Shalom Lappin

After many years during which it languished in relative obscurity, in remote classrooms of computer science departments and in small prototype projects for tech companies, artificial intelligence (AI) is now a searingly hot topic across the media. Yet much of the public discussion is so feverish that an understanding of the basic scientific and engineering elements of the field is easily lost, often resulting in exaggerated claims, as well as dangerously neglected threats. This concise and sober book presents a brief history of AI, explaining in clear language the central engineering innovations that have produced the current revolution. It distinguishes between imagined dangers and the very real problems that AI is creating. Spread across seven short and accessible chapters, this book explains the developments behind deep learning and the applications of deep neural networks (DNNs). It addresses both the imagined and actual risks posed by the AI revolution, before outlining the elements of a rational public policy on AI, covering topics like tech monopolies, disinformation, bias, hate speech, intellectual property rights, and inequality. Suitable for the general reader, Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia is the ideal book for anyone seeking a clear and informed introduction to AI.

After many years during which it languished in relative obscurity, in remote classrooms of computer science departments and in small prototype projects for tech companies, artificial intelligence (AI) is now a searingly hot topic across the media. Yet much of the public discussion is so feverish that an understanding of the basic scientific and engineering elements of the field is easily lost, often resulting in exaggerated claims, as well as dangerously neglected threats. This concise and sober book presents a brief history of AI, explaining in clear language the central engineering innovations that have produced the current revolution. It distinguishes between imagined dangers and the very real problems that AI is creating. Spread across seven short and accessible chapters, this book explains the developments behind deep learning and the applications of deep neural networks (DNNs). It addresses both the imagined and actual risks posed by the AI revolution, before outlining the elements of a rational public policy on AI, covering topics like tech monopolies, disinformation, bias, hate speech, intellectual property rights, and inequality. Suitable for the general reader, Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia is the ideal book for anyone seeking a clear and informed introduction to AI.

Generations raised after the Second World War took for granted a world of stability and prosperity, and with it the waning of ancient hatreds. Recent decades have been more sobering. Instability and extremism have returned in force. As Shalom Lappin explains in this worrying book, an upsurge of antisemitism across the political spectrum has accompanied them. Recent events in the Middle East have transformed it into a tidal wave. Lappin explores in particular the disturbing correlation between the expansion of economic globalization and the return of the anti-Jewish ideas that we thought had been consigned to the past. He examines this relationship within the context of the assault on democracy and social cohesion that anti-globalist reactions have launched in different parts of the world. To understand contemporary antisemitism, Lappin argues, it is essential to recognize the way in which its antecedents have become deeply embedded in Western and Middle Eastern cultures over millennia. This allows hostility to Jews to cross political boundaries easily, left and right, in a way that other forms of racism do not. Combatting antisemitism effectively requires a new progressive politics that addresses its root causes. The New Antisemitism is crucial reading for anyone concerned with the social pathologies unleashed by our current economic and political discontents.

A textbook exploring predictive modes of linguistic development and analysis. During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features:combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics.is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas.provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features:combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics.is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas.provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks.

The second edition of The Handbook of Contemporary Semantic Theory presents a comprehensive introduction to cutting-edge research in contemporary theoretical and computational semantics. Features completely new content from the first edition of The Handbook of Contemporary Semantic TheoryFeatures contributions by leading semanticists, who introduce core areas of contemporary semantic research, while discussing current researchSuitable for graduate students for courses in semantic theory and for advanced researchers as an introduction to current theoretical work

The second edition of The Handbook of Contemporary Semantic Theory presents a comprehensive introduction to cutting-edge research in contemporary theoretical and computational semantics. Features completely new content from the first edition of The Handbook of Contemporary Semantic TheoryFeatures contributions by leading semanticists, who introduce core areas of contemporary semantic research, while discussing current researchSuitable for graduate students for courses in semantic theory and for advanced researchers as an introduction to current theoretical work

This book provides a systematic study of three foundational issues in the semantics of natural language that have been relatively neglected in the past few decades. focuses on the formal characterization of intensions, the nature of an adequate type system for natural language semantics, and the formal power of the semantic representation language proposes a theory that offers a promising framework for developing a computational semantic system sufficiently expressive to capture the properties of natural language meaning while remaining computationally tractable written by two leading researchers and of interest to students and researchers in formal semantics, computational linguistics, logic, artificial intelligence, and the philosophy of language

The book offers a detailed critique of the economy-of-derivation model of grammar that has emerged within the framework of Chomsky's Minimalist Program. It looks at the conceptual and computational complexity problems as well as the empirical consequences of both global and local economy principles. The book compares the economy-of-derivation model with a local constraint model of grammar that does not invoke conditions on sets of derivations or on possible operations in a derivation. It argues that the pure local constraint model of grammar avoids the complexity problems resulting from economy-of-derivation principles and provides a more satisfactory explanation of the linguistic facts that economy theorists have cited in support of their approach. The local constraint model also allows for a more natural and empirically well-motivated grammatical architecture than the one postulated by the Minimalist Program.