Search results for "Generative AI System Design"
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Generative AI for Connected and Autonomous Vehicles
Harness generative AI for connected and autonomous vehicle systems As generative AI reshapes intelligent transportation, engineers and researchers need rigorous frameworks for deploying these technologies in safety-critical systems. This book offers a comprehensive treatment of the models, protocols, and adaptation techniques required to bring large language models, vision-language models, diffusion models, and agentic systems into modern CAV architectures. The book progresses from generative foundations through multimodal representation, language-based interaction, agentic coordination, communication protocols, model adaptation, and validation and safety. Mathematical, probabilistic, and reinforcement-learning prerequisites are consolidated into dedicated appendices, allowing the main chapters to focus on system design and CAV-specific applications. Written by an award-winning researcher with more than 100 patents across connected vehicles and AI, readers will also find: Practical treatment of prompt engineering, retrieval-augmented generation, function calling, and tool use for grounding GenAI in vehicular contextChapters on agent architectures, multi-agent systems, orchestration frameworks, and MCP and A2A protocols for collaborative CAV intelligenceAdaptation and deployment techniques including supervised fine-tuning, reinforcement learning fine-tuning, and knowledge distillation for resource-constrained edge platformsDedicated chapters on simulation platforms, evaluation methodologies, and ethical and safety considerations for deploying generative AI in autonomous vehicles Automotive engineers, AI researchers, and graduate students at the intersection of machine learning and intelligent transportation will find this book indispensable. Readers from OEMs, mobility startups, or academic labs gain the foundations and practices needed to design, adapt, and validate generative AI in connected mobility.
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Introduction to Generative AI
Introduction to Generative AI: Theoretical Foundations, Analysis and Practical Use CasesGenerative Artificial Intelligence is reshaping how we create, learn, and imagine. This book offers a clear and complete journey through the science and art behind this revolution — from how machines learn to produce text, images, and music, to how they are being built, tested, and applied in the real world.The first part of the book lays out the ideas that make generative AI possible — neural networks, transformers, diffusion models, and the mysterious “latent spaces” where creativity meets computation. The middle chapters explore how today’s AI systems reason, plan, and use tools, drawing on powerful frameworks such as LangChain, LangGraph, and AutoGen. The final section moves beyond algorithms, discussing how to build safe, fair, and energy-conscious AI that serves human goals rather than replacing them.Throughout, real-world examples bring the technology to life — from language models that write poetry, to multimodal systems that can paint or compose music, to intelligent agents that collaborate with humans in research, education, and design. Written in accessible language and supported by visual explanations, this book bridges deep technical ideas with human-centered understanding.HighlightsExplains the core principles of how generative models think and createShows how AI systems combine text, vision, and sound in unified waysConnects theory with hands-on applications and real-world use casesDiscusses ethics, creativity, and sustainability in the age of intelligent machinesWritten for learners, researchers, and professionals who want both clarity and depthIntroduction to Generative AI: Theoretical Foundations, Analysis and Practical Use Cases is an essential guide for anyone curious about how artificial intelligence is shaping the next chapter of human innovation.
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Generative AI for Communications Systems
Comprehensive review of state-of-the-art research and development in Generative AI for future communications and networking Generative AI for Communications Systems provides a systematic foundation of knowledge on Generative AI for communications and networking. This book discusses the great potential and challenges in applying Generative AI as promising solutions to future communications systems and enables and facilitates “Generative AI as a Service” by exploring novel communications, networking architectures, protocols, and research trends. The book also includes information on: Crucial challenges to solve in Generative AI, such as training data availability, computational complexity, generalization for various scenarios, robustness of noisy and incomplete data, and real-time adaptation in communications and networking systemsCybersecurity concerns such as ethics and privacy in relation to Generative AIApplications of Generative AI across various layers, including the PHY layer, MAC layer, Network layer, and Application layerCommunications and networking solutions to meet the computing and communications challenges and demands to train and deploy large-scale Generative AI models Generative AI for Communications Systems is an excellent up-to-date resource on the subject for scholars and researchers in the fields of communications, artificial intelligence, machine learning, and network optimization as well as professionals working in the communications industry including engineers, network architects, and system designers.
