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Generative AI and Generative AI of Things for Sustainable Smart Cities
This pioneering book invites readers on a compelling journey into Generative Artificial Intelligence (AI) and its groundbreaking convergence with AI of Things (AIoT), introducing GAIoT as a transformative frontier in urban computing and intelligence. GAIoT signals a paradigm shift towards more intelligent, self-learning, self-evolving, and context-aware systems—capable of generating adaptive, forward-looking solutions to the complex infrastructuraland environmental challenges confronting sustainable smart cities.With its combination of theoretical depth, applied innovation, and interdisciplinary scope, the book offers a comprehensive examination of deep generative models—namely Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Transformers, and hybrid architectures—and their applications in environmental sustainability, climate resilience, infrastructure optimization, dynamic decision-making, and data-driven urban management and planning. From synthetic data generation, data augmentation, and data imputation to predictive modeling and scenario simulation, GAIoT is driving the next wave of climate-responsive, environmentally conscious smart city innovation.What sets this book apart is its first-of-its-kind focus on GAIoT as a pathbreaking force in shaping the future of sustainable urban development. It delivers actionable insights, conceptual and operational frameworks, case studies, and policy guidance—equipping diverse stakeholders with the tools to build cities that not only respond to change but also anticipate and shape it. Targeting a broad and cross-disciplinary audience, the book shares state-of-the-art research, presents innovative solutions, and forecasts future trends in urban transformation. As both a seminal reference and a practical resource for researchers, technologists, practitioners, professionals, and policymakers, it provides essential guidance for those engaged in advancing the next frontier in urban computing and intelligence.
R 3,636.34
RAG-Driven Generative AI
Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedbackGet With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey FeaturesImplement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agentsDeliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphsBalance cost and performance between dynamic retrieval datasets and fine-tuning static dataBook DescriptionRAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs.This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize your project’s performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs.You’ll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.What you will learnScale RAG pipelines to handle large datasets efficientlyEmploy techniques that minimize hallucinations and ensure accurate responsesImplement indexing techniques to improve AI accuracy with traceable and transparent outputsCustomize and scale RAG-driven generative AI systems across domainsFind out how to use Deep Lake and Pinecone for efficient and fast data retrievalControl and build robust generative AI systems grounded in real-world dataCombine text and image data for richer, more informative AI responsesWho this book is forThis book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you’ll find this book useful.
R 1,433.00
Building Business-Ready Generative AI Systems
Supercharge your business with context-aware AI controllers, adaptive agents, multimodal reasoning functionality, neuroscientific memory systems, and flexible handler mechanisms that integrate the emerging generative AI models. Get with your book: PDF copy, AI Assistant, and Next-Gen Reader free.Key FeaturesBuild an adaptive, context-aware AI controller with advanced memory strategiesEnhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT)Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fitBook DescriptionStandalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build agentic, ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context engineering.You'll design AI controller architectures with multi-user memory retention to dynamically adapt your system to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation system with Pinecone to combine instruction-driven scenarios. Through context engineering, you’ll minimize token usage, maximize response quality, and create systems that reason across complex tasks with precision. You'll enhance your system’s intelligence with multimodal capabilities—image generation, voice interactions, and machine-driven reasoning—leveraging Chain-of-Thought and context chaining to address cross-domain automation challenges. You'll also integrate OpenAI’s suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem.With context engineering as the backbone, every step becomes a deliberate act of shaping model behavior. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, and connect to live external data, all wrapped in a polished, investor-ready interface.What you will learnImplement an AI controller with a conversation AI agent and orchestrator at its coreBuild contextual awareness with short-term, long-term, and cross-session memoryDesign cross-domain automation with multimodal reasoning, image generation, and voice featuresExpand a CoT agent by integrating consumer-memory understandingIntegrate cutting-edge models of your choice without disrupting your existing GenAISysConnect to real-time external data while blocking security breachesWho this book is forThis book is for AI and Machine Learning Engineers seeking to enhance their understanding of Generative AI and its enterprise applications. It will particularly benefit those interested in building AI agents, creating advanced orchestration systems, and leveraging AI for automation in marketing, production, and logistics. Software architects and enterprise developers looking to build scalable AI-driven systems will also find immense value in this guide. No prior superintelligence experience is necessary, but familiarity with AI concepts is recommended.
