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R 428.00
Understanding Generative AI in a Cultural Context: Artificial Myths and Human Realities
The interplay between artificial intelligence (AI) and cultural dynamics is reshaping communication practices in the digital era, highlighting how technology and culture influence each other. Generative AI technologies, deeply embedded in cultural contexts, are transforming how individuals and societies interact, share ideas, and create meaning. Understanding these interactions requires diverse methodologies and perspectives to capture the complexity of AI's role in cultural evolution. This exploration helps illuminate the broader societal impact of AI, offering insights into its potential to shape communication and cultural practices worldwide. Understanding Generative AI in a Cultural Context: Artificial Myths and Human Realities explores the nuanced interactions between AI, cultural dynamics, and communication practices within the digital landscape. It offers a multidisciplinary examination of how generative AI technologies are reshaping the contours of digital life, focusing on how these technologies are influenced by cultural contexts and how they, in turn, influence cultural and communicative practices. Covering topics such as AI Influencers, human-AI interaction, and storytelling, this book is an excellent resource for academicians, researchers, scholars, students, policymakers, technologists, industry professionals, librarians, and more.
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Kubernetes for Generative AI Solutions
Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples.Key FeaturesBuild and deploy your first Generative AI workload on Kubernetes with confidenceLearn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automationGain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloadsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management.This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience.By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learnExplore GenAI deployment stack, agents, RAG, and model fine-tuningImplement HPA, VPA, and Karpenter for efficient autoscalingOptimize GPU usage with fractional allocation, MIG, and MPS setupsReduce cloud costs and monitor spending with Kubecost toolsSecure GenAI workloads with RBAC, encryption, and service meshesMonitor system health and performance using Prometheus and GrafanaEnsure high availability and disaster recovery for GenAI systemsAutomate GenAI pipelines for continuous integration and deliveryWho this book is forThis book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.
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Predictive Analytics and Generative AI for Data-Driven Marketing Strategies
The book provides an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions. It includes real-world case studies and examples from diverse marketing domains, demonstrating how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. It showcases emerging trends in predictive analytics and generative AI for marketing, and potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to effectively use predictive analytics and generative AI for marketing strategies.• Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data.• Includes generative AI techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making.• Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context.• Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions.• Present the recent advances in predictive analytics and generative AI, discuss how they can affect the area of data-driven marketing.
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AI Value Creators
We've arrived in a new era-GenAI is reshaping industries and decision-making processes across the board. As a result, understanding their potential and pitfalls has become crucial. But in order to stay ahead of the curve, you'll need to develop fresh perspectives on leveraging AI beyond mere technical know-how. Geared toward business leaders and tech professionals alike, this book demystifies the strategic integration of AI into business practices, ensuring you're equipped not just to participate but to lead in this new landscape.This insightful guide by industry leaders Rob Thomas, Paul Zikopoulos, and Kate Soule goes beyond the basics, offering real-life success stories and learned lessons to provide a blueprint for meaningful AI engagement. Whether you're a novice or a seasoned expert, you'll come away with an enhanced understanding of GenAI.Recognize the transformative potential of AI in business and how to harness itNavigate the ethical and operational challenges posed by AI with confidenceUnderstand the dynamic interplay between AI technology and business strategyImplement actionable strategies to integrate AI into your organizational cultureStep confidently into the role of an AI value creator, equipped to lead and innovate
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Microlearning with GenAI
This Element explores the innovative integration of microlearning and generative artificial intelligence (GenAI) in second-language education. Designed for language teachers, teacher trainers, and future educators, it offers research-informed insights and actionable strategies for designing learner-centred, technology-enhanced instruction. The Element introduces the evolution and applications of GenAI in language learning, examines its synergy with microlearning, and presents pedagogical frameworks rooted in SLA theory, cognitive load theory, and multimodal learning. Practical guidance is provided for designing, implementing, and evaluating AI-assisted microlearning tasks, focusing on interactivity, accessibility, and ethical use. The Element also explores GenAI's role in teacher professional development and considers future trends shaping language education. While celebrating the transformative potential of GenAI, it critically engages with the challenges and limitations of emerging technologies. Through a balanced blend of theory and practice, this Element equips educators with the knowledge and tools to navigate and harness the evolving AI-enhanced language learning landscape.
