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Beginner’s Guide to Generative AI
This book is the essential guide for anyone curious about AI's creative power. In the rapidly evolving landscape of artificial intelligence, generative AI stands out as one of the most transformative technologies of our time. Designed for beginners and requiring no prior knowledge of AI, this book breaks down the fundamentals of generative AI, from text and image generation to the workings of models like ChatGPT and Google Bard. The authors provide step-by-step coverage of the essential concepts and techniques that power generative AI. From the basics of how machines learn to generate text and images, to the intricate workings of models like Transformers, ChatGPT, and Google Bard, readers will gain a solid foundation in AI's most cutting-edge tools. Rather than focusing on a single method, the authors introduce a spectrum of generative modeling techniques, including diffusion models, variational autoencoders, and transformers. This comprehensive exposure ensures readers will be well-prepared to understand and adapt to the rapidly evolving AI landscape. In addition, real-world applications of generative AI across various industries are explored including healthcare innovations, business analytics, and legal technology, and the authors provide practical insights and examples that show how generative AI is revolutionizing these fields.
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Handbook of Generative AI in Education
Generative AI is transforming educational systems at a pace that challenges researchers, practitioners, and institutions to develop principled approaches to understanding, evaluating, and implementing these technologies responsibly. In response to this need, this volume synthesizes research on the use of Generative Artificial Intelligence (GAI) in education from leading global scholars across disciplines. The book explores effective methodologies and frameworks for advancing research and applications of GAI in higher education and related domains while addressing new possibilities for both the research and application of teaching, learning, and assessment. It examines the ways in which GAI can be used to transform education within the context of risks, ethical concerns, and other potential negative impacts, addressing the significant new challenges GAI may pose for instructors, students, administrators and researchers.Key areas of coverage include:• Foundational frameworks for understanding GAI systems and their implications for educational research and practice• Empirical evaluation of AI tutors, agentic systems, and automated feedback against rigorous learning science standards• Innovations in assessment that expand what can be measured and at what scale• Applications and findings across K–12, higher education, health sciences, and workforce training, with attention to equity and institutional implementation• Ethical implications of GAI use in educational research and applicationsThe Handbook of Generative AI in Education is an essential resource for researchers, professors, educators, and graduate students as well as professionals and practitioners in instructional design, educational technology, educational psychology, health science education, teacher education, and educational practice and policy.
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Unlocking Data with Generative AI and RAG
Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.What you will learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents' roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.
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Video Generation with AI
Video generation is rapidly becoming a key area in generative AI—combining spatial, temporal, and multimodal reasoning to produce moving images that are both coherent and creative. For many practitioners, however, understanding how these models function and implementing them remains a significant challenge. Video Generation with AI offers a straightforward guide for exploring this new terrain.Author Joseph Enochs leverages his experience leading enterprise AI projects to clarify how diffusion transformers, multimodal large language models, and spatiotemporal architectures combine to create high-quality video. Blending technical detail with practical examples, he demonstrates how to transition from isolated experiments to production-ready systems that transform creative fields, media, and human-machine collaboration.Understand the fundamental architectures of modern video generative modelsTrain and fine-tune models using diffusion transformers and multimodal encodersMaintain temporal coherence and consistency across complex scenes and sequencesApply generative video tools to creative, industrial, and scientific workflowsEvaluate, troubleshoot, and improve generated video quality at scale
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Designing Distributed Systems
Every distributed system strives for reliability, performance, and quality, but building such a system is hard. Establishing a set of design patterns enables software developers and system architects to use a common language to describe their systems and learn from the patterns and practices developed by others.The popularity of containers and Kubernetes paves the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help guide the systems you build using common patterns and practices drawn from some of the highest performing distributed systems in use today. These common patterns make the systems you build far more approachable and efficient, even if you've never built a distributed system before.Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system.This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.Understand how patterns and reusable components enable the rapid development of reliable distributed systemsUse the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machineExplore loosely coupled multinode distributed patterns for replication, scaling, and communication between componentsLearn distributed system patterns for large-scale batch data processing covering work queues, event-based processing, and coordinated workflows
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Foundations of Intelligent Systems
This book constitutes the proceedings of the 28th International Symposium on Methodologies for Intelligent Systems, ISMIS 2026, held in Lyon, France, during July 22–24, 2026.The 19 full papers and 5 industrial papers presented in this volume were carefully reviewed and selected from 44 submissions. The papers are organized in the following topical sections: Invited talks; Knowledge Representation and Formal Reasoning;Generative and Representation Learning; Reliability, Safety and Explainable AI; Industry Session; and Time Series and Stream Data.