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Applied Generative AI for Beginners
This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI.Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You’ll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.What You Will LearnGain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google BardImplement large language models using Sklearn, complete with code examples and best practices for real-world applicationLearn how to integrate LLM’s in enterprises, including aspects like LLMOps and technology stack selectionUnderstand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insightsWho This Book Is ForData scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.
R 1,832.00
Databricks Certified Generative AI Engineer Associate Study Guide
As generative AI becomes central to modern data and machine learning workflows, earning the Databricks Certified Generative AI Engineer Associate certification signals both credibility and technical fluency. This study guide prepares you for this sought-after certification with comprehensive coverage of core GenAI and LLM concepts, practical data management using Delta Lake, and retrieval-augmented generation (RAG) application workflows on the Databricks platform. Written by AI expert Rajaniesh Kaushikk, this book combines hands-on labs, real-world examples, and exam-aligned content to help you build and deploy effective GenAI applications. From prompt engineering and RAG-based solutions to model governance with Unity Catalog, MLflow tracking, and leveraging Hugging Face models in GenAI workflows, this guide supports both your certification journey and real-world AI development. Understand the fundamentals of generative AI, LLMs, and vector databasesPrepare and manage large-scale data using Delta Lake and the data lakehouseBuild and deploy generative AI models with Databricks and MLflowExplore RAG for enhanced model performanceImplement responsible AI practices and model governance with Unity Catalog
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ML and Generative AI in the Data Lakehouse
In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform
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Generative AI: Techniques, Models and Applications
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from:Clear explanations of advanced concepts.Practical implementation insights.Current industry applications.Ethical framework discussions.Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
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Generative AI for Software Development
In just a few short years, AI has transformed software development, and snazzy new tools continue to arrive, with no let-up in sight. How, as a software engineer, product builder, or CTO, do you keep up? This practical book is the result of Sergio Pereira's mission to test every AI tool he could find and provide practitioners with much-needed guidance through the commotion.Generative AI for Software Development focuses on AI tool comparisons, practical workflows, and real-world case studies, with each chapter encompassing critical evaluations of the tools, their use cases, and their limitations. While product reviews are always relevant, the book goes further and delivers to readers a coherent framework for evaluating the tools and workflows of the future, which will continue to complicate the work of software development.Learn how code generation and autocompletion assistants are reshaping the developer experienceDiscover a consistent method for rating code-generation tools based on real-world coding challengesExplore the GenAI tools transforming UI/UX design and frontend developmentLearn how AI is streamlining code reviews and bug detectionReview tools that are simplifying software testing and QAExplore AI for documentation and technical writingUnderstand how modern LLMs have redefined what chatbots can do
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Building Generative AI Agents
The dawn of AI agents is upon us. Tech visionaries like Bill Gates, Andrew Ng, and Vinod Khosla have highlighted the monumental potential of this powerful technology. This book will provide the knowledge and tools necessary to build generative AI agents using the most popular frameworks, such as AutoGen, LangChain, LangGraph, CrewAI, and Haystack. Recent breakthroughs in large language models have opened up unprecedented possibilities. After years of gradual progress in machine learning and deep learning, we are now witnessing novel approaches capable of understanding, reasoning, and generating content in ways that promise to revolutionize nearly every industry. This platform shift is as significant as the advent of mainframes, PCs, cloud computing, mobile technology, and social media. It's why the world's largest technology companies - like Microsoft, Apple, Google, and Meta - are making enormous investments in this category. While chatbots like ChatGPT, Claude, and Gemini have demonstrated remarkable potential, the years ahead will see the rise of generative AI agents capable of executing complex tasks on behalf of users. These agents already exhibit capabilities such as running test suites, searching the web for documentation, writing software, answering questions based on vast organized information, and performing intricate web-based tasks across multiple domains. They can autonomously investigate cybersecurity incidents and address complex customer support needs. By integrating skills, knowledge bases, planning frameworks, memory, and feedback loops, these systems can handle many tasks and improve over time. Building Generative AI Agents serves as a high-quality guide for developers to understand when and where AI agents can be useful, their advantages and disadvantages, and practical advice on designing, building, deploying, and monitoring them. What You Will Learn The foundational concepts, capabilities, and potential of AI agents.Recent innovations in large language models that have enabled the development of AI agents.How to build AI agents for launching a product, creating a financial plan, handling customer service, and using Retrieval Augmented Generation (RAG).Essential frameworks for building generative AI agents, including AutoGen, LangChain, LangGraph, CrewAI, and Haystack.Step-by-step guidance on designing, building, and deploying AI agents.Insights into the future of AI agents and their potential impact on various industries. Who This Book Is For Experienced software developers
