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Revolution of AI and Machine Learning
for Design and Automation

Revolution of AI and Machine Learning for Design and Automation

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection


<p>
</p><p><b>This is the updated and expanded edition of the only radical, multidisciplinary, international introduction to intellectual property in its socio-economic contexts. </b><br><br>Bringing together voices from law, sociology, economics, political science, and cultural studies from across the Global North and Global South, experts and activists Mat Callahan and Jim Rogers offer a detailed history and analysis of IP before asking critical questions designed to underscore its importance in the broader struggle between capital and the commons. What are the broader implications of IP, whether for agribusiness, pharmaceuticals, education, or entertainment? Is IP law a fundamental pillar of our culture, or has it been used to protect and assert the ownership of, and thus limit, the spread of ideas and creativity?<br><br>With a chapter structure moving from historical and theoretical foundations to industry-specific conflicts and policy analysis, this book covers the breadth and depth of IP areas, including patents, copyrights, trademarks, and trade secrets, as well as emerging issues such as digital rights and biotechnology.<br><br>Key features include:<br><br>- voluminous, up-to-date case studies, both Western and non-Western;<br><br>- an extensive, jargon-busting list of abbreviations of specialized or technical terms;<br><br>- a new chapter on copyright and socialism; <br><br>- revisions and updates accounting for the seismic impacts of artificial intelligence.<br><br>This is a foundational must-read for all researchers, students, and activists interested in critical analyses of IP laws and policies--not just their legal frameworks, but also their broader social, economic, and political implications.</p><p></p>


A Radical Guide to the Past, Present, and Future of the Ownership of Ideas

Rethinking Intellectual Property


<p>
</p><p>'Michael Auerbach has one of the most original minds I've ever encountered . . . This is a dazzling and entertaining book . . . But it is also an urgent warning' <b>PATRICK RADDEN KEEFE, author of Empire of Pain </b></p><p><b>A brilliant exploration of the phenomenon of prediction markets that asks: what happens when democracy becomes another commodity? </b></p><p>Prediction markets are sweeping the world, fundamentally changing the nature of gambling - and the very ways in which we attempt to divine the future.</p><p><br>Posed as binary 'Yes/No' questions about future events, prediction markets allow any difference of opinion - whether on pop culture or politics, wars or assassinations - to be financialised. A regulatory minefield with moral implications that divide nations, governments and bettors, they have proved as effective at accurately predicting the timing of missile strikes as they have at calling the outcomes of elections.</p><p>But are these markets really a new phenomenon? And what does it mean when those tasked with safeguarding democracy hold financial stakes in its volatility?</p><p>From Renaissance Rome, where merchants wagered on papal conclaves, through nineteenth-century London's wartime coffee house gossip, to the Pentagon's ill-fated terrorism futures exchange and blockchain-based betting on Polymarket today, <i>The Big Bet </i>tells the gripping five-hundred-year story of a permissive regime that no one designed and no one voted for.</p><p>Drawing on deep expertise, Michael Auerbach examines how these markets are changing our world, and what they mean for the future where democracy becomes just another commodity to be priced and traded. Because prediction markets are not an anomaly - they are the logical endpoint of a society that has applied market capitalism to every aspect of life. And in a world where everything is a wager and nothing is off limits, knowing where the chips will fall matters more than ever.</p><p></p>


Prediction Markets and the Future of Democracy

The Big Bet


<p>
</p><p><b>This open access book critically analyses a wide range of consumer law issues generated or exacerbated by the Internet of Things.</b><br><br>The Internet of Things challenges many of the dichotomies which have shaped current laws: goods-services, hardware-software, tangible-intangible, online-offline. The book focuses on 4 specific challenges: things-as-service, liability, bricking of physical devices through software control, and the potential for enhanced personalisation, especially for digital assistants.<br><br>The chapters analyse specific applications such as smart homes, connected cars, and consumer wearables with a view to providing innovative solutions to the new challenges posed by the Internet of Things, and to steer the development of the Internet of Things in a fairer direction.<br><br>As well as offering an important analysis of the potential for smart consumer laws in the Internet of Things, the book also provides important insights into the growing divergence between Germany's and the UK's consumer laws since Brexit.<br><br><i>The ebook editions of this book are available open access under a CC BY 4.0 licence on bloomsburycollections.com. Open access was funded by UKRI (Arts and Humanities Research Council) and the German Research Foundation (DFG).</i></p><p></p>