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Understanding the Ethics of Generative AI
The book discusses the ethical complexities that software developers face as they build AI systems capable of autonomous creation. It explores the ethical decisions developers must make when building generative AI systems, from mitigating bias in training data to protecting user privacy and navigating regulatory compliance. Through real-world case studies and actionable frameworks, it equips technical professionals with both the understanding and tools to build AI systems that are fair, transparent, and worthy of public trust.Identifies and corrects algorithmic bias in training datasets, ensuring AI systems produce equitable outputs that don't systematize discriminationDesigns privacy-first AI architectures and implements transparency practices that comply with data protection regulations while building user trustNavigates evolving legal and regulatory landscapes (GDPR, AI Act, sector-specific rules), helping teams stay ahead of compliance requirementsApplies ethical frameworks to real-world decisions: what to do when fairness and accuracy conflict, how to audit AI systems for hidden harms, when to say no to a projectProvides a governance model for embedding ethics into development workflows, not as an afterthought but as core design practice
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26th International Society for Design and Process Science Conference
The book presents the proceedings of the 26th Society for Design and Process Science Conference (SDPS 2025), which took place in St. Julian, Malta, December 7-10, 2025. The 2025 conference centers on generative artificial intelligence (GenAI) and its impact on medicine, academia, and industry. The papers focus on the generational challenges that GenAI presents, which industry leaders agree constitutes a sea change in technology not seen since the Industrial Revolution. The conference emphases that, as the AI/GenAI community creates vast new and novel opportunities at a rapid pace, it consequently creates challenges with lack of understanding and immature designs, processes, research, engineering, while also exposing gaps in formal AI/GenAI design and process education. The authors also discuss, for the sake of analysis, the general fear of unknown AI/GenAI technologies, as well as system ambiguity and disguised complexities. The authors aim to present solutions that help to mitigate risk in these in critical environments (e.g., medical, financial, cyber defense etc). To better serve developers and modern business leaders with guidance to innovate, mitigate risk, and capitalize on novel advancements in AI/GenAI, the Society for Design and Process Science (SDPS) brings together the advanced academic research community with industry to more qualitatively harness the power of formal AI/GenAI design and process to accelerate science and business.
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Loop Engineering for AI Agents
Design reliable agentic AI loops that act autonomously while remaining bounded, observable, independently verifiable, and accountable.Key FeaturesApply a minimum-sufficient-loop framework, adding complexity only when measurable failures justify itConnect agentic AI architecture with organizational design and verified-outcome economicsBuild reliable loops with independent verification, protected evidence, budgets, and escalationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDesigning reliable agentic AI systems requires more than writing the perfect prompt. The central challenge is designing the control system around a probabilistic worker: what it may do, what evidence proves completion, when it may retry, how much it may spend, what state must persist, and when a human must take over. This book provides a practical framework for addressing these challenges.Loop Engineering for AI Agents starts by defining loop engineering and separating it from prompt, context, and harness engineering. You'll examine how persistent AI loops change team structures, governance, management, and the economics of software production. The practical sections turn these principles into implementation through the Ralph fresh-context pattern, local loop architecture, protected verification, durable plans, retry and recovery policies, observability, prompt-injection boundaries, and human escalation.Written by Enrico Papalini, who has over 25 years of software engineering experience, the book provides a signal-first framework for adding only the mechanisms that justify their cost.By the end, you'll understand how to delegate autonomy through explicit completion contracts, independent evidence, bounded resources, authorization policies, reversible state transitions, and accountable human ownership.What you will learnDefine bounded completion contracts for agentic workSeparate workers from verifiers using external evidencePersist intent, state, lessons, and checkpoints outside model contextDesign retry, resume, rollback, and budget policiesProtect specifications, verifiers, and authorization policiesChoose between fresh-context loops and full orchestrationMeasure cost per accepted outcome instead of token usageDesign human-escalation policies for reliable autonomyWho this book is forEngineering leaders evaluating or scaling agentic development workflows, senior software engineers and architects designing AI-agent control systems, and platform and developer-experience engineers building internal AI infrastructure. AI, risk, security, and governance specialists working in controlled or regulated environments will also benefit. Familiarity with software engineering principles and AI-assisted development workflows is recommended.
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Multimodal Generative AI
This book stands at the forefront of AI research, offering a comprehensive examination of multimodal generative technologies. Readers are taken on a journey through the evolution of generative models, from early neural networks to contemporary marvels like GANs and VAEs, and their transformative application in synthesizing realistic images and videos. In parallel, the text delves into the intricacies of language models, with a particular on revolutionary transformer-based designs. A core highlight of this work is its detailed discourse on integrating visual and textual models, laying out state-of-the-art techniques for creating cohesive, multimodal AI systems. “Multimodal Generative AI” is more than a mere academic text; it’s a visionary piece that speculates on the future of AI, weaving through case studies in autonomous systems, content creation, and human-computer interaction. The book also fosters a dialogue on responsible innovation in this dynamic field. Tailored for postgraduates, researchers, and professionals, this book is a must-read for anyone vested in the future of AI. It empowers its readers with the knowledge to harness the potential of multimodal systems in solving complex problems, merging visual understanding with linguistic prowess. This book can be used as a reference for postgraduates and researchers in related areas.