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Generative AI for Cybersecurity
This book lays a systematic foundation for professionals, researchers, and industry readers who are interested the applications and implications of generative AI for cybersecurity. It covers the latest advances in generative AI and its applications, risks, and opportunities in cybersecurity.The authors first introduce the fundamental background of generative AI, the latest cybersecurity issues, and related potential applications in cybersecurity systems. Following this, they comprehensively review the state-of-the-art research and development, covering various aspects of generative AI applications in this area and related challenges and issues, such as training data availability, computational complexity, generalization to different scenarios, AI governance, quantum-empowered AI and many more. These discussions provide a strong understanding of recent advances in the two fields of generative AI and cybersecurity and the convergence of these domains, which will help readers to shape the field as it matures. Hands-on experiments presented throughout will also give them the practical skills for success. By leveraging its capabilities, readers can overcome challenges, understand the risks, enhance performance, and unlock new opportunities for handling the challenges of cybersecurity with generative AI. Consequently, they will be able to apply their knowledge to utilize generative AI in cybersecurity applications to prevent economic and other losses due to cyber risks such as phishing, fake news, deepfake-based fraud, and other cyberattacks.The contents of this book are appropriate for a wide range of readers from general readers to industry experts and scientists. Because it bridges the gap between generative AI and cybersecurity, experts from both fields will benefit from the information presented within. Students with a background in either area will also benefit from the approach that leads from general to specific applications.
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Generative AI for Cybersecurity
Generative AI for Cybersecurity explores how rapidly evolving generative models are reshaping modern digital defense. As organizations become more interconnected and data-driven, traditional cybersecurity measures are increasingly challenged by adaptive, AI-powered threats. Generative AI introduces new capabilities that can significantly enhance threat detection, automate security operations, and improve situational awareness, but it also enables sophisticated offensive techniques, deepfakes, automated malware generation, and large-scale misinformation.This book provides a balanced and comprehensive examination of this dual-use technology. It highlights how generative models can be leveraged to build resilient, intelligent, and proactive defense mechanisms capable of anticipating and countering emerging cyber risks. At the same time, it critically analyzes the vulnerabilities, ethical dilemmas, and regulatory challenges introduced by the misuse of generative AI. Through diverse perspectives and expert contributions, the book bridges theoretical foundations with real-world applications, demonstrating how GenAI can support adaptive intrusion detection, anomaly analysis, secure autonomous systems, and more transparent and explainable security solutions.Beyond technical considerations, the book addresses broader societal, geopolitical, and governance implications, including issues of trust, sovereignty, and responsible AI deployment. It offers frameworks, methodologies, and practical insights suitable for researchers, practitioners, students, and policymakers seeking to understand, develop, or regulate GenAI-driven cybersecurity systems.By examining both the opportunities and the risks, Generative AI for Cybersecurity serves as a timely reference for navigating an era where AI is not only a tool for defense but also a catalyst for new forms of cyber aggression, highlighting the urgent need for innovative, ethical, and resilient approaches to securing the digital world.
