Search results for "Generative AI and Quantum Computing"
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R 641.00
Quantum Computing and Artificial Intelligence
This book constitutes the proceedings of the Second International Workshop on Quantum Computing and Artificial Intelligence, QC+AI 2026, which took place in Singapore on January 27, 2026.The 7 full papers included in this book were carefully reviewed and selected from 17 submissions. They deal with up to date topics in quantum optimization and quantum machine learning.
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R 1,750.00
R 9,525.00
Advances in Quantum Inspired Artificial Intelligence
This book advances the artificial intelligence techniques by strongly inspired from quantum computing. Quantum computing (QC), rooted in the mysterious principles of quantum mechanics, has fascinated scientists, researchers, and visionaries for a long time. Its potential lies in changing how we compute, moving from classical bits to quantum bits (qubits), and unlocking incredible processing capabilities. At the same time, artificial intelligence (AI) has been on a trajectory of rapid advancement, with deep learning becoming proficient at tasks that were once deemed impossible. At this juncture, integrating these two fields through various means can play a pivotal role to bring a technological revolution.
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Quantum Machine Learning in Industrial Automation
This book focuses on quantum machine learning that harnesses the collective properties of quantum states, such as superposition, interference, and entanglement, uses algorithms run on quantum devices, such as quantum computers, to supplement, expedite, or support the work performed by a classical machine learning program. The devices that perform quantum computations are known as quantum computers. Quantum computers have the potential to revolutionize computation by making certain types of classically intractable problems solvable. A few large companies and small start-ups now have functioning non-error-corrected quantum computers composed of several tens of qubits, and some of these are even accessible to the public through the cloud. Additionally, quantum simulators are making strides in fields varying from molecular energetics to many-body physics. Most known use cases fit into four archetypes: quantum simulation, quantum linear algebra for AI and machine learning, quantum optimization and search, and quantum factorization. Advantages of quantum computing are many and to list a few, first, they're fast. Ultimately, quantum computers have the potential to provide computational power on a scale that traditional computers cannot ever match. In 2019, for example, Google claimed to carry out a calculation in about 200 seconds that would take a classical supercomputer around 10,000 years. Second, they can solve complex problems. The more complex a problem, the harder it is for even a supercomputer to solve. When a classical computer fails, it's usually because of a huge degree of complexity and many interacting variables. However, due to the concepts of superposition and entanglement, quantum computers can account for all these variables and complexities to reach a solution. Last but not the least, they can run complex simulations. The speed and complexity that quantum computing can achieve means that, in theory, a quantum computer could simulate many intricate systems.
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Quantum Artificial Intelligence
This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples. It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.
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R 580.00
Quantum Computing from Hopfield Nets
This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models. The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.
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R 275.00
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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Dependable Computer Systems: Methods, Models, and Applications
This book presents contributions from the 21st DepCoS RELCOMEX Conference that addresses the reliability, safety, security, and performance of modern information systems—domains that are rapidly evolving under the influence of artificial intelligence, quantum technologies, and emerging cybersecurity challenges. Readers will find insights into cutting edge analytical tools, innovative architectures, and real world applications that shape the future of dependable computer systems and networks. Spanning theoretical advances, engineering methodologies, and practical case studies, the proceedings reflect the inherently multidisciplinary nature of contemporary system design.The scope of the DepCoS conferences has always encompassed the diverse challenges that arise in the performability and dependability analysis of modern computer systems. This year, the topical range has been further enriched by a strong set of contributions on quantum computing—a rapidly developing paradigm with the potential to transform the way computers will be used in the future. It has also become increasingly clear that artificial intelligence methods and tools play a central role in today’s information technology and computer engineering and that cybersecurity—both its tools and its standards—is gaining critical importance, particularly in light of the rapidly growing threats posed by AI driven attack systems.
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Gentle Introduction to Quantum Machine Learning
Quantum machine learning (QML) is revolutionizing artificial intelligence by leveraging the power of quantum computing to access previously unimaginable computational possibilities. However, the field remains fragmented--balancing rigorous quantum theory with practical AI applications remains a challenge. This book bridges this gap, offering a systematic, hands-on guide for AI researchers, ML practitioners, and computer scientists eager to explore this emerging frontier. It provides a cohesive roadmap, covering everything from fundamental quantum computing principles to state-of-the-art QML techniques. Readers will explore quantum kernel methods, quantum neural networks, and quantum Transformers, gaining insight into their theoretical foundations, performance advantages, and practical implementations. The book's code demonstrations offer hands-on experience, ensuring that readers can move beyond theory to real-world applications. Designed for those with an AI or ML background, this tutorial does not assume prior expertise in quantum computing. Instead, it presents complex concepts with clarity, making it an essential resource for researchers, graduate students, and industry professionals eager to stay ahead in the quantum AI revolution. Whether you seek to understand quantum speedups, develop quantum-based models, or explore future research directions, this book provides the foundation you need to engage with QML and shape the future of intelligent computing.
