Artificial intelligence (AI) Books

Book SynopsisThe fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.This book is written for researchers, practitioners, engineers, and AI consultants.Table of Contents1. Multiobjective Optimization: An Overview. 2. Inverse Problems. 3. Decision Tree for Classification and Forecasting. 4. A Review of Choice Topics in Quantum Computing and Some Connections with Machine Learning. 5. Sparse Models for Machine Learning. 6. Interpretability in Machine Learning. 7. Big Data: Concepts, Techniques, and Considerations. 8. A Machine of Many Faces: On the Issue of Interface in Artificial Intelligence and Tools from User Experience. 9. Artificial Intelligence Technologies and Platforms. 10. Artificial Neural Networks. 11. Multicriteria Optimization in Deep Learning. 12. Natural Language Processing: Current Methods and Challenges. 13. AI and Imaging in Remote Sensing. 14. AI in Agriculture. 15. AI and Cancer Imaging. 16. AI in Ecommerce: From Amazon and TikTok, GPT-3 and LaMDA, to the Metaverse and Beyond. 17. The Difficulties of Clinical NLP. 18. Inclusive Green Growth in OECD Countries: Insight from The Lasso Regularization and Inferential Techniques. 19. Quality Assessment of Medical Images. 20. Securing Machine Learning Models: Notions and Open Issues.

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Book SynopsisHandbook of Alternative Data in Finance, Volume I motivates and challenges the reader to explore and apply Alternative Data in finance. The book provides a robust and in-depth overview of Alternative Data, including its definition, characteristics, difference from conventional data, categories of Alternative Data, Alternative Data providers, and more. The book also offers a rigorous and detailed exploration of process, application and delivery that should be practically useful to researchers and practitioners alike. Features Includes cutting edge applications in machine learning, fintech, and more Suitable for professional quantitative analysts, and as a resource for postgraduates and researchers in financial mathematics Features chapters from many leading researchers and practitioners Trade Review"Alternative data has become a hot topic in finance. New kinds of data, new data sources, and of course new tools for processing such data offer the possibility of new and previously unsuspected signals. In short alternative data lead to the promise of enhanced predictive power. But such advance does not come without its challenges - in terms of the quality of the data, the length of its history, reliable data capture, the development of appropriate statistical, AI, machine learning, and data mining tools, and, of course, the ethical challenges in the face of increasingly tough data protection regimes. Gautam Mitra and his colleagues have put together a superb collection of chapters discussing these topics, and more, to show how alternative data, used with care and expertise, can reveal the bigger picture."– Professor David J. Hand, Emeritus Professor of Mathematics and Senior Research Investigator, Imperial College, London"Digital capital is now so important that it can rightly be viewed as a factor of production, especially in the financial sector. This handbook does for the field of alternative data what vendors of alternative data do for data itself; and that is to provide structure, filter noise, and bring clarity. It is an indispensable work which every financial professional can consult, be it for an overview of the field or for specific details about alternative data."– Professor Hersh Shefrin, Mario L. Belotti Professor of Finance, Santa Clara UniversityAn impressive and timely contribution to the fast developing discipline of data driven decisions in the trading and management of financial risk. Automated data collection, organization, and dissemination is part and parcel of Data Science and the Handbook covers the current breadth of these activities, their risks, rewards, and costs. A welcome addition to the landscape of quantitative finance.–Professor Dilip Madan, Professor of Finance, Robert H. Smith School of Business"The Handbook of Alternative Data in Finance is the most comprehensive guide to alternative data I have seen. It could be called the Encyclopaedia of Alternative Data. It belongs to the desktop, not the bookshelf, of every investor."– Ernest Chan, Respected Academic, Author, Practicing Fund Manager, Entrepreneur and Founder of PredictNow.AI "Professor Gautam Mitra and his team unpack the topic of alternative data in finance, an ambitious endeavor given the fast-expanding nature of this new and exciting space. Alternative data powered by Natural Language Processing and Machine Learning has emerged as a new source of insights that can help investors make more informed decisions, stay ahead of competition and mitigate emerging risks. This handbook provides a strong validation of the substantial added value that alternative data brings. It also helps promote the idea that data driven decisions are better and more sustainable – something we, at RavenPack, firmly believe."– Armando Gonzalez, CEO and Founder of RavenPack"As the 1st Duke of Marlborough, John Churchill, wrote in 1715: 'No war can be conducted successfully without early and good intelligence.' The same can be said for successful trading. In that light, the Handbook of Alternative Data in Finance contains vital insights about how to gather and use alternative data —in short, intelligence —to facilitate successful trading."– Professor Steve H. Hanke, Professor of Applied Economics, The Johns Hopkins University, Baltimore, USA"The Handbook of Alternative Data in Finance is cutting edge and it bridges a huge gap in the representative studies on emerging areas of finance where alternative data can be profitably utilised for better informed decisions. The practical insights in the book would come very handy to both investors and researchers who look for fresh ideas."– Ashok Banerjee, Director, Indian Institute of Management Udaipur, Formerly Dean, and Faculty-in-charge of the Finance Lab at Indian Institute of Management CalcuttaTable of Contents1. Alternative Data: Overview. Part I. Alternative Data: Processing and Impact. 2. Contemplation and Reflection on Using Alternative Data for Trading and Fund Management. 3. Global Economy and Markets Sentiment Model. Part II. Coupling Models with Alternative Data for Financial Analytics. 4. Enhanced Corporate Bond Yield Modelling Incorporating Macroeconomic News Sentiment. 5. AI, Machine Learning and Quantitative Models. Part III. Handling Different Alternative Datasets. 6. Asset Allocation Strategies: Enhanced by Micro-Blog. 7. Asset Allocation Strategies: Enhanced by News. 8. Extracting Structured Datasets from Textual Sources: Some Examples. 9. Comparative Analysis of NLP Approaches for Earnings Calls. 10. Sensors Data. Part IV. Alternative Data Use Cases in Finance. Part IV.A. Application in Trading and Fund Management (Finding New Alpha). 11. Media Sentiment Momentum. 12. Defining Market States with Media Sentiment. Part IV.B. Application in Risk Control. 13. A Quantitative Metric for Corporate Sustainability. 14. Hot off the Press: Predicting Intraday Risk and Liquidity with News Analytics. 15. Exogenous Risks Alternative Data Implications for Strategic Asset Allocation: Multi-Subordination Levy Processes Approach. Part IV.C. Case Studies on ESG. 16. ESG Controversies and Stock Returns. 17. Oil and Gas Drilling Waste: A Material Externality. 18: ESG Scores and Price Momentum Are Compatible: Revisited. Part V. Directory of Alternative Data Vendors.