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Machine Learning and Generative AI in Smart Healthcare
The healthcare landscape is constantly evolving, and one of the most significant concerns that healthcare professionals deal with is understanding how to use biomedical intelligence to improve patient outcomes. With the increasing complexity of healthcare computing systems, including technologies like deep learning and the Internet of Things, it can be challenging to navigate these advancements. Machine Learning and Generative AI in Smart Healthcare is a practical tool for healthcare professionals, researchers, and policymakers who are seeking to implement biomedical intelligence solutions. It provides a clear roadmap for using prescriptive and predictive analytics in machine learning to enhance healthcare outcomes. Going beyond the basics, it delves into healthcare computing and networking complexities. By delving into topics such as data mining, disease prediction, and AI applications, deep learning approaches, decision support systems, and optimization techniques, this book equips readers with the practical knowledge they need to optimize healthcare delivery and management.
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Challenges and Applications of Generative Large Language Models
Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.
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Generative Artificial Intelligence in Healthcare
Generative artificial intelligence (AI) is a transformative force in smart healthcare. It can produce contents virtually indistinguishable from human-created material, with the power to redefine healthcare and revolutionize how medical science interacts with technology. This book presents the potential applications and benefits of generative AI in healthcare and discusses its execution challenges and ethical aspects.Generative Artificial Intelligence in Healthcare: Current Practices and Future Development starts by reviewing the past and then discussing the present of generative AI-based systems and the potential challenges the future might bring. It then highlights the integration of generative AI with IoT and blockchain technologies along with offering case studies. The book explores how to integrate augmented and virtual reality into generative AI-based healthcare and covers the development of an effective machine algorithm for generative AI-based healthcare, as well as the design and implementation of generative adversarial networks (GANs) in healthcare systems.Discussions about data security and ergonomic design for healthcare products are also included in the book. This book will provide a platform for the past, present, and future of generative AI and will help researchers and healthcare practitioners generate more robust solutions.
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Impacts of Generative AI on Creativity in Higher Education
Many educators in the realm of higher education face the critical challenge of fostering creativity in students using traditional teaching methods. In today's rapidly evolving world, these methods have become inadequate to nurture the innovative thinking demanded by modern society. Impacts of Generative AI on Creativity in Higher Education reveals a solution in the integration of generative AI into higher education. To revolutionize how we nurture and harness student creativity, the book explores the intersection of creativity, generative AI, and higher education with a fresh perspective and practical guidance for educators and institutions. It delves into the fundamental concepts of generative AI and its potential applications, providing educators with the tools to create more engaging and innovative learning environments. This book comprehensively examines the role of generative AI in higher education to inspire interdisciplinary collaboration and dialogue among researchers and educators. It offers novel pedagogical approaches that leverage generative AI to foster creativity and provides a roadmap for the future of education. This timely publication is essential reading for educators, students, scholars, and managers looking to understand and harness the transformative power of generative AI in higher education.
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Generative Methods for Social Media Analysis
This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks.The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified.The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications.
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Quantum Generative Adversarial Networks
GANs represent a generative model leveraging the growing power of deep learning (DL) algorithms. This book presents the breakthroughs, applications and latest research innovations of GANs and of quantum GANs. In particular, the book includes contributions on Autoencoders and Variational Autoencoders, Semi-Supervised GANs, Quantum GANs and Quantum Data Privacy, and Quantum GANs for image manipulation.