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Educational Assessments in the Age of Generative AI
The rapid and profound permeation of generative AI technology into all aspects of modern society also changes the landscape of higher education and thus leads to a compelling call to harness the power of AI for transforming higher education. One of the most significant areas of opportunity offered by generative AI is in the assessment of learning. The goal of assessment is to foster learning especially essential for success beyond the classroom, and this can only be possible with well-designed assessments that have the potential for determining whether students have learned the content. By harnessing generative AI, assessments can be designed that have the potential to support inquiry-based learning and foster creativity, all essential for the development of a deeper conceptual understanding of content. Educational Assessments in the Age of Generative AI contributes to the effort to bring generative AI to the forefront of assessing students' learning by exploring how the use of generative AI tools and applications can transform and revolutionize assessment practices. Furthermore, it is devoted to exploring the use of AI in assessments to measure knowledge, skills and students' abilities in order to prepare them for careers in the 21st century. Covering topic including academic integrity, higher education, and mathematics education, this book is an excellent resource for educators, higher education administrators, policymakers, information technology support professionals, tests and assessment developers, researchers, scholars, academicians, professionals, and more.
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Making Art With Generative AI Tools
In the dynamic realm of generative artificial Intelligence (AI), the fusion of human creativity and machine intelligence has created a vibrant ecosystem of collaborative artmaking. However, this transformative process brings forth a myriad of concerns, ranging from ethical considerations and the need for originality to navigating the legal complexities surrounding intellectual property. As more and more online communities appear around the use of AI to aid in the creation of images, there arises a pressing need for a comprehensive guide that not only dissects the intricacies of artmaking with generative AI tools but also offers practical solutions to the evolving dilemmas faced by artists, researchers, and technologists. Making Art With Generative AI Tools emerges as an exploration of the challenges posed by this intersection of human expression and artificial intelligence. Artists engaging with generative AI find themselves grappling with issues of authenticity, social toxicity, and the commercial viability of their creations. From avoiding stereotypical visuals to ensuring proper crediting, the realm of generative AI is rife with these complexities. Furthermore, the blurred lines between human and machine authorship necessitate a deeper exploration of how these innovative tools impact creativity, representation, and the very fabric of the art world. Tailored for a diverse audience of researchers, artists, and instructional designers, the book navigates through the intricacies of large language models, provides case studies on the usage of generative AIs, and offers practical guidance on creating visuals for storytelling, teaching, and learning. With a keen focus on fostering human creativity, promoting ethical practices, and addressing the legal nuances of this evolving landscape, the book serves as an indispensable compass for those navigating the uncharted waters of artmaking with generative AI tools.
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Generative Artificial Intelligence Empowered Learning
This book explores the integration of Generative Artificial Intelligence (GenAI), such as ChatGPT, into educational practices and research methodologies. With the potential to transform traditional learning environments, this book addresses both the opportunities and challenges that come with leveraging GenAI for enhancing teaching and learning experiences.This comprehensive guide delves into multiple aspects of GenAI in education. It covers critical topics such as project-based learning (PBL), personalized learning through AI, AI-driven student assessment, and the role of AI in special education. Furthermore, it examines ethical considerations, the readiness of students and teachers for AI technologies, and case studies on AI’s impact on student outcomes. With insights from educators, researchers, and practitioners from around the globe, the book provides a balanced perspective on both theoretical frameworks and practical implementation of GenAI technologies.Ideal for educators, researchers, and policymakers, this book serves as a practical resource for those looking to effectively integrate AI into their pedagogical and research efforts. It is an essential reference for anyone interested in staying at the forefront of educational innovation and exploring the future possibilities of AI-empowered learning.