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Book SynopsisThis book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems.Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain.Table of Contents1 Healthcare Fees-Centric to Value-Centric Transformation through Data, Analytics, and Artificial Intelligence2 AI-Based Healthcare: Top Businesses and Technologies 3 Insights into AI, Machine Learning, and Deep Learning 4 Deep Learning for Visual Perceptual Brain Decoding as Image Classification 5 Automatic Brain Tumor Segmentation in Multimodal MRI Images Using Deep Learning 6 Automated Prediction of Lung Cancer Using Deep Learning Algorithms 7 Cervical Cancer Screening Approach Using AI D. Santhi, M. Carmel Sobia, and M. Jayalakshmi 8 Progression Detection of Multiple Sclerosis in Brain MRI Images 9 Artificial Intelligence Clustering Techniques on Dermoscopic Skin Lesion Images 10 Automated Alzheimer’s Disease Detection with Optimized Fuzzy Neural Network 11 A Comprehensive Survey with Bibliometric Analysis on Recent Research Opportunities of Multimodal Medical Image Fusion in Various Applications 12 Big Data in IoT for Healthcare Application 13 Automatic Detection of Diabetic Retinopathy to Avoid Blindness 14 A Review on Wireless BAN to Measure the Respiration Rate Using SoC Architecture 15 Deep Feature Extraction for EEG Signal Classification in Motor Imagery Tasks 16 Effect of Age in Normal Women by Heart Rate Variability Analysis17 EEG Signal Analysis Using Machine Learning and Artificial Intelligence for Identification of Brain Dysfunction18 Cervical Cancer Screening Methods: Comprehensive Survey 19 Understanding Assessment Methods and Sensors for ADHD Hyperactive-Impulsive Type among Children 20 Security of Medical Image Information by Cryptography and Watermarking Using Python 21 Integration of Biosensors and Drug Delivery Systems for Biomedical Applications 22 Automatic Liver and Lesion Segmentation in CT Using 3-D Context Convolutional Neural Network: 3-D Context U-Net

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Book SynopsisThe demand for telehealth solutions has been growing exponentially after the Covid-19 pandemic. Hospitals remain understaffed, which leads to staff burnouts and unsatisfactory patient experience. They also find it difficult to use AI to reduce the workload for doctors and nurses. Doctors barely use data collected from wearables and home-use medical devices to make diagnosis. As generative AI advances, traditional medical device manufacturers are exploring with open innovation to transform into a software-based business model facing competition from large tech companies and startups. This book shares the perspectives from different stakeholders around the challenges of the use of AI in healthcare.Table of ContentsTelehealth Solution Market Demands in China and in Europe (Germany, Denmark and Italy). Developing the Innovation Ecosystem: A Case Study on the Chinese Digital Healthcare Industry. Use of AI in Combating COVID-19: Practices in Different Economies. COVID-19 and the Digitalization of the Healthcare System.

