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Chris Fregly: AI Systems Performance
Engineering

AI Systems Performance Engineering

Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch

Wendepunkte in der Analogen und Digitalen Welt

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

Quantum Computing, Machine Learning, and Bio-inspired Optimization

Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

Advances in Industrial and Systems Engineering

Advances in Human-AI Collaboration

Handbooks in Communication and Media

"The Handbook of Artificial Intelligence and Journalism will be the first of its kind in seeking to unveil the algorithmic transformation of journalism and define this new research field by highlighting the interplay between journalism and technological advancements. The rise of AI and its pervasive reach has ushered in a new era of practice and research in the field of communication, particularly in the realm of journalism, where it is reshaping not only the organizational structure of the news but the way it is produced, distributed, and consumed. This Handbook delves into the AI's potential for the Journalism field, as well as possible outcomes of the integration of intelligent systems into news journalism. Incorporating studies from around the world, including the global south, chapters will provide an inclusive and holistic understanding of the algorithmic transformation in journalism across different cultural, economic, and societal contexts. Topics covered include AI and mis/disinformation, ethical implications, emerging business models in algorithm-driven journalism, and AI governance. For students and scholars of communication and media studies or journalism, this handbook offers a theoretical framework, key concepts, and new approaches to algorithm-driven journalism, aiding them in the comprehension of the potentials and impact of the use of intelligent technologies in journalism"--

The Handbook of Artificial Intelligence and Journalism

Foundations, Techniques and Applications

Edge AI

Micro & Nano Technologies

The advances in microsystems offer new opportunities and capabilities to develop systems for biomedical applications, such as diagnostics and therapy. In its third edition, Microsystems for Bioelectronics offers a comprehensive treatment of microsystems by thoroughly covering basic physics, chemistry, biology, materials, and engineering concepts, with an emphasis on the performance limits associated with the shrinking scale of microsystems. Microsystems for Bioelectronics: Scaling and Performance Limits considers physical principles and trends in extremely scaled autonomous microsystems such as integrated intelligent sensor systems, with a focus on energy minimization. It explores the implications of energy minimization on device and system architecture. It further details behavior of electronic components and its implications on system-level scaling and performance limits. In particular, fundamental scaling limits for energy sourcing, sensing, memory, computation and communication subsystems are developed and new applications such as optical, magnetic and mechanical sensors are presented. This fully revised and updated edition includes information on DRAM and 3D NAND semiconductor memories, betavoltaic energy conversion, advanced packaging, recent technology trends, and new technology roadmaps. The new edition of this well-proven book with its unique focus and interdisciplinary approach shows the complexities of the next generation of nanoelectronic microsystems in a clear and easy-to-understand manner, and is aimed for a broad audience within several disciplines.

Scaling and Performance Limits

Microsystems for Bioelectronics

Distributed and Parallel Computing

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Tiny Machine Learning: Design Principles and Applications

Human Machine Integration in Industry 5.0

Digital Twin and Digital Triplet Technologies

Multi-Agent Control Systems

Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource capturing the challenges and mitigation strategies involved in designing such systems. Divided into nine sections, the book covers the most important applications of safety-critical systems, helping readers understand how related problems are being solved in different domains/problem settings. The goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety. The authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models.

A Comprehensive Guide

Artificial Intelligence and Machine Learning for Safety-Critical Systems

AI Systems Performance Engineering

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