Search results for "Generative AI Engineering"
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R 428.00
Generative Programming and Component Engineering
This volume constitutes the proceedings of the ?rst ACM SIGPLAN/SIGSOFT International Conference on Generative Programming and Component Engine- ing (GPCE 2002), held October 6–8, 2002, in Pittsburgh, PA, USA, as part of the PLI 2002 event, which also included ICFP, PPDP, and a?liated workshops. The future of Software Engineering lies in the automation of tasks that are performed manually today. Generative Programming (developing programs that synthesize other programs), Component Engineering (raising the level of mo- larization and analysis in application design), and Domain-Speci?c Languages (elevating program speci?cations to compact domain-speci?c notations that are easier to write and maintain) are key technologies for automating program de- lopment. In a time of conference and workshop proliferation, GPCE represents acounter-trend in the merging of two distinct communities with strongly ov- lapping interests: the Generative and Component-Based Software Engineering Conference (GCSE) and the International Workshop on the Semantics, App- cations, and Implementation of Program Generation (SAIG). Researchers in the GCSE community address the topic of program automation from a contemporary software engineering viewpoint; SAIG correspondingly represents a community attacking automation from a more formal programming languages viewpoint. Together, their combination provides the depth of theory and practice that one would expect in apremier research conference. Three prominent PLI invited speakers lectured at GPCE 2002: Neil Jones (University of Copenhagen), Catuscia Palamidessi (Penn State University), and Janos Sztipanovits (Vanderbilt University). GPCE 2002 received 39 submissions, of which 18 were accepted.
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AI-Generated Image and Video Synthesis
Technical depth and ethical frameworks for AI visual media synthesis Generative AI models for visual media are transforming virtual reality and biomedical imaging while raising urgent questions about deepfakes and misinformation. AI-Generated Image and Video Synthesis addresses both dimensions. A team of researchers provide algorithmic foundations alongside detection strategies, authentication methods, and regulatory analysis. Coverage spans text-to-image generation, image-to-image translation, video synthesis, neural rendering, and 3D-aware generation. The book examines AI applications in CT, MRI synthetic data augmentation, and virtual staining for biomedical contexts. Case studies explore AI-assisted filmmaking, music videos, and style transfer. A dedicated chapter forecasts emerging trends including diffusion-transformer hybrids and autonomous generative agents. Readers will also find: Comparative analyses of generative models including GANs, diffusion models, and transformers with implementation guidance and code repositories for hands-on experimentationDeepfake detection strategies and digital content authentication techniques addressing misinformation, intellectual property rights, and emerging regulatory frameworks worldwideIndustry case studies demonstrating real-world deployments in creative industries, surveillance systems, education, and cultural preservation applicationsBiomedical imaging applications covering synthetic data generation for CT and MRI, virtual staining techniques, and data augmentation strategiesPractical toolkits and online resources supporting implementation and evaluation of AI synthesis techniques across professional and academic contexts Designed for AI researchers, computer vision engineers, and graduate students studying deep learning and image processing, this book connects theoretical principles with practical deployment. The combination of technical depth, application coverage, and ethical analysis makes it a comprehensive resource for professionals navigating AI-generated visual media.
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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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Generative AI and Large Language Models: Opportunities, Challenges, and Applications
This book provides a comprehensive exploration of the transformative impact of AI technologies across diverse fields. From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs). With a focus on both opportunities and challenges, the book examines the architectural challenges of transformer-based models, the ethical implications of AI, and the importance of language-specific adaptations, particularly for low-resource languages like Arabic. It also highlights the role of AI in code development, multimodal applications, and its integration with intellectual property frameworks. This book is an essential resource for researchers, practitioners, and policymakers seeking to understand and harness the potential of AI to drive innovation and global progress.