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Book SynopsisThis book explores in theory and practice how artificial intelligence (AI) intersects with and alters the city. Drawing upon a range of urban disciplines and case studies, the chapters reveal the multitude of repercussions that AI is having on urban society, urban infrastructure, urban governance, urban planning and urban sustainability.Contributors also examine how the city, far from being a passive recipient of new technologies, is influencing and reframing AI through subtle processes of co-constitution. The book advances three main contributions and arguments:First, it provides empirical evidence of the emergence of a post-smart trajectory for cities in which new material and decision-making capabilities are being assembled through multiple AIs. Second, it stresses the importance of understanding the mutually constitutive relations between the new experiences enabled by AI technology and the urban context. Trade Review"One of the great puzzles of modernity involves the way new technologies change the very systems that spawn them. Artificial Intelligence and the City brings together a diverse array of ideas that show how digital developments from autonomous vehicles, drones and robots to platform economies and predictive policing, are changing the way we behave and the regulations we are inventing to contain them. This is the first book to provide an integrated picture of the new landscape of urban artificial intelligences, one that we will all need to navigate on the road to the future. Essential reading for all who are attempting to understand the critical challenges of AI." Michael Batty, Bartlett Professor of Planning, University College London "The advent of generative AI and deep learning algorithms has undercut and transcended the concept and technical practice of the so-called smart city. With Artificial Intelligence and the City the shift from smart ontologies to AI logics of the urban is explored across multiple case studies, from urban drones to autonomous vehicles in the city. A timely and important intervention." Louise Amoore, Professor of Political Geography, Durham University "Artificial intelligence is transforming the socio-technical characteristics of cities under late modernity. This vital collection of essays presents multiple vantage points from which to reflect on emerging articulations between AI and urban space." Matthew Gandy, Professor of Geography, University of Cambridge. "By departing from the polemic that typifies explorations of artificial intelligences, this book is a well-structured and thoughtfully curated volume on the interrelationships between AI and cities. This welcome departure from smart urbanism explores the textures of urban AI at varying scales and geographic contexts, and offers the reader many stories of caution and hope by exploring, not only how the city is influenced by autonomous vehicles, robotics, platforms and algorithms, but also how it reframes and reorders these socio-technical relations." Nancy Odendaal, Professor in City Planning, University of Cape Town "This timely book provides a comprehensive and thought-provoking analysis of urban AI, examining in detail the workings and implications of autonomous vehicles, robotics, and AI-enabled platforms and services for city life. Richly illustrated with case studies, it is an essential guide to our emerging sentient cities." Rob Kitchin, Professor of Human Geography, Maynooth University "In this fantastic contribution to the field of Urban AI, the authors outline the plethora of issues pertaining to the era of urban artificial intelligence that is now upon us. They present the many ways in which AI and robotics have entered into urban spaces while reminding the reader that such techno-urban symbiosis is not new, and thus deserves careful consideration for the short and long term implications on the fabric of the city. For any reader interested in (sustainable) AI and the future of cities, this book is sure to open one's eyes to the many ways in which cities have become experimental testing sites for AI with implications for those living in cities, for the structure of the city, and for the future of AI." Aimee van Wynsberghe, Alexander von Humboldt Professor of Applied Ethics for AI, University of Bonn "One of the great puzzles of modernity involves the way new technologies change the very systems that spawn them. Artificial Intelligence and the City brings together a diverse array of ideas that show how digital developments from autonomous vehicles, drones and robots to platform economies and predictive policing, are changing the way we behave and the regulations we are inventing to contain them. This is the first book to provide an integrated picture of the new landscape of urban artificial intelligences, one that we will all need to navigate on the road to the future. Essential reading for all who are attempting to understand the critical challenges of AI." Michael Batty, Bartlett Professor of Planning, University College London "The advent of generative AI and deep learning algorithms has undercut and transcended the concept and technical practice of the so-called smart city. With Artificial Intelligence and the City the shift from smart ontologies to AI logics of the urban is explored across multiple case studies, from urban drones to autonomous vehicles in the city. A timely and important intervention." Louise Amoore, Professor of Political Geography, Durham University. "Artificial intelligence is transforming the socio-technical characteristics of cities under late modernity. This vital collection of essays presents multiple vantage points from which to reflect on emerging articulations between AI and urban space." Matthew Gandy, Professor of Geography, University of Cambridge. "By departing from the polemic that typifies explorations of artificial intelligences, this book is a well-structured and thoughtfully curated volume on the interrelationships between AI and cities. This welcome departure from smart urbanism explores the textures of urban AI at varying scales and geographic contexts, and offers the reader many stories of caution and hope by exploring, not only how the city is influenced by autonomous vehicles, robotics, platforms and algorithms, but also how it reframes and reorders these socio-technical relations." Nancy Odendaal, Professor in City Planning, University of Cape Town "This timely book provides a comprehensive and thought-provoking analysis of urban AI, examining in detail the workings and implications of autonomous vehicles, robotics, and AI-enabled platforms and services for city life. Richly illustrated with case studies, it is an essential guide to our emerging sentient cities." Rob Kitchin, Professor of Human Geography, Maynooth University "In this fantastic contribution to the field of Urban AI, the authors outline the plethora of issues pertaining to the era of urban artificial intelligence that is now upon us. They present the many ways in which AI and robotics have entered into urban spaces while reminding the reader that such techno-urban symbiosis is not new, and thus deserves careful consideration for the short and long term implications on the fabric of the city. For any reader interested in (sustainable) AI and the future of cities, this book is sure to open one's eyes to the many ways in which cities have become experimental testing sites for AI with implications for those living in cities, for the structure of the city, and for the future of AI." Aimee van Wynsberghe, Alexander von Humboldt Professor of Applied Ethics for AI, University of Bonn Table of ContentsChapter 1: Introducing AI into Urban Studies Section 1 – Autonomous Vehicles and Mobility Chapter 2: Reinforcing and Refracting Automobility: Urban Experimentation with Autonomous Vehicles Chapter 3: Trials and Tribulations: Who Learns What from Urban Experiments with Self-driving Vehicles? Chapter 4: Autonomous Lorries, Artificial Intelligence and Urban (Freight) Mobilities Chapter 5: An Urbanistic Take on Autonomous Vehicles Chapter 6: A Roadmap for the Sustainable Deployment of Autonomous Vehicles: Superblocks Driving Cars out of Neighbourhoods Section 2 – Urban Robots and Robotic Spaces Chapter 7: Regulating and Making Space for the Expanded Field of Urban RoboticsChapter 8: Everyday Droning: Uneven Experiences of Drone-enabled AI Urbanism Chapter 9: Exploring Temporal Pleats and Folds: the Role of Urban AI and Robotics in Reinvigorating the Cyborg City Chapter 10: Robots in AI Urbanism Chapter 11: Airport Robots: Automation, Everyday Life and the Futures of Urbanism Section 3 – City Brains and Urban Platforms Chapter 12: Ambient Commons? Valuing Urban Public Spaces in an Era of AI-Enabled Ambient Computing Chapter 13: Encountering Limits in Cooperative Platforms: the More-Than-Technical Labour of Urban AI Chapter 14: Performed Imaginaries of the AI-Controlled City: Conducting Urban AI Experimentation in China Chapter 15: Optimizing the Immeasurable: on the Techno-Ethical Limits of Predictive Policing Chapter 16: Chinese Artificial Intelligence Governance Platforms 2.0: the Belt and Road Edition Section 4 – Urban Software Agents and Algorithms Chapter 17: Perceptions of Intelligence in Urban AI and the Contingent Logics of Real Estate Estimate Algorithms Chapter 18: Caring is Connecting: AI Digital Assistants and the Surveillance of Elderly and Disabled Family Members in the Home Chapter 19: AI Doctors or AI for Doctors? Augmenting Urban Healthcare Services Through Artificial Intelligence Chapter 20: Algorithms and Racial Discrimination in the U.S. Housing Market Chapter 21: Architectural AI: Urban Artificial Intelligence in Architecture and Design Chapter 22: Conclusions: The Present of Urban AI and the Future of Cities