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Designing Instruction with Generative AI
Designing Instruction with Generative AI offers a novel set of tools and strategies for leveraging generative AI to create engaging and personalized learning experiences. While instructional designers are a tremendous asset to higher education, not all colleges or universities have the robust staff needed to support all instructors on staff or large student populations. Drawing on a wealth of research, professional experience, and strategic insights, this book equips new and seasoned teaching faculty and trainers with step-by-step directions on how freely accessible artificial intelligence software can assist with all aspects of the course creation and instruction process and cater to the needs of diverse learners. Each chapter offers forward-thinking and empirically validated ways to help faculty create and improve instructional materials, course design, and learning environments while supporting their digital literacies. Rather than introduce AI as a means of outsourcing subject-area expertise, critical thinking, or cognitive processes, the author instead emphasizes its potential to build on traditionally honed knowledge and foundational instructional design practices. From optimizing course alignment and accessibility practices to fostering active learning, motivation, and engagement, educators will find new solutions to common teaching and learning challenges with greater efficiency in time and capacity.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.
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Teaching and Learning in Higher Education with Generative AI
This interdisciplinary book demonstrates how Generative Artificial Intelligence (GenAI) changes higher education across diverse academic fields, including philology, management, engineering, and psychology. Drawing on empirical case studies, surveys, and experiments, the book combines theoretical and practical perspectives to explore AI’s impact on learning, and cognitive development. Each chapter offers discipline-specific analysis of how GenAI reshapes pedagogy – supporting multilingual education, enhancing reflective thinking, fostering self-regulated learning, and encouraging ethical awareness. By comparing case studies from varied academic and cultural contexts, the book highlights the opportunities and challenges of AI adoption, emphasizing responsible, creative, and critically informed approaches to academic innovation. It will appeal to researchers of AI in Higher Education across disciplines.Key Features:- Explains how Generative AI transforms higher education across a variety of disciplines, from logistics to psychology.- Includes practical and theoretical insights into AI’s role in reshaping pedagogy, fostering critical thinking, and supporting multilingual education.- Provides a global perspective on the opportunities and challenges of AI adoption through diverse case studies and empirical research.
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Deep Swarm and Evolution for Generative Artificial Intelligence
This book provides theoretical and practical knowledge about swarm and evolutionary approach of generative AI and Large Language Models (LLMs). The development of such tools contributes to better optimizing methodologies with the integration of several machinelearning and deep learning techniques. In particular, it discusses how the “emergence” concept can contribute to the improvement of AI.The book aims to model human cognitive f unction in terms of “emergence” and to explain the feasibility of AI. To this end, it focuses on human perceptions of “utility.” It describes the emergence of various cognitive errors, and irrational behaviours in the multiobjective situations. It also reviews the cognitive differences and similarities between humans and LLMs. Such studies are important when applying LLMs to real-world tasks that involve human cognition, e.g., financial engineering and market issues.The book describes the intelligent behaviour of living organisms. This is to clarify how to achieve AI in the direction of artificial life. It describes sexual selection, which is a well-known natural phenomenon that troubled Darwin, i.e., why evolutionarily useless items evolved such as peacock feathers and moose antlers etc. The book shows how sexual selection is extended as “novelty search” for the application of generative AI, i.e., the image generation with diffusion model. Real-world applications are emphasised. Empirical examples from real-world data show how the concept of deep swarm and evolution is successfully applied when addressing tasks from such recent fields as robotics, e-commerce Web Shop and image generation etc.
