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Kudhail V: Authentic Marketing

Authentic Marketing

Combining Human Thinking and Creativity with Technology to Inspire Audiences

This book frames "theories" as tools for understanding and applying real-world phenomena, bridging classic scholarship with the unprecedented challenges and opportunities posed by machine intelligence, digital platforms, and shifting cultural dynamics. Structured in four sections—Foundational Media Theories, Interaction and Impact Theories, Critical Media Perspective Theories, and Advanced and Future-Facing Theories—the textbook progresses from essential theories like Marshall McLuhan’s&nbsp;and Neil Postman's Media Ecology Theory to cutting-edge concepts like Ambient Media and Attention Economy Theories. With chapters addressing topics such as algorithmic influence, media ecology, participatory media, and posthuman digital embodiment, the book offers a holistic view of media’s past, present, and evolving futures. The 12 future-focused media theories presented include the introduction of Quantum Media Theory and Gestalt Media Theory as an integrated, cross-disciplinary, approach to understanding media theory from a wholistic “whole is more than the sum of its parts” perspective.Jack Myers&nbsp;is a Senior Lecturer on New Media Theory at the University of Arizona School of Information Sciences, author of&nbsp;The Myers Report&nbsp;column at Substack, and&nbsp;founder of the MediaVillage Education Foundation. He was an adjunct professor of Media Business at NYU Steinhardt. His honors include the Excellence in Education Award from the Global Forum on Education &amp; Learning, the George Foster Peabody Award, Academy and EMMY Award nominations for documentary filmmaking, the Crystal Heart Award from the Heartland Film Festival, the World Music Award as its producer, and a Top 10 Most Admired Leader in Tech by Pinnacle Tech Insights.

This book frames "theories" as tools for understanding and applying real-world phenomena, bridging classic scholarship with the unprecedented challenges and opportunities posed by machine intelligence, digital platforms, and shifting cultural dynamics. Structured in four sections—Foundational Media Theories, Interaction and Impact Theories, Critical Media Perspective Theories, and Advanced and Future-Facing Theories—the textbook progresses from essential theories like Marshall McLuhan’s&nbsp;and Neil Postman's Media Ecology Theory to cutting-edge concepts like Ambient Media and Attention Economy Theories. With chapters addressing topics such as algorithmic influence, media ecology, participatory media, and posthuman digital embodiment, the book offers a holistic view of media’s past, present, and evolving futures. The 12 future-focused media theories presented include the introduction of Quantum Media Theory and Gestalt Media Theory as an integrated, cross-disciplinary, approach to understanding media theory from a wholistic “whole is more than the sum of its parts” perspective.

Theories of Culture, Communications, and Consciousness

Media Ecology for the 21st Century

Building a Program for Strategy and Oversight

Practical AI Governance

Smart Charging Infrastructures

Augmented and Virtual Reality in Healthcare

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

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

Learn to create lasting impact for global fashion brands through branding, marketing and consumer engagement.

Strategic Management and Promotion for Global Fashion Brands

International Fashion Marketing

Enhancing Reflective Teaching in Higher Education

Redefining Teaching and Learning

GenAI in Higher Education

Resistance in the Age of Artificial Intelligence

Algorithmic Capital

Depending on who you ask, artificial intelligence is our salvation or our doom; an overhyped, bigoted artist or the ticket to immortality and utopian abundance; just another tech fad or the last invention we need ever make. The AI discourse is confusing, messy, and frustrating. But the technology — and our response to it — will shape the future, whether or not we’re part of the conversation.
Obsolete is&nbsp;for those who are interested in learning more about AI, but unsure of where to start and who to believe. They may feel intimidated by the technical jargon, put off by dry and abstract prose, or skeptical of the loudest voices on the issue (like Elon Musk and Sam Altman). Obsolete will introduce readers to the basics of AI, the idea that it could lead to human extinction, the roiling three-sided debate surrounding extinction fears, and the people and companies trying to build artificial general intelligence (AGI) — that which can outwit humans across the board. The book will grapple with the core arguments animating the AI debates, which are, by turns, uncritically parroted and unduly dismissed. It will also cut through industry hype while seriously entertaining the implications of AGI.
The risk that AGI could result in our extinction is being recognized by a significant and growing number of leading AI researchers, industrialists, and policymakers, along with the wider public. Existential risk from AI has been explored in other books, but Obsolete will be the first to center its analysis on how both capitalist and great power competition make AI more dangerous.
Obsolete will also tackle questions like: Can machines actually outsmart humanity? If so, when could that happen? If AGI is possible, is it inevitable? Why are people trying to build a technology they claim could end the world? Is the idea of AI-driven extinction the product of a big tech conspiracy aiming to hype the technology and control its regulation? Why has the left mostly ignored or dismissed existential risk from AI? Why do some powerful techies welcome human extinction? How could AI enable stable authoritarian regimes? How could killer robots reshape war and the balance of power? What do China and the US want from AI? And why has it become the front line of their brewing Cold War? And finally: What can and should the left do about it?

Power, Profit, And The Race For Machine Superintelligence

Obsolete

The MIT Press Essential Knowledge series

How tech companies, journalists, and policymakers can prevent AI decision-making from going wrong. Our lives are increasingly governed by automated systems influencing everything from medical care to policing to employment opportunities, but researchers and investigative journalists have proven that AI systems regularly get things wrong. Auditing AI is a first-of-its-kind exploration of why and how to audit artificial intelligence systems. It offers a simple roadmap for using AI audits to make product and policy changes that benefit companies and the public alike. The book aims to convince readers that AI systems should be subject to robust audits to protect all of us from the dangers of these systems. Readers will come away with an understanding of what an AI audit is, why AI audits are important, key components of an audit that follows best practices, how to interpret an audit, and the available choices to act on an audit’s results. The book is organized around canonical examples: from AI-powered drones mistakenly targeting civilians in conflict areas to false arrests triggered by facial recognition systems that misidentified people with dark skin tones to HR hiring software that prefers men. It explains these definitive cases of AI decision-making gone wrong and then highlights specific audits that have led to concrete changes in government policy and corporate practice. The Marquand House Collective: Marc Aidinoff, Lena Armstrong, Esha Bhandari, Ellery Roberts Biddle, Motahhare Eslami, Karrie Karahalios, Nate Matias, Danaé Metaxa, Alondra Nelson, Christian Sandvig, and Kristen Vaccaro.

Auditing AI

Authentic Marketing

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