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System Innovation in an AI-Driven World
As artificial intelligence fundamentally restructures contemporary socio-technical infrastructure, reductive technological paradigms fail to capture emerging institutional complexities. System Innovation in an AI-Driven World establishes a rigorous theoretical framework for transitioning from discrete algorithmic deployment to comprehensive systemic transformation. This volume equips scholars and strategic architects with the epistemological and methodological tools required to analyze, model, and govern complex socio-technical transitions under the influence of pervasive automation.The monograph interrogates the dialectical relationship between advanced computational architectures—specifically deep neural networks, autonomous multi-agent systems, and predictive modeling—and legacy institutional frameworks. Utilizing cross-disciplinary case studies from cyber-physical systems, public health logistics, and decentralized socio-ecological regimes, the text explicates the mechanics of systemic innovation within volatile technological landscapes. The foundational findings indicate that optimal AI integration is not a function of raw algorithmic efficacy; rather, it depends on systemic absorptive capacity, adaptive regulatory polycentricity, and dynamic feedback integration. The research offers structural typologies for mapping non-linear systemic feedback loops, mitigating algorithmic opacity, and synthesizing human-machine governance frameworks. Consequently, the volume demonstrates that sustainable institutional efficacy resides in embedding computational intelligence within fundamentally reconceptualized, non-linear system architectures.This volume is explicitly designed for academic researchers, systems theorists, computational sociologists, and advanced policymakers. It constitutes an indispensable resource for graduate-level seminars and senior scholars investigating the intersection of technology policy, complex adaptive systems, and institutional design amid the contemporary digital transition.
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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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Deep Generative Modeling
This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression, among others.Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It should find interest among students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics who wish to get familiar with deep generative modeling.In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site: github.com/jmtomczak/introdgmThe ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.
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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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Systemic Text Generation as Problem Solving
This study explores an approach to text generation that interprets systemic grammar as a computational representation. Terry Patten demonstrates that systemic grammar can be easily and automatically translated into current AI knowledge representations and efficiently processed by the same knowledge-based techniques currently exploited by expert systems. Thus the fundamental methodological problem of interfacing specialized computational representations with equally specialized linguistic representations can be resolved. The study provides a detailed discussion of a substantial implementation involving a relatively large systemic grammar, and a formal model of the method. It represents a fundamental and productive contribution to the literature on text generation.
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Integrating Generative AI in Education to Achieve Sustainable Development Goals
A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AI’s rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education. This book's primary objective is to serve as a resource for academicians, independent researchers, research scholars, students, scientists, libraries, and industry experts. Through a diverse range of topics, it investigates how generative AI can advance Sustainable Development Goals, making education more accessible, individualized, and effective. By harnessing the power of AI, the book argues, education can act as a catalyst for positive change, empowering individuals and fostering progress toward a sustainable and equitable future. The recommended topics within each part provide a structured framework for deepening the understanding of Generative AI's role in shaping the future of education, making this book a valuable reference for scholars and practitioners alike.
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Generative AI Security
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selectionIntelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generationDeepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integritySecurity attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacksPrivacy-preserving AI techniques including federated learning and homomorphic encryption Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.
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Oxford Handbook of the Foundations and Regulation of Generative AI
Generative AI technology holds immense transformative potential, with applications spanning from text generation to image creation and scientific discovery. Yet, its rapid development and adoption have introduced significant legal and ethical concerns, such as potential cybersecurity risks, misinformation, unsustainability, and algorithmic discrimination. The Oxford Handbook of the Foundations and Regulation of Generative AI offers theoretical insights and practical recommendations for ensuring the responsible development and application of this emerging technology.Edited and written by leading scholars in law, AI ethics, economics, and computer science, this Handbook brings together a global team of experts to examine the foundational principles and regulatory challenges posed by generative AI systems. The Handbook introduces core technical concepts-including explainable AI (XAI), agentic AI safety, and prompting-and discusses pressing legal and ethical concerns such as AI bias, consumer protection, platform and worker rights, data protection, copyright, and liability for AI-generated speech. The volume also considers how generative AI is transforming industries, from business and legal practice to healthcare, finance, and cybersecurity. Finally, it concludes with in-depth analyses of AI regulation in various jurisdictions, including the EU, US, China, Brazil, India, and Australia. Interdisciplinary in scope and global in coverage, this Handbook is an essential resource for anyone seeking to understand and navigate the evolving landscape of generative AI. It provides an accessible entry point for students, while offering the depth and rigor needed to inform practice, policy, and advanced research.
