Shalom Lappin – författare

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:

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:

Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction and information theory, rather than on formal models of grammar induction.

This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioral science and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language. The text represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between computational models and natural language.

Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction and information theory, rather than on formal models of grammar induction.

This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioral science and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language. The text represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between computational models and natural language.

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.