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Pervasive Intelligence – From Architectures to Sustainable Edge AI Systems-of-Systems
Artificial intelligence is rapidly moving beyond centralized cloud computing into distributed edge environments, creating a new generation of intelligent systems that are autonomous, adaptive, efficient, and sustainable. Pervasive Intelligence: From Architectures to Sustainable Edge AI Systems-of-Systems explores the technologies, architectures, and engineering methodologies driving this transformation.Written by leading researchers and industry experts, the book provides a comprehensive examination of edge AI, covering topics such as embedded AI acceleration, active inference agents, hardware-software co-design, distributed orchestration, privacy-preserving intelligence, and sustainable computing. It bridges theory and practice by addressing key deployment challenges, including real-time speech enhancement, neural network optimization for embedded devices, AI benchmarking on ARM processors, FPGA-based acceleration, and trustworthy AI systems operating at the edge.A recurring theme is the emergence of edge AI systems-of-systems, in which intelligent agents, sensors, devices, and computing resources collaborate seamlessly across the edge-to-cloud continuum. The book highlights the importance of interoperability, resilience, trustworthiness, adaptive autonomy, and energy efficiency in building next-generation intelligent infrastructures.Drawing on practical applications in robotics, autonomous systems, surveillance, smart agriculture, environmental forecasting, and cyber-physical systems, the contributors demonstrate how pervasive intelligence is transforming industries and enabling more responsive, data-driven decision-making. By combining advances in artificial intelligence with systems engineering, control theory, and physics-informed modeling, this volume offers both a strategic vision and a technical framework for the future of sustainable edge intelligence.An essential resource for researchers, engineers, system architects, and advanced students, this book provides the knowledge and tools needed to design, deploy, and manage intelligent systems operating at the edge.
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Introduction to AI Techniques for Renewable Energy System
Introduction to AI techniques for Renewable Energy SystemArtificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.Features Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systemsThis book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.
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Generative AI for Cybersecurity and Privacy
Generative AI for Cybersecurity and Privacy offers a groundbreaking exploration of how generative artificial intelligence is reshaping the landscape of cybersecurity and privacy protection in an era of rapid digital transformation. As cyber threats grow in sophistication and scale, this book provides a timely and authoritative guide to harnessing generative AI to safeguard digital ecosystems, secure sensitive data, and address emerging challenges across diverse domains.Spanning a series of expertly curated chapters, this volume delves into cutting-edge advancements and practical applications of generative AI in cybersecurity. It covers critical areas such as AI-driven threat detection and response, privacy-preserving AI models, secure IoT and cloud computing frameworks, and robust defenses for cyber-physical systems, including Smart Cities and wireless networks. The book balances rigorous theoretical foundations with real-world case studies, making it an essential resource for researchers, security professionals, policymakers, and organizational leaders.The book offers comprehensive coverage of key topics, including:• Leveraging generative AI for proactive threat detection, risk analysis, and automated incident response• Innovative approaches to data privacy, compliance, and governance in AI-driven systems• Advanced methodologies for securing IoT, mobile applications, and cloud infrastructures• Practical frameworks for integrating generative AI into cybersecurity strategies for critical infrastructures• Emerging applications of generative AI in personalized, secure digital experiences, such as e-commerce and smart systemsAuthored by a global team of leading researchers and practitioners, this book stands out by not only addressing current cybersecurity and privacy challenges but also proposing forward-thinking, scalable solutions powered by generative AI. Unlike traditional resources, it emphasizes the transformative potential of AI in revolutionizing risk analysis, threat mitigation, and privacy preservation across multiple domains. Whether you’re navigating the complexities of IoT, cloud security, or emerging cyber threats, Generative AI for Cybersecurity and Privacy equips you with the knowledge and tools to build intelligent, secure, and future-ready strategies for a dynamic digital world.
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Generative AI for Leaders Playbook
Lead the AI revolution with confidence. Learn how to craft strategy, build AI teams, and drive innovation responsibly with this practical leadership playbook for the Generative AI era.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild AI fluency and lead digital transformation with confidenceDesign enterprise-wide AI strategy, teams, and governance modelsUnlock growth with responsible and scalable Generative AI adoptionBook DescriptionGenerative AI has shifted from a technical breakthrough to a leadership imperative. This essential guide helps executives, directors, and tech leads navigate the transformation, bridging AI’s technical realities with enterprise applications and providing the frameworks and vocabulary needed to make informed decisions.Written for strategic leaders and decision-makers, this practical playbook demystifies key concepts, including generative AI, large language models (LLMs), and emerging agentic AI systems. It shows you how to develop AI strategies aligned with business goals, build AI-first organizations, and establish governance frameworks for responsible, compliant adoption. Real-world examples show how leading companies use AI to drive innovation, efficiency, and growth.Beyond strategy, you’ll learn how to build and scale AI teams, evaluate technology vendors, and integrate AI across business functions. You’ll also explore global policy trends, ethical implications, and the governance frameworks emerging across jurisdictions.This playbook provides a roadmap for turning AI disruption into sustainable competitive advantage, equipping executives with the strategic understanding and practical guidance needed to lead, implement, and scale AI responsibly.*Email sign-up and proof of purchase requiredWhat you will learnUnderstand generative and agentic AI capabilities and business valueBuild organizational AI literacy and cultural readinessCraft an enterprise AI strategy aligned with business goalsDesign and scale high-performing AI teams and structuresEvaluate AI platforms and vendors for transparency and reliabilityEmbed responsible AI governance and compliance frameworksWho this book is forThis book is for AI professionals, AI business leaders, founders, and senior managers who need to navigate the strategic, operational, and ethical challenges of AI adoption. It’s also invaluable for consultants, policy leaders, and transformation specialists advising enterprises on AI readiness. No coding knowledge is required—only a vision to lead AI-powered change across industries.