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Book SynopsisThis book explores the relationships between artificial intelligence (AI) and education in China. It examines educational activity in the context of profound technological interventions, far-reaching national policy, and multifaceted cultural settings. By standing at the intersection of three foundational topics AI and the recent proliferation of data-driven technologies; education, the most foundational of our social institutions in terms of actively shaping societies and individuals; and, finally, China, which is a frequent subject for dramatic media reports about both technology and education this book offers an insightful view of the contexts that underpin the use of AI in education, and promotes a more in-depth understanding of China. Scholars of educational technology and digital education will find this book an indispensable guide to the ways new technologies are imagined to transform the future, while being firmly grounded in the past.Trade Review"Western commentators often talk about the rise of AI in Chinese education with a mixture of fascination and horror. Jeremy Knox moves beyond the usual techno-orientalist stereotypes, and offers a clear-eyed appraisal of what China can teach us about the fast-changing relationships between AI, education, society and culture."Neil Selwyn, Monash University, AustraliaTable of Contents1 Introduction 2. Policy, governance, and the state 3. Innovation, entrepreneurialism, and private enterprise 4. ‘Double reduction’ and the return of the state 5. Cities, regions, and rural divides 6. ‘Talent’ and the international flow of AI expertise 7. Personalisation, subjectivity, and the Chinese ‘self’ 8. Conclusions

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Book SynopsisUX research, the key to comprehending users'' behaviors, motivations, and preferences for developing delightful experiences, thrives on effective teamwork and collaboration. This comprehensive guide brings together the expertise and insights from seasoned researchers, cross-functional partners, and product leaders in order to transform how you collaborate and unlock the true potential of UX research.Key FeaturesIncludes a comprehensive selection of ready-to-use templatesIncorporates insights and advice from cross-functional stakeholdersOffers a wide range of strategies tailored to various expertise levels, catering to both novice and advanced practitionersPresents universally applicable methodologies and insights, equipping a diverse range of researchers, including consultants, vendors, and in-house professionalsFroTable of Contents1. Introduction, 2. Who Are (Your) Cross-Functional Partners and Why Is Their Buy-In Important?, 3. Mistakes That UX Researchers Make With Cross-Functional Partnerships, 4. Simple Strategies for Effective Cross-Functional Collaboration, 5. Advanced Strategies for Effective Cross-Functional Collaboration

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Book SynopsisCloud based Multi-Modal Information Analytics: A Hands-on Approach discusses the various modalities of data and provide an aggregated solution using cloud. It includes the fundamentals of neural networks, different types and how they can be used for the multi-modal information analytics. The various application areas that are image-centric and videos are also presented with deployment solutions in the cloud. Features Life cycle of the multi- modal data analytics is discussed with applications of modalities of text, image, and video. Deep Learning fundamentals and architectures covering convolutional Neural Networks, recurrremt neural networks, and types of learning for different multi-modal networks. Applications of Multi-Modal Analytics covering Text , Speech, and Image. This book is aimed at researchers in Multi-modal analytics and related areas

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Book SynopsisThe smart city movement, during the last decade and a half, advocated the built environment and digital technology convergence with the backing of institutional capital and government support. The commitment of a significant number of local governments across the globe, in terms of official smart city policies and initiatives, along with the constant push of global technology giants, has reinforced the popularity of this movement. This two-volume treatment on smart cities thoroughly explores and sheds light on the prominent elements of the smart city phenomenon and generates a smart city blueprint.This first volume, with its 12 chapters, provides a sound understanding on the key foundations and growth directions of smart city frameworks, technologies, and platforms, with theoretical expansions, practical implications, and real-world case study lessons.The second companion volume offers sophisticated perspectives on the key foundations and directions of smart city policTrade Review‘Finding a place among the seminal works and explaining the way cities are and the way they should be, is Tan Yigitcanlar’s two-volume Smart City Blueprint book. These volumes explain how in the second decade of the 21st century information and communications technologies are ubiquitous and part of every urban network, every urban infrastructure.’—Professor Richard E. Hanley, The City University of New York, USA‘Smart City Blueprint that comes in two volumes provides an innovative, holistic view of smart cities that will prove invaluable for research, classroom, and practice. From a novel examination of their development to a rethinking of the concept, 24 chapters of these volumes offer new frameworks and evidence that will surely influence the future of the field.’—Professor Karen Mossberger, Arizona State University, USA‘Drawing together and synthesising a vast range of literature and empirical case examples of applied practice, the two volumes of Smart City Blueprint form a comprehensive resource for policymakers, communities and academics interested in critically evaluating and implementing smart city initiatives globally.’—Professor Rob Kitchin, Maynooth University Social Sciences Institute, Ireland‘Notwithstanding recent trends to localise the smart city within particular contexts, there remains a need for critical knowledge about key principles underlying the smart city. In response, the two volumes of Smart City Blueprint deliver a comprehensive framework that maps core building blocks for use in smart city theory, policy, and planning. A must read.’—Professor Simon Joss, University of Glasgow, UKTable of ContentsPart 1: Smart City Framework. 1. Multidimensional Perspective. 2. Smart and Sustainable. 3. Transformation Readiness. Part 2: Smart City Technology. 4. Perception and Utilisation. 5. Smarter with Technology. 6. Urban Artificial Intelligences. 7. Green Artificial Intelligence. 8. Smart Urban Mobility. Part 3: Smart City Platform. 9. Mobile Energy as a Service. 10. Mobility as a Service. 11. Urban Management Platform. 12. City as a Platform.