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R 456.00
Metaverse, Generative AI, and Brain-Computer Interfaces
This compelling exploration delves into the heart of cutting-edge technologies, specifically the Metaverse, Brain-Computer Interfaces (BCI), and generative artificial intelligence (AI). The authors unravel their transformative applications in shaping the future and contemplate how the next generation of virtual environments will manifest, as well as how the digital landscape can be affected.The book encompasses various examples of the Metaverse, brain-computer interfaces, and generative AI across diverse fields, including education, engineering, and medical science. In the dynamic realm of the Metaverse, where virtual realities converge, the book showcases how these innovations can transform the way we learn, interact, and build. Through Brain-Computer Interfaces (BCIs), users can directly interface with technology using neural signals, opening up new avenues for communication and control. Additionally, generative AI algorithms empower us to create immersive virtual environments and generate content with unprecedented creativity.Whether you're a student, educator, or industry professional, this book is your ticket to understanding and harnessing the power of the future. It provides a pathway for those who believe in the limitless possibilities that the Metaverse, BCI, and generative AI can bring to our daily experiences.
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Generative AI: Current Trends and Applications
This comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as Generative AI models, transfer learning and Natural Language Processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in Generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of Generative AI and how it can be harnessed to tackle complex real-world challenges.
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R 489.00
Proceedings of International Conference on Generative AI, Cryptography and Predictive Analytics
The book presents the proceedings of the International Conference on Generative AI, Cryptography and Predictive Analytics (ICGCPA 2024), held at VIPS-TC, School of Engineering and Technology, Pitampura, Delhi, India, during June 28 – 29, 2024. It covers Generative AI's role in problem-solving, examining applications in image synthesis, content creation, healthcare, and optimization challenges. This book is a valuable resource for postgraduate students in various engineering disciplines.
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Generative Ai In Clinical Practice: A Physician's Guide To Transforming Medicine
Generative AI in Clinical Practice: A Physician's Guide to Transforming Medicine, is a practical guide for clinicians who want to understand and apply generative AI in everyday care. Designed for readers without a background in computer science, the book explains how large language models and related tools are being integrated into clinical workflows, with clarity, realism, and clinical relevance.You'll explore how generative AI is already supporting tasks like ambient documentation, diagnostic reasoning, patient education, and care coordination. Each chapter combines clinical vignettes, implementation strategies, and curated examples from frontline practice to show what works, and what to watch out for.The book also addresses critical issues of ethics, bias, regulation, and physician oversight. Rather than offering abstract predictions, it provides grounded, actionable guidance for safely using AI at the point of care.Whether you are a physician, nurse, or healthcare executive, this book will help you make informed decisions about AI adoption. It will also give you the language and insight needed to lead conversations about these emerging tools within your organization. Clear-eyed and practical, this guide is written to help clinicians gain confidence, not just in the technology, but in how to use it wisely.
R 1,806.33
EAI International Conference on Computational Intelligence and Generative AI
The book presents the proceedings of the EAI International Conference on Computational Intelligence and Generative AI (ICCIGAI 2024), which took place March 8-9, 2024, in Guntur, India. The book explores the exciting and rapidly evolving field of artificial intelligence, with a specific focus on computational intelligence and generative models. The conference introduces cutting-edge techniques and methodologies that enable machines to simulate human-like intelligence and creativity. Readers can find a comprehensive overview of state-of-the-art AI methods, including neural networks, evolutionary algorithms, deep learning, and generative adversarial networks (GANs). The conference explores real-world applications in a range of areas, including computer vision, natural language processing, and the creation of original content.