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Hybrid Mind
The Hybrid Mind: The Human-AI Convergence explores the transformative potential of humans and AI systems collaborating to revolutionize risk management, oversight, and adaptive governance. Introducing Agentic AI—systems capable of autonomous decision-making while preserving human authority—the book outlines a hybrid inference architecture that combines Large and Small Language Models, machine learning, and probabilistic reasoning.Structured around the OODAF (Observe, Orient, Decide, Act, Feedback) loop, the book offers accessible explanations of intelligent system behavior and its implications. Use cases in finance, healthcare, cybersecurity, and SaaS environments demonstrate how hybrid intelligence enables scalable yet accountable operations, providing practical insights for the AI era.It introduces original frameworks, including Human ETHOS, the Experimentum Crucis Technology Matrix (ECTM), and the Hybrid Audit Model (HAM), that reimagine ethical design, system evaluation, and real-time auditability. Chapters explore topics such as feedback architecture, ethical interruption, decision traceability, and context framing, critical elements for governing AI with integrity.Designed for business leaders, technologists, and policymakers, The Hybrid Mind serves as both a strategic guide and a moral blueprint for the AI era. It equips readers to lead in a world where intelligence is shared, automation is consequential, and responsibility must be architected, not assumed.
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Pioneering Applications of Generative AI
Integrating generative artificial intelligence (AI) into art, design, and media presents a double-edged sword. While it offers unprecedented creative possibilities, it raises ethical concerns, challenges traditional workflows, and requires careful regulation. As AI becomes more prevalent in these fields, there is a pressing need for a comprehensive resource that explores the technology's potential and navigates the complex landscape of its implications. The Pioneering Applications of Generative AI is a pioneering book that addresses these challenges head-on. It provides a deep dive into the evolution, ethical considerations, core technologies, and creative applications of generative AI, offering readers a thorough understanding of this transformative technology. By showcasing cutting-edge research and innovative use cases, the book bridges academia and practice, fostering cross-disciplinary collaboration and pushing the boundaries of creativity. Researchers, academicians, scientists, and research scholars will find this book invaluable in navigating the complexities of generative AI in art, design, and media. With its focus on ethical and responsible AI and discussions on regulatory frameworks, the book equips readers with the knowledge and tools needed to harness the full potential of generative AI while ensuring its responsible and ethical use.
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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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Understanding Generative AI Business Applications
This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity.The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting,Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems.In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you’re a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization’s success.What You Will LearnWhat are the technical elements that constitute the makeup of Generative AI products?What are the practical applications of Generative AI?How can algorithms be designed to automate and improve written communication?What are the latest Generative AI architectures and algorithms?Who This Book Is ForData scientists, data analysts, decision makers, and business executives interested in gaining an understanding of Generative AI products
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Generative Engine Optimization: Sichtbar in KI-Systemen
Dieses Essential erklärt, wie Unternehmen in der neuen Ära der KI-Systeme online sichtbar bleiben. Denn: ChatGPT, AI Mode oder Microsoft Copilot verändern die Art und Weise, wie Menschen nach Informationen suchen - und stellen Unternehmen vor einen grundlegenden Wandel. Wer nicht in KI-Antworten auftaucht, verliert Reichweite und Relevanz.Die Autoren führen in die neue Disziplin Generative Engine Optimization (GEO) ein - die die klassische Suchmaschinenoptimierung (SEO) ersetzen wird. Sie zeigen, wie Marken in KI-Antworten auftauchen, welche Faktoren die Sichtbarkeit beeinflussen und wie sich Erfolge messen lassen.Ein kompaktes Fachbuch für Marketingverantwortliche und Entscheider*innen, die verstehen wollen, wie ihr Unternehmen in KI-Systemen präsent bleibt.