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Book SynopsisWhen the digital world started, many companies moved slowly and cautiously, not willing to replace their traditional operations. Now most companies have gone digital. We are now moving beyond digital into an AI world. Don't ignore it. This important book will guide you by providing a fresh perspective on the interrelationships between humans and AI. â Philip KotlerDo you feel overwhelmed by the AI wave? Worried that it could cost you your job, harm your business, or even take over?AI has pervaded our lives and is aggressively disrupting business. No person today can afford to ignore AI.Age of Agency is your companion, helping you leverage AI's capabilities to power your productivity and success. By understanding AI, you will learn to use it as a tool for personal career growth and business success.Former Microsoft executive Kerushan Govender demystifies AI, emphasising the importance of human agency. Reconnect with the needs of humanity and learn the importance of care as a differentiator in an AI world. Avoid the potential pitfalls of excessive reliance on the technology. Age of Agency is a blueprint for ensuring human agency outpaces computer agency. It boldly pits the limits of machine learning against the infinity of human ability. With this survival guide, youâll uncover ways to connect with humanity on a deeper level, going beyond anything AI can do. Ready to become AI-savvy, with your humanity as your differentiator? Dive into the future with the confidence to ride the wave of todayâs AI revolution.Table of ContentsPreface. About the Author. 1 Introduction. SECTION 1 MASTER YOUR OWN AGENCY. 2 Defining Agency. 3 The Power of Observation, Resourcefulness, and Creativity. 4 Harnessing Human Agency in an AI Era. SECTION 2 MASTER AI. 5 A Bit of History. 6 Coming to Terms with What AI Can and Cannot Do. 7 AI as a Transformative Productivity Hack. SECTION 3 DEVELOP CARE. 8 Care in the Professional Realm. 9 Building Client Experiences Rooted in Care. 10 Reimagining Client Experiences. SECTION 4 UNDERSTAND HUMANITY. 11 A Theoretical Grounding. 12 The Real World. 13 Rise with AI. 14 Conclusion. Index.

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Book SynopsisUnderstanding Dialogical Leadership emphasizes the power of dialogue, self-reflection, and shared decision-making in navigating the complexities of collaborative leadership, especially in the face of evolving challenges in modern life.The book underscores the importance of being conscious and open to diverse perspectives. The four pillars of dialogical leadership, which form the foundation of its philosophy, focus on flexible application of internal and external dialogues, self-awareness in leadership roles, proactive interchange of influence styles, and creating conditions for meaningful dialogues. A dialogue approach can provide answers to critical questions and wicked issues in the contemporary world, such as the balance between intellect and wisdom in the age of advanced technologies. The book positions dialogue as a dynamic force that brings people together, fostering an environment where valuable insights can emerge. The authors intertwine the ideas of Yuval Harari, David Bohm, Kenneth Gergen, Hubert Hermans, and other leadership authorities, highlighting the importance of relational reflection, with the practice of self-observation, meta-consciousness, and meditation. The objective is to create a collaborative and empowering environment that fosters innovation, trust, and the well-being of others.Transcending traditional leadership models by acknowledging the need for adaptability, self-awareness, and open communication, and including a chapter on dialogical leadership and artificial intelligence, this book encourages leaders to engage in conversations that bridge diverse perspectives and foster a deeper understanding of the self in a relational world and the complex challenges faced by humanity. It will appeal to leaders, coaches, and HR professionals, as well as those studying and looking to develop leadership.

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Book SynopsisMachine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included.Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machiTable of Contents1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work