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Generative AI: Innovations and Applications Across Industries
This book explores Generative AI, covering its transformative applications and innovative technologies. Each chapter meticulously examines Generative AI's impact across diverse domains, including creative fields, healthcare, education, entertainment, finance, and more.The book begins by establishing the foundation for comprehending Generative AI, presenting fundamental concepts, tracing its historical progression, and analyzing key technologies like deep neural networks (DNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs) in Chapter 1. In Chapter 2 we explore Generative AI's role in revolutionizing creative domains like writing, visual arts, and music composition. Chapter 3 delves into its impact on business and marketing, reshaping strategies and operations through AI-generated content and personalized experiences. In Chapter 4, we investigate Generative AI's influence on healthcare, accelerating drug discovery, personalized medicine, and medical imaging. Education is spotlighted in Chapter 5, showcasing Generative AI's contributions to personalized learning and interactive educational materials. Chapter 6 examines its impact on entertainment and media, while Chapters 7 through 11 delve into legal, financial, industrial, and environmental applications, addressing ethical considerations and future trends. Finally, Chapter 12 concludes with case studies and expert insights, offering readers a comprehensive understanding of Generative AI's potential and societal implications. Join us on this enlightening journey through the diverse landscapes of Generative AI, where innovation knows no bounds and the future is limited only by our imagination.In the end, readers will gain a comprehensive understanding of the transformative potential and societal implications of Generative AI.What you will learn:What are the foundational concepts and technological advancements behind Generative AI.What are large language modelsUnderstand Generative AI's role in reshaping business landscapes.What is the future of Generative AI and its potential impact. Who this book is for:AI Professionals, machine learning engineers and software professionals seeking a comprehensive understanding of Generative AI across various business domains.
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Generative AI
From algorithms that draft clinical notes in seconds to autonomous agents that triage emergency room backlogs, artificial intelligence is quietly reshaping every facet of healthcare. Generative AI: Unlocking the Next Chapter in Healthcare is the first comprehensive guide to this revolution—charting how we got here, what’s working now, and where the technology could take us next.Drawing on frontline case studies from leading health systems, biotech labs, and tech giants, the book reveals how large language models, synthetic data engines, and multi agent ecosystems are boosting diagnostic accuracy, accelerating drug discovery, and personalizing patient engagement. It also confronts the hard questions—privacy, bias, liability, and the fear of “machines taking over”—offering clear, actionable frameworks for ethical, human centered deployment.Written for clinicians, executives, innovators, and policy makers and patients alike, this definitive volume distils the expertise of industry pioneers into an accessible roadmap. You’ll explore:• The evolution from early machine learning to today’s generative powerhouses• Real world wins—ambient documentation, AI assisted radiology, virtual nursing, and more• The players shaping the landscape: Epic, Google, Microsoft, NVIDIA, Salesforce, and a wave of insurgent startups• Guardrails that preserve trust, protect privacy, and keep humans in the loop• Bold yet plausible futures—from digital twin medicine to globe-spanning agent networksWhether you’re leading a hospital, building health tech solutions, a tech startup, or simply curious about the future of care, Generative AI: Unlocking the Next Chapter in Healthcare delivers the insight, inspiration, and practical guidance you need to thrive in the era where human empathy and machine intelligence work side by side.