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Generative AI and Creativity
Generative AI and Creativity: From Theory to Practice is an immersive exploration into the revolutionary domain of generative artificial intelligence. It takes a step-by-step journey starting with the fundamentals of generative AI and progressing to advanced applications. The book unravels the complexities of generative models, delving into key techniques such as generative adversarial networks (GANs) and variational autoencoders (VAE)s, providing a solid foundation for readers to understand the underlying principles.FEATURES• Explores the collaboration between humans and AI in creative processes• Examines generative art movements, graphic design applications, and artist-AI collaborations• Defines creativity in AI, by showcasing case studies and discussing ethical considerationsMoving beyond creative content generation in art and music to practical implementations in industries like healthcare and finance, the book showcases the versatility and transformative potential of generative AI across diverse domains. Readers will gain practical skills through coding examples, case studies, and collaborative projects, empowering them to integrate generative AI into their own work.The book is enriched with stories of innovation, featuring pioneers who have leveraged generative AI to push the boundaries of creativity. Ethical considerations and the societal impact of generative AI are also explored to foster a holistic understanding of the technology.
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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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Generative AI on Microsoft Azure
Companies are now moving generative AI projects from the lab to production environments. To support these increasingly sophisticated applications, they're turning to advanced practices such as multiagent architectures and complex code-based frameworks. This practical handbook shows you how to leverage cutting-edge techniques using Microsoft's powerful ecosystem of tools to deploy trustworthy AI systems tailored to your organization's needs. Written for and by AI professionals, Generative AI on Microsoft Azure goes beyond the technical core aspects, examining underlying principles, tools, and practices in depth, from the art of prompt engineering to strategies for fine-tuning models to advanced techniques like retrieval-augmented generation (RAG) and agentic AI. Through real-world case studies and insights from top experts, you'll learn how to harness AI's full potential on Azure, paving the way for groundbreaking solutions and sustainable success in today's AI-driven landscape. Understand the technical foundations of generative AI and how the technology has evolved over the last few yearsImplement advanced GenAI applications using Microsoft services like Azure AI Foundry, Copilot, GitHub Models, Azure Databricks, and Snowflake on AzureLeverage patterns, tools, frameworks, and platforms to customize AI projectsManage, govern, and secure your AI-enabled systems with responsible AI practicesBuild upon expert guidance to avoid common pitfalls, future-proof your applications, and more
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GPT Meets Game Theory
This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today’s most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way.As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to “mean-field” models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved.This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.
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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.
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Generative AI for Web Engineering Models
Web engineering faces a pressing challenge in keeping pace with the rapidly evolving digital landscape. Developing, designing, testing, and maintaining web-based systems and applications require innovative approaches to meet the growing demands of users and businesses. Generative Artificial Intelligence (AI) emerges as a transformative solution, offering advanced capabilities to enhance web engineering models and methodologies. This book presents a timely exploration of how Generative AI can revolutionize the web engineering discipline, providing insights into future challenges and societal impacts. Generative AI for Web Engineering Models offers a comprehensive examination of integrating AI-driven generative approaches into web engineering practices. It delves into methodologies, models, and the transformative impact of Generative AI on web-based systems and applications. By addressing topics such as web browser technologies, website scalability, security, and the integration of Machine Learning, this book provides a roadmap for researchers, scientists, postgraduate students, and AI enthusiasts interested in the intersection of AI and web engineering. Through insightful analysis and real-world examples, this book equips readers with the knowledge and tools to navigate the complexities of web engineering in the AI era. It offers practical strategies for leveraging Generative AI to develop more efficient, scalable, and secure web-based solutions. As the digital landscape continues to evolve, Generative AI for Web Engineering Models is a valuable resource for those seeking to stay at the forefront of web technology.
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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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