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Oxford Handbook of the Foundations and Regulation of Generative AI
Generative AI technology holds immense transformative potential, with applications spanning from text generation to image creation and scientific discovery. Yet, its rapid development and adoption have introduced significant legal and ethical concerns, such as potential cybersecurity risks, misinformation, unsustainability, and algorithmic discrimination. The Oxford Handbook of the Foundations and Regulation of Generative AI offers theoretical insights and practical recommendations for ensuring the responsible development and application of this emerging technology.Edited and written by leading scholars in law, AI ethics, economics, and computer science, this Handbook brings together a global team of experts to examine the foundational principles and regulatory challenges posed by generative AI systems. The Handbook introduces core technical concepts-including explainable AI (XAI), agentic AI safety, and prompting-and discusses pressing legal and ethical concerns such as AI bias, consumer protection, platform and worker rights, data protection, copyright, and liability for AI-generated speech. The volume also considers how generative AI is transforming industries, from business and legal practice to healthcare, finance, and cybersecurity. Finally, it concludes with in-depth analyses of AI regulation in various jurisdictions, including the EU, US, China, Brazil, India, and Australia. Interdisciplinary in scope and global in coverage, this Handbook is an essential resource for anyone seeking to understand and navigate the evolving landscape of generative AI. It provides an accessible entry point for students, while offering the depth and rigor needed to inform practice, policy, and advanced research.
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Quantum Computers
This book presents various theories and algorithms to create a quantum computer. The concept of the classical and quantum computers, and the concept of circuits and gates are reviewed. The example of the Deutsch and the Deutsch-Josca algorithm is discussed to illustrate some key features of quantum computing. The Grover algorithm, considered to be of major milestone of the subject, is discussed in detail to exemplify the techniques used in computer algorithms. The role of quantum superposition (also called quantum parallelism) and of quantum entanglement is discussed in order to understand the key advantages of a quantum over a classical computer.
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Securing Uncontrolled Gen-AI Upload-Induced Data Leakage
The rapid growth of generative artificial intelligence (Gen-AI) is transforming industries such as healthcare, finance, education, law and software engineering. While Gen-AI enhances productivity and decision-making, it also introduces significant security, privacy and governance challenges, particularly through the unintended exposure of sensitive data. Securing Uncontrolled Gen-AI Upload-Induced Data Leakage examines the risks associated with uploading personal information, intellectual property, confidential communications and proprietary source code to AI systems. It explores key threats, including unsafe prompt engineering, shadow AI usage, prompt injection, model inversion, data poisoning and API misuse, which can result in data leakage, model memorization and regulatory violations. Through real-world case studies, forensic analysis and log tracing, this book demonstrates how sensitive information propagates across AI environments, and how such risks can be identified and mitigated. It also presents governance and forensic frameworks aligned with regulations such as GDPR, HIPAA and CCPA, providing practical guidance for secure, transparent and responsible AI adoption.
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Climate Resilience: Impact of Quantum Computing and Artificial Intelligence on Urban Planning
This book discusses how changes in climate can affect biodiversity, the natural world, and human society. The exploitation of natural resources, improperly managed development projects, human activities, and other factors are causing regular changes in the global environmental circumstances. Because of its severe effects on living things, climate change is one of the main problems that has attracted the attention of scientists, policy officials, academics, businesses, and other stakeholders worldwide. The main adverse implications of climate change are human health problems, changes in mean sea level, lower agricultural output, the spread of vectors, and degradation of the ecosystem. Utilizing cutting-edge techniques such as robotics, deep learning, machine learning, and artificial intelligence help construct greener, more intelligent energy systems or boost energy efficiency to mitigate the harmful consequences of climate change. Furthermore, by using real-time data analysis, these strategies might be useful in monitoring and anticipating climate challenges. It helps in developing novel approaches to quantifying the effects of climate change by taking into account the infrastructure that is currently in place, plant growth, power use, and emissions from vehicles and industries. In order to reduce the consequences, recent techniques are helpful in the research and management of climate-related concerns are also thoroughly explored. This is also helpful for future projections of weather phenomena along with climate extremes, etc. These methods are rapidly growing and widely applied in engineering and research. Therefore, it is anticipated that a large number of researchers are able to provide excellent reviews, case studies, and research findings regarding how these strategies might assist the globe to cope with climate variability. Additionally, this book also includes other strategies for lowering the risks associated with climate change as well as various forecasting techniques for climate uncertainty. Additionally, this book contributes to the accomplishment sustainable development goals (SDGs).
