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Generative AI for Students
Using Generative Artificial Intelligence as a student can be confusing and challenging. This authoritative guide equips you with practical knowledge, enabling you to leverage Generative AI as a powerful study aid and embark on your academic journey with confidence. Exploring a wide spectrum of topics, from demystifying jargon and fostering critical thinking to addressing diversity, inclusion and academic integrity, this book is an essential companion for any Higher Education student looking to enhance their academic skill set and achieve success in their studies. By the end of this book you will:- Become critically literate at using Generative AI- Learn how to use Generative AI to enhance traditional study methods- Encounter real-life case studies and worked-through examples to put theory into practice- Gain a comprehensive grounding in how to engage with Generative AI Tools
R 880.00
Generative AI for Students
Using Generative Artificial Intelligence as a student can be confusing and challenging. This authoritative guide equips you with practical knowledge, enabling you to leverage Generative AI as a powerful study aid and embark on your academic journey with confidence. Exploring a wide spectrum of topics, from demystifying jargon and fostering critical thinking to addressing diversity, inclusion and academic integrity, this book is an essential companion for any Higher Education student looking to enhance their academic skill set and achieve success in their studies. By the end of this book you will:- Become critically literate at using Generative AI- Learn how to use Generative AI to enhance traditional study methods- Encounter real-life case studies and worked-through examples to put theory into practice- Gain a comprehensive grounding in how to engage with Generative AI Tools
R 3,438.80
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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Humanising Language Education in the Generative Artificial Intelligence Age
Examining the presence and influence of GenAI in the contexts of English for Academic Purposes (EAP), and written composition within university-level English as a foreign language (EFL) programmes, this book asserts a humanist view of language education informed by Critical Realism and the social realist philosophy of education.This author looks at the GenAI-language education interaction through explicit philosophical engagement, with the aim of strengthening a critical and emancipatory approach to EAP, written composition, and language education. By highlighting technological determinism and relating it to broader neoliberalist mechanisms, it provides new insights into the social, political, economic, cultural and historical contexts within which the use of GenAI technologies by language teachers and learners can be understood.Humanising Language Education in the Generative Artificial Intelligence Age will appeal to students and scholars of linguistics, education and sociology as well as organizations devoted to understanding the evolution, application, and impact of GenAI on education and language learning.
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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.
R 4,559.00
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.
R 8,894.00
Gen AI-assisted Agile Course, Project-Based Teaching and Learning
This title represents an essential resource for understanding the evolving landscape of AI-augmented pedagogy, examining the transformative effects of generative artificial intelligence (GenAI) on education through the lens of agile course design and project-based learning.Through a detailed analysis of diverse empirical case studies from eight countries between 2015 and 2024, the authors effectively illustrate the dual impact of GenAI on education, academic integrity, and teacher autonomy. They demonstrate how GenAI can enhance personalized STEM education and STREAM (Science, Technology, Reading and Writing, Engineering, Arts and Mathematics) instruction, as well as project-based learning, while simultaneously presenting significant challenges. They provide a nuanced assessment of the opportunities and disruptions introduced by GenAI and offer concrete pedagogical strategies for teaching practitioners.This book serves as a vital reference for scholars in GenAI, educational technology and curriculum, and instruction. It also provides practical, ready-to-implement pedagogical approaches for pre-service teachers in higher education and primary and secondary schools.
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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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Information Access in the Era of Generative AI
Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people’s personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction.The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, the sociotechnical implications of GenAI for information access, recommendations within GenIR, and the future of information access with GenIR.The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.
R 8,251.14
AI in Popular Culture
This book examines the transformative impact of generative AI on cultural production, with a particular focus on how AI technologies like ChatGPT are reshaping the creation of popular culture.Through a comprehensive analysis of both Global North and South perspectives, the book offers critical insights into AI governance, ethical considerations, and the future of cultural production in an increasingly AI-integrated creative landscape. Tracing the evolution of AI in cultural industries, from its early representations in films to its current active role in content creation across various media, including film, television, music, and animation, the book explores how AI has shifted from being merely depicted in cultural works to becoming an integral tool in the creative process, from pre-production to post-production stages. It critically analyzes three core concepts—creativity, control, and credentials (3Cs)—in the context of AI-driven cultural production, putting focus on the examination of "precarious creativity," the tension between human creative expression and AI-generated content. The work addresses crucial concerns about labor rights, job security, and creative control in the cultural industries.This book will interest graduate students and researchers in media studies, cultural studies, anthropology, globalization studies, sociology, and science and technology studies (STS), as well as interested policymakers and practitioners interested in AI's impact on the creative industries.
R 9,299.98
R 306.00
Generative AI
Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI’s ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research to think about ethical implications and balance it with innovation to benefit everyone. The author covers the technological, product, and societal impact of Generative AI to give a comprehensive guide for this important technology. This book will help the data scientist, product manager, and business leader alike because it covers the API economy and critical industries impacted and offers a framework to think about automation and jobs impacted and what competencies are needed to excel in GenAI and offers an in-depth guide to build GenAI products and applications.
R 5,890.24
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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Integration Strategies of Generative AI in Higher Education
Amidst the rapid evolution of educational technology, a pressing challenge confronts higher education institutions: how to effectively integrate generative artificial intelligence (AI) into their existing frameworks. As universities strive to adapt to the digital age, they are met with the complexities of incorporating AI-driven solutions to enhance teaching, learning, and administrative processes. However, the lack of comprehensive strategies and guidance hinders their ability to leverage AI's full potential, leaving educators and administrators grappling with uncertainty. In response to this critical dilemma, Integration Strategies of Generative AI in Higher Education emerges as a guide for clarity and innovation. By offering methodological insights and practical frameworks, this book equips higher education stakeholders with the tools needed to navigate the intricacies of AI integration. From curriculum enhancement to AI-driven content creation, the book provides actionable strategies tailored to the unique needs and challenges of higher education institutions. As educators, administrators, and policymakers seek to harness the transformative power of AI in education, this book serves as an indispensable roadmap for success. By synthesizing best practices from diverse educational contexts and addressing key considerations such as regulatory frameworks and ethical implications, this book empowers stakeholders to embrace AI with confidence, ushering in a new era of innovation and excellence in higher education.
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