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Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices
Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices, Volume 141 in the Advances in Computers series, explores the convergence of artificial intelligence, machine learning, and modern software engineering practices. Chapters in this new release include Demystifying the Cloud-native Artificial Intelligence (CNAI) Paradigm, Articulating Machine and Deep Learning Models for Next-Generation Software Development, Delineating Artificial Intelligence (AI) and Its Potentials for Automated Software Engineering, Leveraging Machine and Deep Learning (ML/DL) Algorithms towards AI Models for Automating Software Development, and more.Other sections cover Artificial Intelligence (AI) Technologies and Tools for Accelerated Software Development, Demystifying the Agentic AI Paradigm for Accelerated Software Engineering, Detailing AI Techniques and Tools for Software Engineering Acceleration and Automation, Generative AI Tools for Accelerated Software Engineering, Empowering Software Engineering Automation through Explainable AI, and much more.
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Generative AI in Software Development
Generative AI in Software Development: Practical Ways AI is Building the Future of Code explores how large language models and generative tools are fundamentally changing the way software is created, tested, and maintained. This isn’t a theoretical or academic deep dive it’s a practical, grounded guide for developers, product teams, and tech leaders who want to understand how generative AI can be embedded into real-world software workflows. From writing and debugging code to generating UI components, managing APIs, and automating deployment tasks, this book shows how AI is becoming a true co-developer. What sets this book apart is its balance: it's not just for hardcore engineers, nor is it a high-level overview filled with buzzwords. Instead, it sits at the intersection technical enough to be useful, but accessible enough for product managers and decision-makers. The industry is at an inflection point where AI-assisted development is no longer optional; it's becoming essential. This book fills the gap for those who want to move beyond the hype and see exactly how generative AI can speed up development, improve code quality, and change the economics of software delivery.
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Enterprise Guide for Implementing Generative AI and Agentic AI
Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities.This book begins by guiding you through the technological evolution of AI, from early machine learning to today's large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action.The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings. You Will: Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively.Learn how to develop and implement a production-ready Agentic AI use case.Discover best practices for building scalable, secure, and enterprise-grade AI solutions.Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices. This book is for: Enterprise Software Engineers and Architects
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Introduction to Artificial Intelligence in Automotive Engineering
This book serves as a practical and timely guide to understanding how artificial intelligence can be applied and leveraged in automotive engineering. It demystifies complex methods of applying artificial intelligence and provides readers with the tools to creatively and immediately apply them to their own engineering challenges, transforming information into knowledge.Drawing on the authors' teaching experience, the book takes a practical approach to applying artificial intelligence tools - such as regression, classification, clustering, deep learning and reinforcement learning- to real-world engineering problems. Through clear explanations and step-by-step algorithmic structures, readers are introduced to the fundamental concepts of artificial intelligence and their practical application by building AI models using automotive data. Examples include predicting the energy consumption of electric vehicles under varying environmental and operating conditions, assessing battery degradation, analyzing brake system wear, and implementing intelligent control for thermal management (HVAC systems). The book shows how the application of artificial intelligence techniques and methods can optimize performance, increase safety, and stimulate innovation in the automotive sector.The book represents an essential guide for all those interested in exploring the application of artificial intelligence in automotive engineering (and beyond) and equally constitutes an effective support in the teaching process (courses, laboratories, projects) of applied artificial intelligence concepts.
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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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Generative AI in Neurodegenerative Disorders
This book delves into the transformative power of AI in the realm of neurodegenerative diseases,covering topics such as ALS, Huntington’s, Parkinson’s, and Alzheimer’s. Generative AIprovides new opportunities for early diagnosis, precise therapy, and individualized rehabilitation,which are crucial as these conditions remain major obstacles for healthcare providersand researchers.Researchers, physicians, AI developers, and healthcare professionals will find this bookan invaluable resource for understanding how AI is influencing the development of treatmentsfor neurodegenerative diseases. It describes important obstacles and future directions whileproviding insights into the newest breakthroughs, thus bridging the gap between technologyand practical clinical applications. Anyone involved in neurodegenerative healthcare, fromscientists conducting AI-driven medical research to physicians seeking to incorporate AI intopatient care or AI professionals investigating new healthcare applications, will find the informationand insights they need in this comprehensive book.Predictive analytics, biomarker identification, and drug discovery are being transformedby AI-driven models, such as deep neural networks, generative adversarial networks (GANs),and variational autoencoders (VAEs). This book offers a comprehensive examination of thesedevelopments. Robots, wearable sensors, and cognitive therapy platforms are some of theAI-enhanced rehabilitation tools covered, as are AI-integrated cutting-edge technologies likefMRI and MRI, gene-editing methods like CRISPR, and more.In addition to discussing recent technical developments, this book takes a close look atthe data privacy, ethics, and regulatory issues that arise when using AI to study neurodegenerativedisorders. Issues like algorithmic bias, model explainability, and fair AI-driven healthcareare thoroughly investigated in light of the growing usage of AI models in clinical decision-making,mental health applications, and cognitive rehabilitation.
