whatever the limitations on current SW representational power drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable. Natural Language Processing as a Foundation of the Semantic Web will appeal to researchers, practitioners and anyone with an interest in NLP, the philosophy of language, cognitive science, the Semantic Web and Web Science generally, as well as providing a magisterial and controversial overview of the history of artificial intelligence

Margaret Masterman was a pioneer in the field of computational linguistics. Working in the earliest days of language processing by computer, she believed that meaning, not grammar, was the key to understanding languages, and that machines could determine the meaning of sentences. She was able, even on simple machines, to undertake sophisticated experiments in machine translation, and carried out important work on the use of semantic codings and thesauri to determine the meaning structure of texts. This volume brings together Masterman's groundbreaking papers for the first time. Through his insightful commentaries, Yorick Wilks argues that Masterman came close to developing a computational theory of language meaning based on the ideas of Wittgenstein, and shows the importance of her work in the philosophy of science and the nature of iconic languages. Of key interest in computational linguistics and artificial intelligence, it will remind scholars of Masterman's significant contribution to the field.

This outstanding collection is designed to address the fundamental issues and principles underlying the task of Artificial Intelligence. The editors have selected not only papers now recognized as classics but also many specially commissioned papers which examine the methodological and theoretical foundations of the discipline from a wide variety of perspectives: computer science and software engineering, cognitive psychology, philosophy, formal logic and linguistics. Carefully planned and structured, the volume tackles many of the contentious questions of immediate concern to AI researchers and interested observers. Is Artificial Intelligence in fact a discipline, or is it simply part of computer science? What is the role of programs in AI and how do they relate to theories? What is the nature of representation and implementation, and how should the challenge of connectionism be viewed? Can AI be characterized as an empirical science? The comprehensiveness of this collection is further enhanced by the full, annotated bibliography. All readers who want to consider what Artificial Intelligence really is will find this sourcebook invaluable, and the editors will undoubtedly succeed in their secondary aim of stimulating a lively and continuing debate.

Margaret Masterman was a pioneer in the field of computational linguistics. Working in the earliest days of language processing by computer, she believed that meaning, not grammar, was the key to understanding languages, and that machines could determine the meaning of sentences. She was able, even on simple machines, to undertake sophisticated experiments in machine translation, and carried out important work on the use of semantic codings and thesauri to determine the meaning structure of texts. This volume brings together Masterman's groundbreaking papers for the first time. Through his insightful commentaries, Yorick Wilks argues that Masterman came close to developing a computational theory of language meaning based on the ideas of Wittgenstein, and shows the importance of her work in the philosophy of science and the nature of iconic languages. Of key interest in computational linguistics and artificial intelligence, it will remind scholars of Masterman's significant contribution to the field.