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Generative AI for Connected and Autonomous Vehicles
Harness generative AI for connected and autonomous vehicle systems As generative AI reshapes intelligent transportation, engineers and researchers need rigorous frameworks for deploying these technologies in safety-critical systems. This book offers a comprehensive treatment of the models, protocols, and adaptation techniques required to bring large language models, vision-language models, diffusion models, and agentic systems into modern CAV architectures. The book progresses from generative foundations through multimodal representation, language-based interaction, agentic coordination, communication protocols, model adaptation, and validation and safety. Mathematical, probabilistic, and reinforcement-learning prerequisites are consolidated into dedicated appendices, allowing the main chapters to focus on system design and CAV-specific applications. Written by an award-winning researcher with more than 100 patents across connected vehicles and AI, readers will also find: Practical treatment of prompt engineering, retrieval-augmented generation, function calling, and tool use for grounding GenAI in vehicular contextChapters on agent architectures, multi-agent systems, orchestration frameworks, and MCP and A2A protocols for collaborative CAV intelligenceAdaptation and deployment techniques including supervised fine-tuning, reinforcement learning fine-tuning, and knowledge distillation for resource-constrained edge platformsDedicated chapters on simulation platforms, evaluation methodologies, and ethical and safety considerations for deploying generative AI in autonomous vehicles Automotive engineers, AI researchers, and graduate students at the intersection of machine learning and intelligent transportation will find this book indispensable. Readers from OEMs, mobility startups, or academic labs gain the foundations and practices needed to design, adapt, and validate generative AI in connected mobility.
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Narrative and Generative AI
This second edition provides an overview of the principal problems, approaches, and challenges in modeling the narrative structure of stories. The book builds upon the prior edition by incorporating an additional focus on generative artificial intelligence and its applications for narratology. The author explains the computational representations of narrative elements, such as characters, events, causality, and time, proposing new methods for representing embedded narratives, fictional entities, tempo, and audience response. The book discusses the rise of generative AI systems, including large language models and transformers. The author explores the current applications of these systems and the extent to which they can be used to automate the cognitive aspects of narrative understanding and generation.
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Generative AI: Techniques, Models and Applications
This book unlocks the full potential of modern AI systems through a meticulously structured exploration of concepts, techniques, and practical applications. This comprehensive book bridges theoretical foundations with real-world implementations, offering readers a unique perspective on the rapidly evolving field of generative technologies. From computational foundations to ethical considerations, the book systematically covers essential topics including foundation models, large-scale architectures, prompt engineering, and practical applications. The content seamlessly integrates complex technical concepts with industry-relevant examples, making it an invaluable resource for researchers, academicians, and practitioners. Distinguished by its balanced approach to theory and practice, this book serves as both a learning tool and reference guide. Readers will benefit from:Clear explanations of advanced concepts.Practical implementation insights.Current industry applications.Ethical framework discussions.Whether you're conducting research, implementing solutions, or exploring the field, this book provides the knowledge necessary to understand and apply generative AI technologies effectively while considering crucial aspects of security, privacy, and fairness.
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Generative Artificial Intelligence for Biomedical and Smart Health Informatics
Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disordersBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systemsTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoringEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.
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Generative AI, Media, and Society
This groundbreaking book demystifies generative AI’s transformative impact on media, socio-cultural dynamics, ethics, and policy.Defining generative AI as an evolutionary leap in the development of artificial intelligence, the author examines intricate human-machine interactions and socio-technical dynamics, advocating robust, proactive AI governance to address emerging uncertainties. The book is clearly structured into six key chapters, each exploring distinct aspects of the relationship between artificial intelligence, media, and society. The chapter on "Transformation" examines how machine behavior is reshaping our datafied society, questioning whether data is the new oil, or digital manure. "Generative AI" investigates the models and future impacts of generative AI as a co-intelligence, revisiting the Turing Test and analyzing societal-business impacts. "AI Media" explores the convergence of media and AI, highlighting robot journalism, synthetic content, and the disinformation era and discussing the trend toward high-risk optimism. "Uncertainties" addresses inherent unpredictability vs. strategic foresight, focusing on challenged business models, sustainability concerns, and emotional intelligence factors. "Ethics" analyzes generative morality and dual-use technology, covering trusted AI principles—from misuse to integrative solutions. Finally, "Policy" discusses governance, labor market impacts, and the importance of human rights and power dynamics in generative AI. Each chapter also provides summaries of impact projects, reflective art, scholarly questions, and strategic takeaways—extended with a comprehensive glossary.This is an essential resource for scholars, students, policymakers, technologists, ethicists, and AI industry leaders seeking to rapidly understand and address the challenges and opportunities of generative AI and AI media in a cohesive framework.
