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91 results in Artificial Intelligence and Natural Language Processing

Page 1 of 5

The Cambridge Companion to Religion and Artificial Intelligence
  • Edited by Beth Singler, Fraser Watts
  • Coming soon
  • Expected online publication date:
    December 2024
    Print publication:
    31 December 2024
  • Religion and artificial intelligence are now deeply enmeshed in humanity's collective imagination, narratives, institutions, and aspirations. Their growing entanglement also runs counter to several dominant narratives that engage with long-standing historical discussions regarding the relationship between the 'sacred” and the 'secular' - technology and science. This Cambridge Companion explores the fields of Religion and AI comprehensively and provides an authoritative guide to their symbiotic relationship. It examines established topics, such as transhumanism, together with new and emerging fields, notably, computer simulations of religion. Specific chapters are devoted to Judaism, Christianity, Islam, Hinduism, and Buddhism, while others demonstrate that entanglements between religion and AI are not always encapsulated through such a paradigm. Collectively, the volume addresses issues that AI raises for religions, and contributions that AI has made to religious studies, especially the conceptual and philosophical issues inherent in the concept of an intelligent machine, and social-cultural work on attitudes to AI and its impact on contemporary life. The diverse perspectives in this Companion demonstrate how all religions are now interacting with artificial intelligence.

Artificial Intelligence
  • Economic Perspectives and Models
  • Wim Naudé, Thomas Gries, Nicola Dimitri
  • Published online:
    23 May 2024
    Print publication:
    30 May 2024
  • Is Artificial Intelligence a more significant invention than electricity? Will it result in explosive economic growth and unimaginable wealth for all, or will it cause the extinction of all humans? Artificial Intelligence: Economic Perspectives and Models provides a sober analysis of these questions from an economics perspective. It argues that to better understand the impact of AI on economic outcomes, we must fundamentally change the way we think about AI in relation to models of economic growth. It describes the progress that has been made so far and offers two ways in which current modelling can be improved: firstly, to incorporate the nature of AI as providing abilities that complement and/or substitute for labour, and secondly, to consider demand-side constraints. Outlining the decision-theory basis of both AI and economics, this book shows how this, and the incorporation of AI into economic models, can provide useful tools for safe, human-centered AI.

Search Methods in Artificial Intelligence
  • Deepak Khemani
  • Published online:
    30 April 2024
    Print publication:
    24 October 2024
  • This book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in computer science and artificial intelligence, it covers a wide spectrum of search methods. Readers will be able to begin with simple approaches and gradually progress to more complex algorithms applied to a variety of problems. It demonstrates that search is all pervasive in artificial intelligence and equips the reader with the relevant skills. The text starts with an introduction to intelligent agents and search spaces. Basic search algorithms like depth first search and breadth first search are the starting points. Then, it proceeds to discuss heuristic search algorithms, stochastic local search, algorithm A*, and problem decomposition. It also examines how search is used in playing board games, deduction in logic and automated planning. The book concludes with a coverage on constraint satisfaction.

The Cambridge Handbook of Private Law and Artificial Intelligence
  • Edited by Ernest Lim, Phillip Morgan
  • Published online:
    21 March 2024
    Print publication:
    28 March 2024
  • AI appears to disrupt key private law doctrines, and threatens to undermine some of the principal rights protected by private law. The social changes prompted by AI may also generate significant new challenges for private law. It is thus likely that AI will lead to new developments in private law. This Cambridge Handbook is the first dedicated treatment of the interface between AI and private law, and the challenges that AI poses for private law. This Handbook brings together a global team of private law experts and computer scientists to deal with this problem, and to examine the interface between private law and AI, which includes issues such as whether existing private law can address the challenges of AI and whether and how private law needs to be reformed to reduce the risks of AI while retaining its benefits.

Deep Learning for Natural Language Processing
  • A Gentle Introduction
  • Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega
  • Published online:
    01 February 2024
    Print publication:
    08 February 2024
  • Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing. It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.

Privacy-preserving Computing
  • for Big Data Analytics and AI
  • Kai Chen, Qiang Yang
  • Published online:
    26 October 2023
    Print publication:
    16 November 2023
  • Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.

Distributional Semantics
  • Alessandro Lenci, Magnus Sahlgren
  • Published online:
    07 September 2023
    Print publication:
    21 September 2023
  • Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to construct semantic representations, a computational framework for acquiring meaning from language data, and a cognitive hypothesis about the role of language usage in shaping meaning. This book aims to build a common understanding of the theoretical and methodological foundations of distributional semantics. Beginning with its historical origins, the text exemplifies how the distributional approach is implemented in distributional semantic models. The main types of computational models, including modern deep learning ones, are described and evaluated, demonstrating how various types of semantic issues are addressed by those models. Open problems and challenges are also analyzed. Students and researchers in natural language processing, artificial intelligence, and cognitive science will appreciate this book.