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Communication Skills for Generative AI
Communication Skills for Generative AI: A Practical Guide for Humans is the first comprehensive book to treat communication with generative AI as a distinct human competence—analogous to digital literacy, media literacy, or academic writing skills. Instead of framing the field through the narrow lens of “prompt engineering,” this book introduces a broader, deeper, and more natural paradigm: human–AI communication. It argues that as generative AI becomes embedded in daily life, professional work, research, education, and creative practices, humans must learn not merely to instruct AI systems but to communicate with them effectively, collaboratively, and safely. The book covers the foundations, principles, techniques, and future directions of communicating with modern AI systems. It begins by explaining why the rise of generative AI requires a new set of human capabilities, then outlines the psychological, linguistic, cognitive, and interactional mechanisms that shape human–AI dialogue. It provides a systematic structure of principles (clarity, intent, iteration, cognitive alignment, ethical responsibility) and practical techniques (contextual communication, constraint-based approaches, multimodality, iterative refinement, tone and persona control). The book also examines pitfalls and sources of misunderstanding—such as ambiguity, hallucination, overtrust, and guardrails—and offers strategies for detecting and resolving them. A central contribution of the book is its treatment of collaborative and co-creative communication, where AI is understood not as a passive tool but as an active cognitive partner capable of supporting reasoning, generating ideas, and co-creating text, visuals, and solutions. Another unique perspective is the chapter on metacommunication, which teaches readers how to talk about the communication process itself— expressing preferences, adjusting strategies, and aligning expectations during the dialogue. The book concludes with a forward-looking exploration of the future of AI communication: post-prompting paradigms, persistent personal assistants, intuitive and emotional interfaces, hybrid reasoning models, and the evolving competencies humans will need as AI becomes increasingly autonomous and embedded. This book relates to existing literature on prompt engineering, human–computer interaction, and AI literacy but significantly expands beyond them. While traditional books focus on crafting prompts or operating tools, this work builds a conceptual and methodological foundation for treating AI as a communicative partner. It introduces a unified framework for understanding interaction across text, multimodal interfaces, and emerging real-time systems. This broader perspective positions the book as a pioneering contribution that addresses an urgent gap in current academic, professional, and educational discourse.
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Revolutionizing Healthcare 5.0: The Power of Generative AI
This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.
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Artificial Intelligence and Digital Systems Engineering
The resurgence of artificial intelligence has been fueled by the availability of the present generation of high-performance computational tools and techniques. This book is designed to provide introductory guidance to artificial intelligence, particularly from the perspective of digital systems engineering.Artificial Intelligence and Digital Systems Engineering provides a general introduction to the origin of AI and covers the wide application areas and software and hardware interfaces. It will prove to be instrumental in helping new users expand their knowledge horizon to the growing market of AI tools, as well as showing how AI is applicable to the development of games, simulation, and consumer products, particularly using artificial neural networks.This book is for the general reader, university students, and instructors of industrial, production, civil, mechanical, and manufacturing engineering. It will also be of interest to managers of technology, projects, business, plants, and operations.
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What Every Engineer Should Know About Artificial Intelligence and Big Data
Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging towards the adoption of distributed open-source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. Rather than focusing on theory, the book shares real-life experiences building AI and big data analytics systems of value to practitioners.• Features practical case studies on building big data and AI models for large scale enterprise solutions.• Discusses the use of design patterns for architecting AI that are safe, secure, and testable.• Covers an array of concepts including deep big data analytics, natural language processing, transformer architecture and evolution of ChatGPT, swarm intelligence, and genetic programming.Informed by the authors' many years of teaching ML, AI, and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.