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Book SynopsisThis book provides comprehensive details of all Swarm Intelligence based Techniques available till date in a comprehensive manner along with their mathematical proofs. It will act as a foundation for authors, researchers and industry professionals. This monograph will present the latest state of the art research being done on varied Intelligent Technologies like sensor networks, machine learning, optical fiber communications, digital signal processing, image processing and many more.Table of ContentsContentsList of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xv1. Evolutionary Computation: Theory and Algorithms . . . . . . . . . . . . . . . .1Anand Nayyar, Surbhi Garg, Deepak Gupta and Ashish Khanna1.1 History of Evolutionary Computation . . . . . . . . . . . . . . . . . . . . . .21.2 Motivation via Biological Evidence . . . . . . . . . . . . . . . . . . . . . . . . .31.3 Why Evolutionary Computing?. . . . . . . . . . . . . . . . . . . . . . . . . . . .51.4 Concept of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . .61.5 Components of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . .91.6 Working of Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . .131.7 Evolutionary Computation Techniques and Paradigms. . . . . . . 151.8 Applications of Evolutionary Computing . . . . . . . . . . . . . . . . . .211.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232. Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26Sandeep Kumar, Sanjay Jain and Harish Sharma2.1 Overview of Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . .262.2 Genetic Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .312.3 Derivation of Simple Genetic Algorithm . . . . . . . . . . . . . . . . . . .382.4 Genetic Algorithms vs. Other Optimization Techniques . . . . . . 422.5 Pros and Cons of Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . .442.6 Hybrid Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .442.7 Possible Applications of Computer Science via GeneticAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .452.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473. Introduction to Swarm Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52Anand Nayyar and Gia Nhu Nguyen3.1 Biological Foundations of Swarm Intelligence . . . . . . . . . . . . . . .523.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .553.3 Concept of Swarm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .613.4 Collective Intelligence of Natural Animals. . . . . . . . . . . . . . . . . .623.5 Concept of Self-Organization in Social Insects. . . . . . . . . . . . . . .673.6 Adaptability and Diversity in Swarm Intelligence . . . . . . . . . . .683.7 Issues Concerning Swarm Intelligence . . . . . . . . . . . . . . . . . . . . .703.8 Future Swarm Intelligence in Robotics – Swarm Robotics . . . . . 713.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .74References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744. Ant Colony Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77Bandana Mahapatra and Srikanta Pattnaik4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .784.2 Concept of Artificial Ants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .794.3 Foraging Behavior of Ants and Estimating Effective Paths . . . . 814.4 ACO Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .854.5 ACO Applied Toward Travelling Salesperson Problem. . . . . . . 894.6 ACO Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .914.7 The Ant Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .934.8 Comparison of Ant Colony Optimization Algorithms . . . . . . . .954.9 ACO for NP Hard Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . .1004.10 Current Trends in ACO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034.11 Application of ACO in Different Fields . . . . . . . . . . . . . . . . . . .1044.12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1075. Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112M. B. Shanthi, D. Komagal Meenakshi and PremKumar5.1 Particle Swarm Optimization – Basic Concepts . . . . . . . . . . . . .1135.2 PSO Variants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1155.3 Particle Swarm Optimization (PSO) – Advanced Concepts . . . 1315.4 Applications of PSO in Various Engineering Domains. . . . . . .1365.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1386. Artificial Bee Colony, Firefly Swarm Optimization, and BatAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .141Sandeep Kumar and Rajani Kumari6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1426.2 The Artificial Bee Colony Algorithm. . . . . . . . . . . . . . . . . . . . . .1436.3 The Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1596.4 The Bat Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1666.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1737. Cuckoo Search Algorithm, Glowworm Algorithm,WASP, and Fish Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . .179Akshi Kumar7.1 Introduction to Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . .1807.2 Cuckoo Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1827.3 Glowworm Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1967.4 Wasp Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2047.5 Fish Swarm Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2097.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .217References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2178. Misc. Swarm Intelligence Techniques . . . . . . . . . . . . . . . . . . . . . . . . . .221M. Balamurugan, S. Narendiran and Sarat Kumar Sahoo8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2228.2 Termite Hill Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2238.3 Cockroach Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . .2268.4 Bumblebee Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2288.5 Social Spider Optimization Algorithm . . . . . . . . . . . . . . . . . . . .2308.6 Cat Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2338.7 Monkey Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2358.8 Intelligent Water Drop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2378.9 Dolphin Echolocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2388.10 Biogeography-Based Optimization . . . . . . . . . . . . . . . . . . . . . . .2408.11 Paddy Field Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2438.12 Weightless Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . .2448.13 Eagle Strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2458.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .246References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2479. Swarm Intelligence Techniques for Optimizing Problems. . . . . . . . .249K. Vikram and Sarat Kumar Sahoo9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2499.2 Swarm Intelligence for Communication Networks. . . . . . . . . .2509.3 Swarm Intelligence in Robotics . . . . . . . . . . . . . . . . . . . . . . . . . .2539.4 Swarm Intelligence in Data Mining. . . . . . . . . . . . . . . . . . . . . . .2579.5 Swarm Intelligence and Big Data. . . . . . . . . . . . . . . . . . . . . . . . .2609.6 Swarm Intelligence in Artificial Intelligence (AI) . . . . . . . . . . .2649.7 Swarm Intelligence and the Internet of Things (IoT). . . . . . . . .2669.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .269References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .274

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Book SynopsisClearly, nature has been very effective in creating organisms that are capable of protecting themselves against a wide variety of pathogens such as bacteria, fungi, and parasites. The powerful information-processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors that researchers are finding very useful for the development of computational models. While some of these models are designed to give us a better understanding of the immune system, other models are being developed to solve complex real-world problems such as anomaly detection, pattern recognition, data analysis (clustering), function optimization, and computer security. Immunological Computation: Theory and Applications is devoted to discussing different immunological mechanisms and their relation to information processing and problem solving. This unique volumTable of ContentsImmunology Basics. Modeling the Biological Immune System. Negative Selection. Artificial Immune Networks. Clonal Selection Algorithm and Hybrid Models. Applications.

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Book SynopsisIntelligent 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.Table of ContentsReactive Machines. Neural Networks. Machine Evolution. State Machines. Robot Vision. Search in State Spaces. Agents that Plan. Uninformed Search. Heuristic Search. Planning, Acting and Learning. Alternative Search. Knowledge Representation and Reasoning. The Propositional Calculus. The Predicate Calculus. Knowledge-based Systems. Representing Common sense Knowledge. Reasoning with Uncertain Information. Learning and Acting with Bayes Nets. Planning Methods Based on Logic. The Situation Calculus. Planning. Communication and Integration. Multiple Agents. Communication Among Agents. Agent Architectures.