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Generative AI For Dummies
Generate a personal assistant with generative AI Generative AI tools capable of creating text, images, and even ideas seemingly out of thin air have exploded in popularity and sophistication. This valuable technology can assist in authoring short and long-form content, producing audio and video, serving as a research assistant, and tons of other professional and personal tasks. Generative AI For Dummies is your roadmap to using the world of artificial intelligence to enhance your personal and professional lives. You'll learn how to identify the best platforms for your needs and write the prompts that coax out the content you want. Written by the best-selling author of ChatGPT For Dummies, this book is the ideal place to start when you're ready to fully dive into the world of generative AI. Discover the best generative AI tools and learn how to use them for writing, designing, and beyondWrite strong AI prompts so you can generate valuable output and save time Create AI-generated audio, video, and imageryIncorporate AI into your everyday tasks for enhanced productivity This book offers an easy-to-follow overview of the capabilities of generative AI and how to incorporate them into any job. It's perfect for anyone who wants to add AI know-how into their work.
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Application of Generative AI in Healthcare Systems
Generative AI has immensely influenced various fields, such as education, marketing, art and music, and especially healthcare. Generative AI can benefit the patient through various approaches. For instance, it can enhance the image qualities negatively affected by radiation reduction, preventing patients from needing to repeat the image-taking process. Also, the generation of one type of image from another more expensive one can help patients save funds. Generative AI facilitates the administrative process, letting the doctor focus more on the treatment process. It even goes further by helping medical professionals with diagnosis and decision- making, suggesting possible treatment plans according to the patient symptoms. This book introduces several practical GenAI healthcare applications, especially in medical imaging, pandemic prediction, synthetic data generation, clinical administration support, professional education, patient engagement, and clinical decision support, providing a review of efficient GenAI tools and frameworks in this area. GenAI empowers the treatment process through several methods; however, some ethical, privacy, and security challenges require attention. Despite the challenges presented, GenAI technological and inherited characteristics smooth the path of improvement for it in the future.
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System-2 Reasoning: from Semantic Anchoring to Causal Intelligence, Volume 2
Large language models can write poetry, pass bar exams, and generate fluent code. Yet they continue to fail in the domains where intelligence must be accountable: distinguishing causation from correlation, recognizing when evidence is insufficient, preserving commitments over time,and correcting their own reasoning failures. This book argues that the transition from pattern matching to genuine reasoning requires a System-2 layer grounded in coordinated diagnosis, audit, causal validation, memory, and meta-cognitive control. The volume develops this architecture from first principles through operational protocols. Semantic Anchoring (UCCT) demonstrates how contextual constraints can bind latent model representations into governed reasoning processes rather than prior-driven completion. Regulated Causal Anchoring (RCA) and RAudit diagnose sycophancy, pathological skepticism, and trace-output inconsistency without relying exclusively on ground-truth supervision. The Causal Abstraction Bridge and the CausalTSK benchmark reveal where models collapse from interventional and counterfactual reasoning back into associative prediction. Epistemic Regret Minimization (ERM) identifies causal shortcuts and failures of warranted inference, while Reinforcement Learning from Epistemic Regret (RLER) transforms those reasoning failures into a structured learning signal. Trivium introduces temporal accountability through a Causal Transaction Log, and Quadrivium integrates contextual, causal, temporal, and meta-cognitive regulation into a unified System-2 MACI architecture. The author's central thesis is that Artificial General Intelligence (AGI) will not emerge from scaling monolithic pattern-completion systems alone. AGI will require architectures capable of explaining why an answer is warranted, refusing conclusions when evidence remains indeterminate, and improving through epistemic failure. Written for researchers, advanced students, and practitioners, this book presents a framework for AI systems that are not merely impressive, but auditable, corrigible, and trustworthy. It is suitable for graduate-level courses in artificial intelligence, multi-agent systems, causal reasoning, and trustworthy AI.