Translation Technology in Accessible Health Communication
  • Meng Ji, Pierrette Bouillon, Mark Seligman
  • Published online:
    31 August 2023
    Print publication:
    21 September 2023
  • Digital health translation is an important application of machine translation and multilingual technologies, and there is a growing need for accessibility in digital health translation design for disadvantaged communities. This book addresses that need by highlighting state-of-the-art research on the design and evaluation of assistive translation tools, along with systems to facilitate cross-cultural and cross-lingual communications in health and medical settings. Using case studies as examples, the principles of designing assistive health communication tools are illustrated. These are (1) detectability of errors to boost user confidence by health professionals; (2) customizability for health and medical domains; (3) inclusivity of translation modalities to serve people with disabilities; and (4) equality of accessibility standards for localised multilingual websites of health contents. This book will appeal to readers from natural language processing, computer science, linguistics, translation studies, public health, media, and communication studies. This title is available as open access on Cambridge Core.

Artificial Intelligence
  • Foundations of Computational Agents
  • 3rd edition
  • David L. Poole, Alan K. Mackworth
  • Published online:
    14 August 2023
    Print publication:
    13 July 2023
  • Fully revised and updated, this third edition includes three new chapters on neural networks and deep learning including generative AI, causality, and the social, ethical and regulatory impacts of artificial intelligence. All parts have been updated with the methods that have been proven to work. The book's novel agent design space provides a coherent framework for learning, reasoning and decision making. Numerous realistic applications and examples facilitate student understanding. Every concept or algorithm is presented in pseudocode and open source AIPython code, enabling students to experiment with and build on the implementations. Five larger case studies are developed throughout the book and connect the design approaches to the applications. Each chapter now has a social impact section, enabling students to understand the impact of the various techniques as they learn them. An invaluable teaching package for undergraduate and graduate AI courses, this comprehensive textbook is accompanied by lecture slides, solutions, and code.

Annotation-Based Semantics for Space and Time in Language
  • Kiyong Lee
  • Published online:
    05 August 2023
    Print publication:
    29 June 2023
  • Space and time representation in language is important in linguistics and cognitive science research, as well as artificial intelligence applications like conversational robots and navigation systems. This book is the first for linguists and computer scientists that shows how to do model-theoretic semantics for temporal or spatial information in natural language, based on annotation structures. The book covers the entire cycle of developing a specification for annotation and the implementation of the model over the appropriate corpus for linguistic annotation. Its representation language is a type-theoretic, first-order logic in shallow semantics. Each interpretation model is delimited by a set of definitions of logical predicates used in semantic representations (e.g., past) or measuring expressions (e.g., counts or k). The counting function is then defined as a set and its cardinality, involving a universal quantification in a model. This definition then delineates a set of admissible models for interpretation.

Handbook of Augmented Reality Training Design Principles
  • Laura G. Militello, Christen E. Sushereba, Sowmya Ramachandran
  • Published online:
    18 May 2023
    Print publication:
    01 June 2023
  • The Handbook of Augmented Reality Training Design Principles is for anyone interested in using augmented reality and other forms of simulation to design better training. It includes eleven design principles aimed at training recognition skills for combat medics, emergency department physicians, military helicopter pilots, and others who must rapidly assess a situation to determine actions. Chapters on engagement, creating scenario-based training, fidelity and realism, building mental models, and scaffolding and reflection use real-world examples and theoretical links to present approaches for incorporating augmented reality training in effective ways. The Learn, Experience, Reflect framework is offered as a guide to applying these principles to training design. This handbook is a useful resource for innovative design training that leverages the strengths of augmented reality to create an engaging and productive learning experience.

Creating a More Transparent Internet
  • Edited by Piek Vossen, Antske Fokkens
  • Published online:
    21 April 2022
    Print publication:
    05 May 2022
  • On social media, new forms of communication arise rapidly, many of which are intense, dispersed, and create new communities at a global scale. Such communities can act as distinct information bubbles with their own perspective on the world, and it is difficult for people to find and monitor all these perspectives and relate the different claims made. Within this digital jungle of perspectives on truth, it is difficult to make informed decisions on important things like vaccinations, democracy, and climate change. Understanding and modeling this phenomenon in its full complexity requires an interdisciplinary approach, utilizing the ample data provided by digital communication to offer new insights and opportunities. This interdisciplinary book gives a comprehensive view on social media communication, the different forms it takes, the impact and the technology used to mine it, and defines the roadmap to a more transparent Web.

Personalized Machine Learning
  • Julian McAuley
  • Published online:
    20 January 2022
    Print publication:
    03 February 2022
  • Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.

Computational Analysis of Storylines
  • Making Sense of Events
  • Edited by Tommaso Caselli, Eduard Hovy, Martha Palmer, Piek Vossen
  • Published online:
    06 November 2021
    Print publication:
    25 November 2021
  • Event structures are central in Linguistics and Artificial Intelligence research: people can easily refer to changes in the world, identify their participants, distinguish relevant information, and have expectations of what can happen next. Part of this process is based on mechanisms similar to narratives, which are at the heart of information sharing. But it remains difficult to automatically detect events or automatically construct stories from such event representations. This book explores how to handle today's massive news streams and provides multidimensional, multimodal, and distributed approaches, like automated deep learning, to capture events and narrative structures involved in a 'story'. This overview of the current state-of-the-art on event extraction, temporal and casual relations, and storyline extraction aims to establish a new multidisciplinary research community with a common terminology and research agenda. Graduate students and researchers in natural language processing, computational linguistics, and media studies will benefit from this book.