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Book SynopsisThe sheer complexity of computer systems has meant that automated reasoning, i.e. the ability of computers to perform logical inference, has become a vital component of program construction and of programming language design. This book meets the demand for a self-contained and broad-based account of the concepts, the machinery and the use of automated reasoning. The mathematical logic foundations are described in conjunction with practical application, all with the minimum of prerequisites. The approach is constructive, concrete and algorithmic: a key feature is that methods are described with reference to actual implementations (for which code is supplied) that readers can use, modify and experiment with. This book is ideally suited for those seeking a one-stop source for the general area of automated reasoning. It can be used as a reference, or as a place to learn the fundamentals, either in conjunction with advanced courses or for self study.Trade Review'Contemporary research in computer science has produced an abundance of formal methods designed to enable hardware and software systems to reason correctly, and to enable us to reason better about these systems. Indeed, the explosion of research and specialised techniques can make it hard for students and newcomers to enter the field. John Harrison's Handbook of Practical Logic and Automated Reasoning is a significant addition to the expository literature on the subject, and will serve as a valuable resource for beginners and experts alike.' Theory and Practice of Logic Programming'John Harrison … has written what clearly will be the book about automation in theorem proving. People often ask me whether they should buy this book. My answer … always is: yes, of course you should buy this book. It is a masterpiece.' Journal of Automated ReasoningTable of ContentsPreface; Ideological orientation; Acknowledgements; How to read this book; 1. Introduction; 2. Propositional logic; 3. First-order logic; 4. Equality; 5. Decidable problems; 6. Interactive theorem proving; 7. Limitations; Appendix 1. Mathematical background; Appendix 2. OCaml made light of; Appendix 3. Parsing and printing of formulas; References; Index.

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Book SynopsisThis 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.

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Book SynopsisThis book gives legal practitioners, academics, and law students a comprehensive look at the main impacts of artificial intelligence use in legal practice. Contributors identify the main challenges surrounding a legally compliant and ethical development of AI and craft a framework for analyzing the costs and benefits of new technology.Table of Contents1. Lawyering in the Digital Age Pietro Ortolani and Larry A. DiMatteo; Part I. Effects of Technology on Legal Practice: 2. Disruptive effects of legal tech Larry A. DiMatteo, Jiang Christine Jiaying and Robert Thomas; 3. The effects of technology on legal practice: from punch card to artificial intelligence? Andrė Janssen and Tom J. Vennmanns; 4. Legal drafting and automation Benjamin Werthmann; 5. Emerging rules on artificial intelligence: Trojan horses of ethics in the realm of law? Florian Möslein and Maximilian Horn; Part II. Legal Tech and ADR: 6. Legal tech in ADR Mateja Durovic and Franciszek Lech; 7. A blockchain-based smart dispute resolution method Alessandro Palombo, Raffaele Battaglini and Luigi Cantisani; 8. Digital dispute resolution: blurring the boundaries of ADR Pietro Ortolani; Part III. Legal Tech in Consumer Relations and Small Claims 9. Legal tech in consumer relations and small-value claims: a survey Francisco de Elizalde; 10. Regulation of legal services and access to justice in the digital age: a war report Jin-Ho Verdonschot and Max Houben; 11. Legal tech and EU consumer law Martin Ebers; 12. The two faces of legal tech in B2C relations Eric Tjong Tjin Tai; Part IV. Legal Tech and Public Law: 13. Blockchain's heterotopia: technological infrastructures and lawyering in the public sector Georgios Dimitropoulos; 14. Fundamental rights and the use of artificial intelligence in court Jean-Marc van Gyseghem; 15. Legal tech in public administration: prospects and challenges Antonios Kouroutakis; Part V. Legal Ethics and Societal Values Confront Technology: 16. Ethics guidelines for trustworthy AI Michel Cannarsa; 17. Ethical digital lawyering: technical and philosophical insights Mathieu Guillermin, Arnaud Billion, Carine Copain-Héritier and Emmanuel de Vaujany; 18. Law, disintermediation, and the future of trust Christoph Kletzer; Part VI. Fate of the Legal Professions: 19. Lawyering somewhere between computation and the will to act: a digital age reflection Jeffrey M. Lipshaw; 20. Surviving the digital transformation – a method for lawyers to approach legal tech Paw Fruerlund and Sebastian Peters; 21. Road forward: promise and danger Larry A. DiMatteo and Pietro Ortolani.

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Book SynopsisWith an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domains where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology and economics.Table of ContentsList of illustrations; Preface; Summary of key notation; 1. Introduction; 2. Games in normal form; 3. Games in extensive form; 4. Negotiation domains; 5. Strategic analysis of single-issue negotiation; 6. Strategic analysis of multi-issue negotiation; 7. The negotiation agenda; 8. Multilateral negotiations; 9. Heuristic approaches; 10. Man-machine negotiations; 11. Axiomatic analysis of negotiation; 12. Applications; 13. Related topics; 14. Concluding remarks; Appendix A. Proofs; References; Index.

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Book SynopsisThis book develops a view of logic as a theory of information-driven agency and intelligent interaction between many agents - with conversation, argumentation and games as guiding examples. It will interest students and scholars in a wide variety of subject areas.Trade Review'Logical Dynamics [of Information and Interaction] is at the frontiers of applied logic. This is an essential book for any student of the subject, written by a master of the field.' Dov Gabbay, King's College London'… this book is the best we can have for now as a great source for the research in the field of logical dynamics of information and interaction. It can be used as a handbook of DEL as well. I think the author has succeeded in demonstrating a new view of logic as a theory of information flow in the interaction of agents.' Yanjing Wang, Studia LogicaTable of ContentsPreface; 1. Logical dynamics, agency, and intelligent interaction; 2. Epistemic logic and semantic information; 3. Dynamic logic of public observation; 4. Multi-agent dynamic-epistemic logic; 5. Dynamics of inference and awareness; 6. Questions and issue management; 7. Soft information, correction, and belief change; 8. An encounter with probability; 9. Preference statics and dynamics; 10. Decisions, actions, and games; 11. Processes over time; 12. Epistemic group structure and collective agency; 13. Logical dynamics in philosophy; 14. Computation as conversation; 15. Rational dynamics in game theory; 16. Meeting cognitive realities; 17. Conclusion; Bibliography.

