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Gesamtkatalog

Catalogue complet

Agarwal P: Edge AI

Edge AI

Foundations, Techniques and Applications

Advanced Biotechnology

Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields Machine Learning and Big Data-enabled Biotechnology discusses how machine learning and big data can be used in biotechnology for a wide breadth of topics, providing tools essential to support efforts in process control, reactor performance evaluation, and research target identification. Topics explored in Machine Learning and Big Data-enabled Biotechnology include: <ul> <li>Deep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequences</li> <li>De novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approaches</li> <li>Metabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell models</li> <li>Automated function and learning in biofoundries and strain designs</li> <li>Machine learning predictions of phenotype and bioreactor performance</li> </ul> Machine Learning and Big Data-enabled Biotechnology earns a well-deserved spot on the bookshelves of reaction, process, catalytic, and environmental engineers seeking to explore the vast opportunities presented by rapidly developing technologies.

Machine Learning and Big Data-enabled Biotechnology

Studies in National Governance and Emerging Technologies

This book examines the relationship between artificial intelligence and power in government. The growth of AI has drastically altered the political landscape, and the shifting power relationships between actors involved in deploying AI have the potential to radically impact the ways in which this technology is understood, adopted and implemented in government. &nbsp;
Drawing together scholars from public policy, public management, information technology and law, this volume throws light on the implications that AI poses for existing power relationships between actors involved in the deployment and use of AI in government, and for existing power relationships between the state and citizens. It addresses these issues through three disciplinary perspectives – legal and regulatory studies, public policy and governance, and public management and innovation – in order to fully assess the interplay between AI and power in different governmental settings across the world. Chapters examine a range of themes pertinent to AI and governance, including the exercise of power, liability issues, ethical policymaking, surveillance, and the use of AI tools in public organizations. A concluding chapter maps out future directions for the study of AI and power in government.
Interdisciplinary in nature, the book will appeal to all those interested in public policy, governance, public management, law, and technology studies.
&nbsp;
Sorin Dan is Assistant Professor of Public Management at the School of Management&nbsp;of the University of Vaasa, Finland.
Niina Mäntylä is Professor of Public Law at the School of Management of the University of Vaasa, Finland.
Jane E. Fountain is Distinguished University Professor and Director of the School of Public Policy at the University of Massachusetts, Amherst, USA.

Artificial Intelligence and Power in Government

Smart Charging Infrastructures

Building a Program for Strategy and Oversight

Practical AI Governance

How to Make Sense of a Complex Future

The AI Paradox

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

Methods, Applications, and Implications

Adaptive AI in Sensor Informatics

Distributed and Parallel Computing

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 theoreticalfoundations 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: <ul> <li>Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selection</li> <li>Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation</li> <li>Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI's greater impact on information integrity</li> <li>Security attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacks</li> <li>Privacy-preserving AI techniques including federated learning and homomorphic encryption</li> </ul> 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.

Triangulating AI Security

Combining Human Thinking and Creativity with Technology to Inspire Audiences

Authentic Marketing

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;&nbsp;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Gehatnerative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.&nbsp;What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;Who This Book Is ForFrom AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation&nbsp;&nbsp;

Building and Deploying Ethical AI-Powered Solutions

Practical Generative AI: From Concept to Deployment

Embedded System and GUI Development using MATLAB for Engineering Applications

Edge AI

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