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Parametric and Generative Design with Rhino Python and Grasshopper
Learn how to create simple to complex parametric models with Rhino Python and Grasshopper (GH) in your form finding and design exploration. Tap into the generative power of Rhino via Grasshopper’s intuitive drag-n-drop visual scripting interface and combine GH nodes with Python to generate virtually any geometry.You’ll start by working through a series of bite-sized exercises to get up-to-speed with basics like creating curves and surfaces, utilizing input widgets, working with lists and data trees, and editing geometry with operations like loft, pipe, and extrude. Then you’ll elevate your skills by learning how to generate popular effects like attractors, tessellations, spirals andloops, stacking and voxelizing, cracks and wireframes, plus their variations. After getting comfortable with GH essentials, in the second half of the book you’ll work through five complex application workshops—(1) Generating basket and textile weaved versions of a 3D model; (2) Creating sliceforms from meshes by cutting interlocking cross sections at various angles and intervals; (3) Making tiles, lithophanes, and molds by generating Breps from images, and projecting them onto other objects as decorative elements; (4) Generating fractals and tree-like canopies from L-systems, and (5) Creating city skyline models from building footprints (GIS data). With five applications under your belt, you’ll wrap up the book by learning how to package Rhino Python and Grasshopper scripts into plugins suitable for online publication.Whether you are interested in architecture, landscape, industrial, product, jewelry, fashion, or naval design, Parametric & Generative Design with Rhino Python and Grasshopper will add many tools to your generative repertoire.You Will Learn to· Generate a wide range of parametric models using Grasshopper and Python scripting· Produce models from images and project them onto other objects as decorative elements· Source building footprints (GIS) data and generate city skyline models from themWho Is This Book ForThis book is for Rhino users who want to learn Grasshopper and Python for parametric design and form finding; some basic knowledge of Python is helpful but not required
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Critiquing Generative AI in Africa’s Media Ecosystems
This book takes a critical approach to the rise of Artificial Intelligence (AI) within the media and communication landscape.The book argues that as technology is a social construct reflecting existing power dynamics, those in the Global South, and in Africa in particular, are bound to be marginalised and have their knowledge, systems overlooked as the Global North dominates technological developments. The birth of AI has ignited debates about its benefits to lower income countries, with some highlighting its potential to advance businesses and grow economies. However, this book argues that AI tools have been produced to serve Western needs, and that instead there is a need for countries in Africa to have home grown solutions defined by local values. Drawing on case studies and analysis from across the continent, the book seeks to caution, challenge, and proffer solutions to the use and understanding of Generative AI for media and communication in non-western societies.At a time when generative AI is threatening to shape the future of media and communication around the world, this book is a vital and timely contribution to debates within critical data studies and African media studies.
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Primer on Generative Adversarial Networks
This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics.The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more.By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.
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Generative Artificial Intelligence and Fifth Industrial Revolution
In this digital era, artificial intelligence (AI) is emerging as a catalyst for transformation across numerous fields, ranging from, economics, environment, finance to healthcare, AI's integration into daily operations and strategic planning presents a pivotal shift towards data-driven decision-making and automation. Each chapter in this volume addresses a unique aspect of AI, from theoretical frameworks and technological advancements to practical applications and ethical considerations. This volume not only highlights the advancements and applications of AI but also addresses the critical challenges of bias, privacy, and ethical implications associated with AI deployment. Through a multidisciplinary approach, it aims to provide readers with a nuanced understanding of AI's role in modern society and its potential to address some of the most pressing challenges of our times. As we stand on the brink of technological revolutions, this volume serves as a guide and a critical examination of the potential pathways AI might forge in the future. It is an essential read for academics, industry professionals, policymakers, and anyone interested in the profound changes AI is poised to bring. This is a multi-author book, but not a collection of essays. In fact, although signed by different authors, all the chapters of the book follow a line of development which is traced in the first part of the book and deepen the various aspects in logical order.The various parts of the book will explore the most important features of AI and analyse the implications of AI in Economics, Law, Policies, Smart Citizens and Territorial Aspects.
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