Artificial Economics
  • Methods, Models, and Interdisciplinary Links
  • Ruben Mercado
  • Published online:
    28 October 2021
    Print publication:
    04 November 2021
  • This introductory overview explores the methods, models and interdisciplinary links of artificial economics, a new way of doing economics in which the interactions of artificial economic agents are computationally simulated to study their individual and group behavior patterns. Conceptually and intuitively, and with simple examples, Mercado addresses the differences between the basic assumptions and methods of artificial economics and those of mainstream economics. He goes on to explore various disciplines from which the concepts and methods of artificial economics originate; for example cognitive science, neuroscience, artificial intelligence, evolutionary science and complexity science. Introductory discussions on several controversial issues are offered, such as the application of the concepts of evolution and complexity in economics and the relationship between artificial intelligence and the philosophies of mind. This is one of the first books to fully address artificial economics, emphasizing its interdisciplinary links and presenting in a balanced way its occasionally controversial aspects.

Vision
  • Edited by Andrew Fabian, Janet Gibson, Mike Sheppard, Simone Weyand
  • With contributions by Andrew Blake , Carolin Crawford , Paul Fletcher , Sophie Hackford , Anya Hurlbert , Dan-Eric Nilsson and Carlo Rovelli
  • Published online:
    17 September 2021
    Print publication:
    26 August 2021
  • Arising from the 2019 Darwin College Lectures, this book presents essays from seven prominent public intellectuals on the theme of vision. Each author examines this theme through the lens of their own particular area of expertise, making for a lively interdisciplinary volume including chapters on neuroscience, colour perception, biological evolution, astronomy, the future of technology, computer vision, and the visionary core of science. Featuring contributions by professors of neuroscience Paul Fletcher and Anya Hurlbert, professor of zoology Dan-Eric Nilsson, the futurist Sophie Hackford, Microsoft distinguished scientist Andrew Blake, theoretical physicist and author Carlo Rovelli, and Dr Carolin Crawford, the Public Astronomer at the University of Cambridge, this volume will be of interest to anybody curious about how we see the world.

Similar Languages, Varieties, and Dialects
  • A Computational Perspective
  • Edited by Marcos Zampieri, Preslav Nakov
  • Published online:
    12 August 2021
    Print publication:
    02 September 2021
  • Language resources and computational models are becoming increasingly important for the study of language variation. A main challenge of this interdisciplinary field is that linguistics researchers may not be familiar with these helpful computational tools and many NLP researchers are often not familiar with language variation phenomena. This essential reference introduces researchers to the necessary computational models for processing similar languages, varieties, and dialects. In this book, leading experts tackle the inherent challenges of the field by balancing a thorough discussion of the theoretical background with a meaningful overview of state-of-the-art language technology. The book can be used in a graduate course, or as a supplementary text for courses on language variation, dialectology, and sociolinguistics or on computational linguistics and NLP. Part 1 covers the linguistic fundamentals of the field such as the question of status and language variation. Part 2 discusses data collection and pre-processing methods. Finally, Part 3 presents NLP applications such as speech processing, machine translation, and language-specific issues in Arabic and Chinese.

Hey Cyba
  • The Inner Workings of a Virtual Personal Assistant
  • Steve Young
  • Published online:
    26 March 2021
    Print publication:
    08 April 2021
  • Recent developments in artificial intelligence, especially neural network and deep learning technology, have led to rapidly improving performance in voice assistants such as Siri and Alexa. Over the next few years, capability will continue to improve and become increasingly personalised. Today's voice assistants will evolve into virtual personal assistants firmly embedded within our everyday lives. Told through the view of a fictitious personal assistant called Cyba, this book provides an accessible but detailed overview of how a conversational voice assistant works, especially how it understands spoken language, manages conversations, answers questions and generates responses. Cyba explains through examples and diagrams the neural network technology underlying speech recognition and synthesis, natural language understanding, knowledge representation, conversation management, language translation and chatbot technology. Cyba also explores the implications of this rapidly evolving technology for security, privacy and bias, and gives a glimpse of future developments. Cyba's website can be found at HeyCyba.com.

Natural Language Processing
  • A Machine Learning Perspective
  • Yue Zhang, Zhiyang Teng
  • Published online:
    16 December 2020
    Print publication:
    07 January 2021
  • With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

Sentiment Analysis
  • Mining Opinions, Sentiments, and Emotions
  • 2nd edition
  • Bing Liu
  • Published online:
    23 September 2020
    Print publication:
    15 October 2020
  • Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.

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