Lessons from Germany and the United Kingdom

From Smart Technologies to Smart Consumer Law


<p>
</p><div><div>Das erste Lexikon im Fintech-Bereich stellt mit über 800 Stichworten einschlägiges Know-how zur "Financial Technology" bereit und hilft damit bei der Klärung aktueller Begrifflichkeiten. Dem interdisziplinären Charakter der Fintech-Entwicklung zufolge sind die Begriffe an der Schnittstelle von (Wirtschafts-)Informatik, Finanzwissenschaft und Innovationsmanagement verortet. Die Fintech-Terminologie richtet sich dabei mit ihren konzise beschriebenen und mit anschaulichen Grafiken ergänzten Inhalten gleichermaßen an Studierende sowie Interessierte aus Wissenschaft und Praxis.</div><div><br></div><div>Die Autoren</div><div>Prof. Dr. Rainer Alt ist Professor für Wirtschaftsinformatik an der Universität Leipzig und Autor zahlreicher Publikationen im Fintech-Bereich.&nbsp;</div><div>Dr. Stefan Huch ist Senior Director bei Capgemini Invent und vertritt die Professur Digitalisierung von Unternehmensprozessen an der Hochschule Merseburg.</div><div><br></div><div>Würdigungen</div><div>"Aus meiner Sicht als Führungskraft einer global agierenden Bank ist das Fintech-Lexikon ein sehr gelungenes Nachschlagewerk, das alle relevanten Begrifflichkeiten in einer sehr verständlichen Art strukturiert darstellt und erläutert- eine gute Stütze, um in der Praxis die relevanten Begrifflichkeiten der Fintech-Szene im Überblick zu haben." Achim Lebeau, COO, DB Privat- und Firmenkunden AG</div><div>"Selbst gründen bedeutet in vielen Bereichen ins kalte Wasser geworfen zu werden. Man muss in kürzester Zeit zum Allround-Experten werden: im Sales, im Marketing, im Finance oder im Fundraising. Hier eine strukturierte Übersicht aller Begrifflichkeiten der Startup-Welt an der Hand zu haben, ist eine enorme Hilfe." Jenny Saft, Geschäftsführung und Co-Founderin, Oviavo GmbH</div><div>"Dieses Fintech-Lexikon bietet einen hervorragenden Orientierungsrahmen, indem es alle relevanten Begrifflichkeiten im Kontext der digitalisierten Finanzwelt aufgreift. Diese sind konsequent strukturiert und inverständlichen Worten definiert. Ein wertvoller Beitrag für die Praxis." Thomas Zerndt, CEO, Business Engineering Institute St. Gallen AG</div><p></p>
</div>

Begriffe für die digitalisierte Finanzwelt

Fintech-Lexikon

The Essential Guide to Launching Your Career in Artificial Intelligence

Break into AI

Research in Creative Writing

Writing Human in the Age of AI


<p>
</p><p>Algorithmic Management: Artificial Intelligence and the Transformation of Organizations is the first comprehensive textbook designed to prepare management students to engage critically and responsibly with artificial intelligence in organizations. Bringing together classical management theory, contemporary AI applications, and systematic power analysis, the book equips future managers to both harness AI's potential and confront its risks.</p><p>Structured in three parts, the book begins with foundational frameworks-socio-technical systems theory, technology and society perspectives, and core management theories reinterpreted for the AI era. It then examines applications across all major management functions-Marketing, HR, Finance, Operations, Supply Chain, and Strategy-using over 30 real-world case studies drawn from different industries, parts of the world, and types of organizations from multinational corporations to nonprofits and social ventures. The final section addresses organizational design, leadership, governance, and the future of algorithmic organizations. Throughout, pedagogical features-learning objectives, vignettes, discussion questions, toolkits, and ethical dilemmas-make the book classroom-ready and adaptable across MBA, undergraduate, and executive education settings. The text advances an approach of pragmatic criticality: AI is neither celebrated uncritically nor rejected outright but treated as a socio-technical phenomenon requiring managerial judgment, ethical reflection, and contextual awareness.</p><p>By bridging management theory, functional practice, and critical perspectives, this book fills a critical market gap and is positioned to become the definitive teaching resource on AI and management for business education worldwide.</p><p></p>


Artificial Intelligence and the Transformation of Organizations

Algorithmic Management

International Series in Operations Research & Management Science

<p>This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.</p><p>The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.</p>

Valid Causal Inference from Observational Data

Banking, Money and International Finance

Human-Centred Intelligent Finance

AI, Fintech, and the Future of Global Financial Systems

Revolution of AI and Machine Learning for Design and Automation

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