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Generative AI, Cybersecurity, and Ethics
"Generative AI, Cybersecurity, and Ethics is an essential guide for students, providing clear explanations and practical insights into the integration of generative AI in cybersecurity. This book is a valuable resource for anyone looking to build a strong foundation in these interconnected fields."—Dr. Peter Sandborn, Professor, Department of Mechanical Engineering, University of Maryland, College Park "Unchecked cyber-warfare made exponentially more disruptive by Generative AI is nightmare fuel for this and future generations. Dr. Islam plumbs the depth of Generative AI and ethics through the lens of a technology practitioner and recognized AI academician, energized by the moral conscience of an ethical man and a caring humanitarian. This book is a timely primer and required reading for all those concerned about accountability and establishing guardrails for the rapidly developing field of AI."—David Pere, (Retired Colonel, United States Marine Corps) CEO & President, Blue Force Cyber Inc. Equips readers with the skills and insights necessary to succeed in the rapidly evolving landscape of Generative AI and cyber threats Generative AI (GenAI) is driving unprecedented advances in threat detection, risk analysis, and response strategies. However, GenAI technologies such as ChatGPT and advanced deepfake creation also pose unique challenges. As GenAI continues to evolve, governments and private organizations around the world need to implement ethical and regulatory policies tailored to AI and cybersecurity. Generative AI, Cybersecurity, and Ethics provides concise yet thorough insights into the dual role artificial intelligence plays in both enabling and safeguarding against cyber threats. Presented in an engaging and approachable style, this timely book explores critical aspects of the intersection of AI and cybersecurity while emphasizing responsible development and application. Reader-friendly chapters explain the principles, advancements, and challenges of specific domains within AI, such as machine learning (ML), deep learning (DL), generative AI, data privacy and protection, the need for ethical and responsible human oversight in AI systems, and more. Incorporating numerous real-world examples and case studies that connect theoretical concepts with practical applications, Generative AI, Cybersecurity, and Ethics: Explains the various types of cybersecurity and describes how GenAI concepts are implemented to safeguard data and systemsHighlights the ethical challenges encountered in cybersecurity and the importance of human intervention and judgment in GenAIDescribes key aspects of human-centric AI design, including purpose limitation, impact assessment, societal and cultural sensitivity, and interdisciplinary researchCovers the financial, legal, and regulatory implications of maintaining robust security measuresDiscusses the future trajectory of GenAI and emerging challenges such as data privacy, consent, and accountability Blending theoretical
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Generative AI for Business Analytics and Strategic Decision Making in Service Industry
Generative AI is transforming industries by enhancing how businesses analyze data, generate content, and automate complex processes. Its ability to produce diverse, high-quality outputs in areas like marketing, customer engagement, and software development is reshaping productivity and innovation. By leveraging advanced models such as GANs and Transformers, organizations can unlock new insights, streamline decision-making, and personalize services at scale. Generative AI also plays a crucial role in detecting errors, managing knowledge, and mitigating harmful content, contributing to safer and more efficient digital environments. This technology not only accelerates growth but also democratizes access to powerful tools, fostering creativity and problem-solving across sectors. Generative AI for Business Analytics and Strategic Decision Making in Service Industry provides a comprehensive exploration of how generative AI techniques can revolutionize business intelligence and data analytics practices. It offers a detailed examination of innovative algorithms, methodologies, and tools for leveraging generative AI to extract actionable insights, drive innovation, and enhance decision-making processes within the service industry. Covering topics such as big data, generative adversarial networks (GANs), and productivity management, this book is an excellent resource for students, academicians, industry professionals, researchers, managers, and more.
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Generative AI with LangChain
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applicationsKey FeaturesBridge the gap between prototype and production with robust LangGraph agent architecturesApply enterprise-grade practices for testing, observability, and monitoringBuild specialized agents for software development and data analysisPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy.Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learnDesign and implement multi-agent systems using LangGraphImplement testing strategies that identify issues before deploymentDeploy observability and monitoring solutions for production environmentsBuild agentic RAG systems with re-ranking capabilitiesArchitect scalable, production-ready AI agents using LangGraph and MCPWork with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-miniDesign secure, compliant AI systems aligned with modern ethical practicesWho this book is forThis book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
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Generative Artificial Intelligence for Biomedical and Smart Health Informatics
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disordersBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systemsTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoringEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
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Procedural Content Generation via Machine Learning
This second edition updates and expands upon the first beginner-focused guide to Procedural Content Generation via Machine Learning (PCGML), which is the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content. The authors survey current and future approaches to generating video game content and illustrate the major impact that PCGML has had on video games industry. In order to provide the most up-to-date information, this new edition incorporates the last two years of research and advancements in this rapidly developing area. The book guides readers on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis. The authors discuss the practical and ethical considerations for PCGML projects and demonstrate how to avoid the common pitfalls. This second edition also introduces a new chapter on Generative AI, which covers the benefits, risks, and methods for applying pre-trained transformers to PCG problems.