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Agentic GraphRAG
What if your AI systems could retrieve information, reason over complex knowledge, plan actions, and continuously learn-all while maintaining enterprise-grade security and compliance? Agentic Graph RAG guides technical leaders, engineers, and architects through the next evolution of generative AI. Combining retrieval-augmented generation (RAG) with graph-based reasoning and agentic capabilities, this guide provides a practical blueprint for building scalable, auditable, and intelligent AI systems.Written by Anthony Alcaraz and Sam Julien, this book demystifies knowledge graphs, graph memory, neural-symbolic reasoning, and agent orchestration through real-world case studies, hands-on design patterns, and production-ready architectures. Readers will learn how to construct graph-native retrieval systems, integrate advanced reasoning into agent workflows, and address enterprise challenges around governance, scalability, and transparency.Design graph-augmented architectures that surpass traditional RAGImplement agents with dynamic memory, planning, and decision-making capabilitiesIntegrate knowledge graphs with large language models for robust, explainable AIDeploy scalable, governable multiagent systems ready for production environments
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Handbook Of Sketch-based Modeling For Engineering Design: A Practical Approach With Refer
Handbook of Sketch-Based Modeling for Engineering Design offers a hands-on introduction to Sketch-Based Modeling (SBM) through the use of a custom software application called REFER, which is accessible on GitHub. It presents both a critical perspective on current trends and a constructive framework for developing the next-generation of hybrid SBM methods that combine human-like perceptual insight, classical rigor, and AI scalability.Sketch-Based Modeling (SBM) can be placed at the intersection of two traditions: the classical approach, rooted in decades of accumulated computational and geometric expertise, and the artificial intelligence (AI) approach, powered by massive data and computing resources. Rather than competing, these paradigms can (and should) be used together in a complementary, synergistic manner.Beyond practical instruction, the book discusses how procedural modeling, graph-based methods, and established CAD principles can inform the training of AI systems and provide a pathway toward more robust and scalable SBM solutions. Through concrete examples and source code discussions, readers are introduced to feature detection, deconstruction, and reconstruction of sketches into procedural 3D models, as well as the challenges of working with imperfect input and capturing design intent.This book is ideal for researchers, advanced undergraduate and graduate students, and advanced practitioners specializing in computational design, computer graphics, and AI-driven design engineering.All software for the book are available on GitHub: https://github.com/REFER-SBM/REFER/
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Computational Methods in Engineering, AI, and Analysis
Computational Methods in Engineering, AI, and Analysis offers a comprehensive concept of modern computational mathematics, bringing together theoretical foundations, advanced numerical methods, and emerging data-driven approaches. The volume is structured across three key themes: mathematical modeling and computational techniques, engineering and physical system applications, and artificial intelligence with machine learning. It presents a collection of the methodologies such as wavelet-based methods, fractional calculus, finite element analysis, and hybrid AI models, with applications to the real-world problems in fluid dynamics, finance, healthcare, and environmental systems. With the contributions from the researchers across interdisciplinary fields, the book highlights the growing interaction between mathematics and the computational domain with the involvement of the advanced computational techniques to address the complex scientific challenges.
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What Every Engineer Should Know About Artificial Intelligence and Big Data
Recognizing the vast potential in analyzing big data through machine learning (ML) and artificial intelligence (AI) technologies, companies are acknowledging these technologies as essential for maintaining relevance. A prevailing trend is emerging towards the adoption of distributed open-source computing for storing big data assets and performing advanced ML/AI analytics to predict future trends and risks for businesses. This book offers readers an overview of the essentials of big data and ML/AI, while acknowledging that the field is extensive and evolving. Rather than focusing on theory, the book shares real-life experiences building AI and big data analytics systems of value to practitioners.• Features practical case studies on building big data and AI models for large scale enterprise solutions.• Discusses the use of design patterns for architecting AI that are safe, secure, and testable.• Covers an array of concepts including deep big data analytics, natural language processing, transformer architecture and evolution of ChatGPT, swarm intelligence, and genetic programming.Informed by the authors' many years of teaching ML, AI, and working on predictive data analytics/AI projects, this book is suitable for use by graduates, professionals, and researchers within the field of data science and engineers and scientists interested in learning more about these essential technologies.
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