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Applications and Principles of Quantum Computing
In a world fueled by information and technology, we stand on the precipice of a monumental challenge – the limitations of classical computing. As the complexity of problems surges, from climate modeling to financial risk assessment, our conventional computational capabilities fall short. Confronted by the exponential growth of data and the intricacies of real-world dilemmas, we find ourselves grappling with barriers that hinder progress. These limitations cast shadows over our aspirations to revolutionize industries and unravel the mysteries of our universe. It is a chasm that can only be bridged by transcending the boundaries of classical computing. Principles and Applications of Quantum Computing, edited by the accomplished Alex Khang PH from the Department of AI and Data Science, is a book which stands as a definitive solution to the quantum conundrum. Diving into the multidisciplinary realms of computer science, physics, and mathematics, it unveils the untapped potential of quantum computing – a realm 158 million times faster than even the most sophisticated supercomputers. With quantum mechanics as its bedrock, this technology heralds a new era of computation, igniting hope for the seemingly insurmountable problems that plague our world. Principles and Applications of Quantum Computing not only introduces the principles and concepts that underpin quantum computing but also illuminate its myriad applications across diverse domains. From artificial intelligence and cybersecurity to financial modeling, drug development, and beyond, this book stitches together a tapestry of insights from quantum computing scientists, researchers, engineers, and scholars worldwide. By embracing this compendium, you are not merely gaining knowledge – you are becoming a pioneer in the quantum revolution. Contribute to this intellectual movement and be at the forefront of innovations that will redefine the limits of what is possible in the digital age.
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Quantum Computing for Programmers
This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. More than 50 quantum techniques and algorithms are explained with mathematical derivations and code for simulation, using an open-source code base in Python and C++. New material throughout this fully revised and expanded second edition includes new chapters on Quantum Machine Learning, State Preparation, and Similarity Tests. Coverage includes algorithms exploiting entanglement, black-box algorithms, the quantum Fourier transform, phase estimation, quantum walks, and foundational QML algorithms. Readers will find detailed, easy-to-follow derivations and implementations of Shor's algorithm, Grover's algorithm, SAT3, graph coloring, the Solovay-Kitaev algorithm, Moettoenen's algorithm, quantum mean, median, and minimum finding, Deutsch's algorithm, Bernstein-Vazirani, quantum teleportation and superdense coding, the CHSH game, and, from QML, the HHL algorithm, Euclidean distance, and PCA. The book also discusses productivity issues like quantum noise, error correction, quantum programming languages, compilers, and techniques for transpilation.
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Integration of AI Theory and Applications in Diverse Industries
The book explores the ways artificial intelligence (AI) theory is being applied to an extremely diverse range of industries, illustrating just how robust and adaptable this technology has become. AI is revolutionizing the way the world operates by allowing machines to learn from data, make choices, and even resolve problems similar to a human, but quicker and oftentimes more precisely. From its integration into innovative applications such as smart assistants and personalized suggestions to the application of quantum machine learning in identifying multifunctional materials for future technologies, AI is breaking boundaries everywhere. Within the book, every chapter explores a particular industry including energy, healthcare, manufacturing, and business and how AI is not only a theoretical idea, but a real-world tool that fuels innovation. For example, a variety of topics are examined such as how AI is central to the computational design of semiconductors, which are critical to electronics and emerging tech innovations. In manufacturing, AI solves challenges in virtual metrology, enhancing the precision and efficiency of production lines. The book also showcases how AI enables sustainability and assists in energy management and the fight against climate change. Oil and gas industries are getting smarter and more efficient with the application of AI to optimize operations and safety. In healthcare, AI is transforming preventive care, assisting doctors in identifying diseases at an early stage and tailoring treatments. The book also dives into the deployment of AI on devices such as smartphones and wearables, enabling real-time, privacy-focused decision-making. Businesses benefit from AI’s capability in data-driven decision-making, leading to smarter strategies and faster growth. Lastly, AI is enhancing our interaction with technology through immersive AR/VR experiences, making them more intuitive and responsive. This book encourages interdisciplinary thinking between energy, healthcare, and business fields.