R 1,649.00
Generative AI in Higher Education: Ethical Governance, Skills, and Employability
Building upon the foundational principles established in the first volume, this second volume of Generative AI in Higher Education advances the debate on AI beyond theoretical frameworks toward concrete institutional practice. Where the first volume developed guiding principles for higher education institutions to use GenAI effectively and ethically in teaching and learning, this volume introduces original, research-informed frameworks that redefine employability as an ethical, adaptive, and human-centred practice. It presents distinctive models including Skills Dynamics, the Justice–Responsibility Framework for Human Flourishing, and the AI Dialogic Partner frameworks that move decisively from principles to practice.The book integrates ethical governance, curriculum design, and skills formation through actionable models such as the Double ERL Framework (Ethical Regulation Literacy and Ethical Reflexive Learning), the ERL Justice Framework, and the Institutional AI Stewardship Lab (IASL) model. Uniquely combining philosophical depth with policy relevance, it bridges global and local perspectives—drawing on UNESCO, OECD, and regional policy frameworks—to address the ethical, social, and regulatory dimensions of AI in higher education. The volume includes comprehensive international policy analysis, diverse global case studies, and practical tools that transform conceptual exploration into implementable guidance.Generative AI in Higher Education: Ethical Governance, Skills, and Employability serves as essential reading for university leaders, policymakers, educators, and employers seeking to align AI innovation with justice, inclusion, and sustainability. Its practical frameworks and transparency principles empower institutions to position education not merely as a response to technological change but as a transformative force for human flourishing, providing both scholarly contribution and actionable roadmap for embedding ethical AI in higher education practice.
R 5,165.00
R 540.00
Generative AI in R
Leverage Generative AI within the R programming environment and prepare for future directions and how new innovations can be applied in the R ecosystem. This pioneering book is designed to bridge the gap between the advanced realms of Generative AI and the practical, statistical computing power of R. You'll begin with an introduction to Generative AI principles and its significance in the current data-driven landscape. You'll then dive into the practicalities of implementing generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in R. See how R, most known for its statistical analysis, can also be used for creative synthetic data, improving model robustness, and generating innovative insights from data. Additionally, this book addresses the demand for ethical AI by emphasizing the use of synthetic data to tackle privacy and data scarcity issues--concerns particularly relevant in healthcare, finance, and social research. We are at a pivotal moment in the evolution of AI and data science. With AI's growing importance, the book's focus on R makes advanced techniques more accessible, promoting ethical and innovative data science practice, preparing readers for upcoming trends. What You Will Learn Grasp the core concepts of Generative AI and its significance in the broader AI landscape. Implement various generative models in R, such as GANS and VAEs. Generate high-quality synthetic data. Apply advanced techniques for improving efficiency and effectiveness of models for different applications. Understand Gen AI ethical considerations. Who This Book Is For Data scientists and statisticians with intermediate R programming skills who want to expand into Generative AI for data analysis and problem-solving. AI enthusiasts and data analysts looking to apply Generative AI techniques in R to enhance their analytical capabilities.
R 912.00
RAG-Driven Generative AI
Building MAS-RAG (multi-agent AI systems for RAG) that reason over real-world data using hybrid retrieval and scalable architectures for production use.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster DualRAG by combining vector search with SQL filtering over structured enterprise dataImplement GraphRAG, Spatial-RAG, and vector search natively in Oracle Database 23aiBuild multimodal video pipelines with human-feedback loops and fine-tuned modelsBook DescriptionStop moving your data to the AI. This second edition defines a revolutionary architectural shift: bringing the AI to the data. By using Oracle Database 23ai as a converged engine in this book, you will architect Sovereign AI systems that eliminate the fragmentation, latency, and massive security risks inherent in traditional data extraction.You’ll work with DualRAG, synchronizing unstructured vector semantics with the deterministic truth of structured SQL, Graph, and Spatial retrieval. This allows your systems to reason over verified corporate data rather than probabilistic guesses, reducing hallucinations at the source. Moving beyond simple pipelines, you’ll also build MAS-RAG (multi-agent systems for RAG), where autonomous agents coordinate across hybrid retrieval workflows, multimodal video pipelines, and graph-based knowledge structures.Designed for developers and architects, these blueprints transform disconnected data silos into a unified engine to architect autonomous enterprise intelligence that scales with RLHF and model fine-tuning. By the end of the book, you’ll be able to design and deploy enterprise AI systems that combine retrieval, reasoning, and structured data to build reliable generative AI applications.