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Book SynopsisThis text provides an exceptional introduction to the multidisciplinary field of mobile robotics using hands-on examples in ROS 2 enabling students to explore concepts either in a simulation or using their own robot hardware. The new edition includes coverage of HRI, robot ethics, and AI techniques for end-to-end robot control.Trade Review'This book is an indispensable tool for any - both pre-university and university - course on mobile robotics. In relation to the first edition, this current one has been sufficiently updated. I recommend this book to researchers - particularly those who study localization or mapping - and doctoral students who are interested in investigating the latest approaches and techniques in the mobile robotics field.' Ramon Gonzalez Sanchez, Computing Reviews'… a great resource for an intermediate or advanced course on mobile robotics.' R. S. Stansbury, ChoiceTable of ContentsAcknowledgments; Preface; 1. Overview and motivation; 2. Fundamental problems; Part I. Locomotion and Perception: 3. Mobile robot hardware; 4. Non-visual sensors and algorithms; 5. Visual sensors and algorithms; Part II. Representation and Planning: 6. Deep learning for robots; 7. Planning in, representing and reasoning about space; 8. System control; 9. Pose maintenance and localization; 10. Mapping and related tasks; 11. Robot collectives; 12. Human-robot interaction; 13. Robot ethics; 14. Robots in practice; 15. The future of mobile robotics; Appendix A. Fictional robots; Appendix B. Probability and statistics; Appendix C. Linear systems, matrices and filtering; Appendix D. Markov models; Bibliography; Index.

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Book SynopsisThe field of artificial intelligence (AI) and the law is on the cusp of a revolution that began with text analytic programs like IBM''s Watson and Debater and the open-source information management architectures on which they are based. Today, new legal applications are beginning to appear and this book - designed to explain computational processes to non-programmers - describes how they will change the practice of law, specifically by connecting computational models of legal reasoning directly with legal text, generating arguments for and against particular outcomes, predicting outcomes and explaining these predictions with reasons that legal professionals will be able to evaluate for themselves. These legal applications will support conceptual legal information retrieval and allow cognitive computing, enabling a collaboration between humans and computers in which each does what it can do best. Anyone interested in how AI is changing the practice of law should read this illuminating wTrade Review'In relation to the composition of this book, it provides a comprehensive and user-friendly description of this interdisciplinary area, focusing on the suitability of developing legal devices based on artificial intelligence. The structure of the work allows users to analyse how representation of legal logic knowledge occurs, and its suitability for computational implementations … On this matter, the author provides relevant and understandable illustrations that facilitate the linkage between theory and the development of the techno legal implementations. … Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age is a fundamental work for those of us who are interested in the intersection between intelligent technology and the legal field, and its promising future.' Jesus Manuel Niebla Zatarain, SCRIPTedTable of ContentsPart I. Computational Models of Legal Reasoning: 1. Introducing AI and Law and its role in future legal practice; 2. Modeling statutory reasoning; 3. Modeling case-based legal reasoning; 4. Models for predicting legal outcomes; 5. Computational models of legal argument; Part II. Legal Text Analytics: 6. Representing legal concepts in ontologies and type systems; 7. Making legal informational retrieval smarter; 8. Machine learning with legal texts; 9. Extracting information from statutory and regulatory texts; 10. Extracting argument-related information from legal case texts; Part III. Connecting Computational Reasoning Models and Legal Texts: 11. Conceptual legal information retrieval for cognitive computing; 12. Cognitive computing legal apps.

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Book SynopsisFor fans of computers and comedy alike, an accessible and entertaining look into how we can use artificial intelligence to make smart machines funny.Most robots and smart devices are not known for their joke-telling abilities. And yet, as computer scientist Tony Veale explains in Your Wit Is My Command, machines are not inherently unfunny; they are just programmed that way. By examining the mechanisms of humor and jokes--how jokes actually works--Veale shows that computers can be built with a sense of humor, capable not only of producing a joke but also of appreciating one. Along the way, he explores the humor-generating capacities of fictional robots ranging from B-9 in Lost in Space to TARS in Interstellar, maps out possible scenarios for developing witty robots, and investigates such aspects of humor as puns, sarcasm, and offensiveness. In order for robots to be funny, Veale explains, we need to analyze humor computationally. Using artific

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Book SynopsisAn in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions.  The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload chara

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Book SynopsisHow to stay in charge in a world populated by algorithms that beat us in chess, find us romantic partners, and tell us to “turn right in 500 yards.”Doomsday prophets of technology predict that robots will take over the world, leaving humans behind in the dust. Tech industry boosters think replacing people with software might make the world a better place—while tech industry critics warn darkly about surveillance capitalism. Despite their differing views of the future, they all seem to agree: machines will soon do everything better than humans. In How to Stay Smart in a Smart World, Gerd Gigerenzer shows why that’s not true, and tells us how we can stay in charge in a world populated by algorithms.Machines powered by artificial intelligence are good at some things (playing chess), but not others (life-and-death decisions, or anything involving uncertainty). Gigerenzer explains why algorithms often fail at finding us romantic part

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