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Securing Uncontrolled Gen-AI Upload-Induced Data Leakage
The rapid growth of generative artificial intelligence (Gen-AI) is transforming industries such as healthcare, finance, education, law and software engineering. While Gen-AI enhances productivity and decision-making, it also introduces significant security, privacy and governance challenges, particularly through the unintended exposure of sensitive data. Securing Uncontrolled Gen-AI Upload-Induced Data Leakage examines the risks associated with uploading personal information, intellectual property, confidential communications and proprietary source code to AI systems. It explores key threats, including unsafe prompt engineering, shadow AI usage, prompt injection, model inversion, data poisoning and API misuse, which can result in data leakage, model memorization and regulatory violations. Through real-world case studies, forensic analysis and log tracing, this book demonstrates how sensitive information propagates across AI environments, and how such risks can be identified and mitigated. It also presents governance and forensic frameworks aligned with regulations such as GDPR, HIPAA and CCPA, providing practical guidance for secure, transparent and responsible AI adoption.
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Next-Generation Recommendation Systems
A detailed guide to building cutting-edge recommendation systems In Next-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits, a team of experienced technologists and educators, each with a proven track record in the field, delivers an expert guide to building robust recommendation systems that can interface with complex databases. The authors’ deep understanding of the subject matter is evident as they explain how to use the latest AI technologies, including LLMs, graph neural networks, diffusion models, and generative adversarial networks, to create recommendation engines that users enjoy and that drive business revenue. The book does not just delve into theoretical concepts, but also connects them to advanced implementation techniques. It demonstrates the application of practical and adaptable techniques, such as graph embeddings and Bayesian networks, to solve real-world problems faced by platform users and businesses. Readers will find the knowledge and tools to tackle these challenges head-on. Comprehensive coverage of practical generative AI techniques, including large language models and diffusion models Detailed exploration of graph neural networks and knowledge graph embeddings to solve common recommendation engine problems Practical guidance on implementing generative adversarial networks and variational autoencoders to address mode collapse and information bottleneck challenges In-depth analysis of hybrid recommendation architectures that combine content-based, collaborative, and knowledge-based filtering Real-world deployment strategies using cloud-native computing environments are not just theoretical concepts in this book. They are actionable strategies that have been tested and proven effective. This emphasis on real-world applicability will reassure readers about the book’s relevance to their professional or academic pursuits. Perfect for data scientists, AI specialists, software engineers, architects, and graduate students, Next-Generation Recommendation Systems is an essential, up-to-date resource for everyone involved in the design, deployment, and optimization of recommendation systems that connect to large, complex datasets.
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Smart Systems Design, Applications, and Challenges
Smart systems when connected to artificial intelligence (AI) are still closely associated with some popular misconceptions that cause the general public to either have unrealistic fears about AI or to expect too much about how it will change our workplace and life in general. It is important to show that such fears are unfounded, and that new trends, technologies, and smart systems will be able to improve the way we live, benefiting society without replacing humans in their core activities.Smart Systems Design, Applications, and Challenges provides emerging research that presents state-of-the-art technologies and available systems in the domains of smart systems and AI and explains solutions from an augmented intelligence perspective, showing that these technologies can be used to benefit, instead of replace, humans by augmenting the information and actions of their daily lives. The book addresses all smart systems that incorporate functions of sensing, actuation, and control in order to describe and analyze a situation and make decisions based on the available data in a predictive or adaptive manner. Highlighting a broad range of topics such as business intelligence, cloud computing, and autonomous vehicles, this book is ideally designed for engineers, investigators, IT professionals, researchers, developers, data analysts, professors, and students.
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Essentials of Generative AI
This book provides a concise yet comprehensive introduction to generative artificial intelligence.The first part explains the foundational technologies and architectures that support the realization of generative models. It covers evolved and deepened elements, word embeddings as a representative example of representation learning, and the Transformer as a network foundation, along with its underlying attention mechanism. Reinforcement learning, which became essential for elevating large-scale language models to language generation models, is also discussed in detail, focusing on essential aspects.The second part deals with language generation. It starts by elucidating language modelsand introduces large-scale language models with broad applications as the foundational architecture of language processing, further discussing language generation models as their evolution. Though not common terminology, in this book, models such as ChatGPT and Llama 2, which are large-scale language models fine-tuned using reinforcement learning, are referred to as generative language models.The third part addresses image generation, discussing variational autoencoders and the remarkable diffusion models. Additionally, it explains Generative Adversarial Networks(GAN). Although GAN poses challenges due to unstable learning, their conceptual framework is widely applicable, especially Wasserstein GAN seems suitable for introducing optimal trans- port distance, which is utilized in various scenarios.This book primarily serves as a companion for researchers or graduate students in machine learning, aiming to help them understand the essence of generative AI and lay the groundwork for advancing their own research.
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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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