*Email sign-up and proof of purchase requiredWhat you will learnBring intelligence directly to the data within Oracle Database 23aiDefeat hallucinations and data poisoning with DualRAG, synchronizing vector semantics with structured SQLBuild MAS-RAG pipelines with Planner, Agent Registry, and MCP-standardized sovereign agentsEngineer an inference-time router using hybrid adaptive RAG to switch between reasoning, retrieval, and human feedbackFuse vector similarity, Oracle Spatial, and SQL Property Graph traversal into a converged hyper-queryMultimodal video RAG with version-controlled schema registry and semantic vector search over visual assetsWho this book is forThis book is for AI engineers, ML engineers, data scientists, and MLOps professionals who want to build production-ready generative AI systems grounded in enterprise data. It will also benefit solutions architects, database engineers, and software developers looking to integrate large language models with structured and unstructured data sources using modern retrieval architectures. Readers should be comfortable with Python and have a basic understanding of machine learning concepts. Prior experience with generative AI or vector databases will help you get the most ou
R 1,791.00
Cambridge Handbook of Generative AI and the Law
This handbook offers an important exploration of generative AI and its legal and regulatory implications from interdisciplinary perspectives. The volume is divided into four parts. Part I provides the necessary context and background to understand the topic, including its technical underpinnings and societal impacts. Part II probes the emerging regulatory and policy frameworks related to generative AI and AI more broadly across different jurisdictions. Part III analyses generative AI's impact on specific areas of law, from non-discrimination and data protection to intellectual property, corporate governance, criminal law and more. Part IV examines the various practical applications of generative AI in the legal sector and public administration. Overall, this volume provides a comprehensive resource for those seeking to understand and navigate the substantial and growing implications of generative AI for the law.
R 8,246.80
R 1,301.00
Proceedings of International Conference on Generative AI, Cryptography and Predictive Analytics
The book presents the proceedings of the International Conference on Generative AI, Cryptography and Predictive Analytics (ICGCPA 2024), held at VIPS-TC, School of Engineering and Technology, Pitampura, Delhi, India, during June 28 – 29, 2024. It covers Generative AI's role in problem-solving, examining applications in image synthesis, content creation, healthcare, and optimization challenges. This book is a valuable resource for postgraduate students in various engineering disciplines.
R 12,523.78
Practical Guide to Generative AI Using Amazon Bedrock
This comprehensive guide gives you the knowledge and skills you need to excel in Generative AI. From understanding the fundamentals to mastering techniques, this book offers a step-by-step approach to leverage Amazon Bedrock to build, deploy, and secure Generative AI applications. The book presents structured chapters and practical examples to delve into key concepts such as prompt engineering, retrieval-augmented generation, and model evaluation. You will gain profound insights into the Amazon Bedrock platform. The book covers setup, life cycle management, and integration with Amazon SageMaker. The book emphasizes real-world applications, and provides use cases and best practices across industries on topics such as text summarization, image generation, and conversational AI bots. The book tackles vital topics including data privacy, security, responsible AI practices, and guidance on navigating governance and monitoring challenges while ensuring adherence to ethical standards and regulations. The book provides the tools and knowledge needed to excel in the rapidly evolving field of Generative AI. Whether you're a data scientist, AI engineer, or business professional, this book will empower you to harness the full potential of Generative AI and drive innovation in your organization. What You Will Learn Understand the fundamentals of Generative AI and Amazon BedrockBuild Responsible Generative AI applications leveraging Amazon BedrockKnow techniques and best practicesSee real-world applicationsIntegrate and manage platformsHandle securty and governance issuesEvaluate and optimze modelsGain future-ready insightsUnderstand the project life cycle when building Generative AI Applications Who This Book Is For Data scientistys, AI/ML engineers and architects, software developers plus AI enthusiasts and studenta and educators, and leaders who want to evangelize within organizatios
R 1,254.00
R 611.00
R 580.00