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Information Processing and Network Provisioning
The four-volume set CCIS 2416, 2417, 2418 and 2419 constitutes the refereed post-conference proceedings of the Third International Conference on Information Processing and Network Provisioning, ICIPNP 2024 Spring, held in Beijing, China, during June 14-16, 2024. The 152 revised full papers presented in these proceedings were carefully reviewed and selected from 347 submissions. They focus on topics ranging from 5G/6G evolution and AI in network optimization to quantum communication and green computing.
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Generative AI Security
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selectionIntelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generationDeepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integritySecurity attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacksPrivacy-preserving AI techniques including federated learning and homomorphic encryption Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.
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Quantum Computing
The discovery of physical systems that exhibit what we now call quantum mechanical behaviour was unquestionably one of the most astonishing events in the history of science. No less remarkable was the realisation that quantum mechanics presents a much more powerful paradigm for computation and information processing than its classical counterpart. This textbook explores the incredible, but somewhat subtle, power of quantum computation, starting with a thorough in-depth exposition of quantum physics. Quantum mechanical behaviour is formalised by the postulates of quantum mechanics, which give rise to some startling results, such as the Bell inequalities. From the postulates of quantum mechanics, the quantum circuit model can also be derived, which enables a visual intuition for quantum computation to be acquired without any loss of rigour in the accompanying analysis. Getting to the principal subject-matter of the book, the question of quantum advantage is tackled head-on, with the theoretical evidence that quantum computation does indeed offer a more powerful computation paradigm, paired with cautionary results that certain classes of circuits can be proven to enjoy no such quantum advantage. The book then presents a canon of the most important quantum algorithms and communication protocols that have been discovered in the relatively brief history of this exciting, emerging field. Starting with historically important query algorithms, 'classic' results such as Grover's and Shor's algorithm are then covered, followed by more recent breakthroughs including the quantum algorithm for solving linear systems (HHL) and the quantum singular value transformation.The final chapter of the book addresses the subject of quantum error correction, building up to the celebrated 'threshold theorem', which implies that the theoretical promises of quantum computation can one day be realised on actual quantum computers.
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Generative AI, Cybersecurity, and Ethics
"Generative AI, Cybersecurity, and Ethics is an essential guide for students, providing clear explanations and practical insights into the integration of generative AI in cybersecurity. This book is a valuable resource for anyone looking to build a strong foundation in these interconnected fields."—Dr. Peter Sandborn, Professor, Department of Mechanical Engineering, University of Maryland, College Park "Unchecked cyber-warfare made exponentially more disruptive by Generative AI is nightmare fuel for this and future generations. Dr. Islam plumbs the depth of Generative AI and ethics through the lens of a technology practitioner and recognized AI academician, energized by the moral conscience of an ethical man and a caring humanitarian. This book is a timely primer and required reading for all those concerned about accountability and establishing guardrails for the rapidly developing field of AI."—David Pere, (Retired Colonel, United States Marine Corps) CEO & President, Blue Force Cyber Inc. Equips readers with the skills and insights necessary to succeed in the rapidly evolving landscape of Generative AI and cyber threats Generative AI (GenAI) is driving unprecedented advances in threat detection, risk analysis, and response strategies. However, GenAI technologies such as ChatGPT and advanced deepfake creation also pose unique challenges. As GenAI continues to evolve, governments and private organizations around the world need to implement ethical and regulatory policies tailored to AI and cybersecurity. Generative AI, Cybersecurity, and Ethics provides concise yet thorough insights into the dual role artificial intelligence plays in both enabling and safeguarding against cyber threats. Presented in an engaging and approachable style, this timely book explores critical aspects of the intersection of AI and cybersecurity while emphasizing responsible development and application. Reader-friendly chapters explain the principles, advancements, and challenges of specific domains within AI, such as machine learning (ML), deep learning (DL), generative AI, data privacy and protection, the need for ethical and responsible human oversight in AI systems, and more. Incorporating numerous real-world examples and case studies that connect theoretical concepts with practical applications, Generative AI, Cybersecurity, and Ethics: Explains the various types of cybersecurity and describes how GenAI concepts are implemented to safeguard data and systemsHighlights the ethical challenges encountered in cybersecurity and the importance of human intervention and judgment in GenAIDescribes key aspects of human-centric AI design, including purpose limitation, impact assessment, societal and cultural sensitivity, and interdisciplinary researchCovers the financial, legal, and regulatory implications of maintaining robust security measuresDiscusses the future trajectory of GenAI and emerging challenges such as data privacy, consent, and accountability Blending theoretical
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