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Steiner P: READY for the future

READY for the future

Contributions to Economics

Artificial intelligence is not just transforming the economy; it is fundamentally redefining the core principles of economic theory.
From the Phillips Curve to portfolio optimization, and from the Laffer Curve to Lewis’s dual-sector model, the core frameworks that have shaped economics for over a century are now evolving through intelligent machines, algorithms, and data-driven decision-making. In this work, Tshilidzi Marwala reanalyzes 18 fundamental principles of economic thought, such as demand and supply, rational choice, information asymmetry, mechanism design, agency theory, comparative advantage, growth, governance, and market efficiency, from an AI perspective.
Drawing on economics, artificial intelligence, finance, public policy, and global governance, the book explains how AI is changing productivity, labor markets, inflation, competition, taxation, and the relationships between states, companies, and citizens. It shows that traditional ideas based on scarcity, human labor, limited rationality, and static institutions are gradually being replaced by a modern economic framework driven by intelligent capital, algorithmic coordination, and autonomous systems. The outcome is a comprehensive and timely framework for understanding the political economy of the AI era, detailed in analysis, globally oriented, and essential for policymakers, scholars, business leaders, and anyone seeking to understand how intelligence is becoming the primary driver of economic transformation.

Artificial intelligence is not just transforming the economy; it is fundamentally redefining the core principles of economic theory.From the Phillips Curve to portfolio optimization, and from the Laffer Curve to Lewis’s dual-sector model, the core frameworks that have shaped economics for over a century are now evolving through intelligent machines, algorithms, and data-driven decision-making. In this work, Tshilidzi Marwala reanalyzes 18 fundamental principles of economic thought, such as demand and supply, rational choice, information asymmetry, mechanism design, agency theory, comparative advantage, growth, governance, and market efficiency, from an AI perspective.
 Drawing on economics, artificial intelligence, finance, public policy, and global governance, the book explains how AI is changing productivity, labor markets, inflation, competition, taxation, and the relationships between states, companies, and citizens. It shows that traditional ideas based on scarcity, human labor, limited rationality, and static institutions are gradually being replaced by a modern economic framework driven by intelligent capital, algorithmic coordination, and autonomous systems.
 
 The outcome is a comprehensive and timely framework for understanding the political economy of the AI era, detailed in analysis, globally oriented, and essential for policymakers, scholars, business leaders, and anyone seeking to understand how intelligence is becoming the primary driver of economic transformation.

How AI Rewrites Theory, Markets, and Policy

Intelligent Economies

Eine lernorientierte Einführung in die Grundzüge der mikroökonomischen Theorie, bei der drei Fragen im Zentrum des Interesses stehen: Mit welcher Güternachfrage maximiert ein privater Haushalt für ein gegebenes Einkommen und gegebene Preise seinen Nutzen? Mit welchem Güterangebot maximiert ein privates Unternehmen für gegebene Güter- und Faktorpreise seinen Gewinn? Wie bildet sich aus Angebot und Nachfrage der Marktpreis? In den Text integrierte Übungsaufgaben erleichtern eine gezielte Prüfungsvorbeitung.

Haushalte, Unternehmen, Märkte

Mikroökonomik

Una nueva forma de entender la inteligencia en un mundo que cambia constantemente. Durante años, el éxito se ha medido a través del coeficiente intelectual (IQ) y la inteligencia emocional (EQ). Sin embargo, en un entorno marcado por la incertidumbre, la velocidad y el cambio constante, estos ya no son suficientes. En AQ, Liz Tran introduce el concepto de Adversity Quotient: la capacidad de adaptarse, resistir y crecer frente a los desafíos. A partir de su experiencia como coach de líderes y fundadores de algunas de las empresas más dinámicas del mundo, presenta un enfoque práctico y aplicable para desarrollar esta habilidad clave. El libro combina investigación, ejemplos reales y herramientas concretas para ayudarte a navegar entornos complejos, tomar mejores decisiones y construir resiliencia a largo plazo. En este libro descubrirás: - Cómo fortalecer tu resiliencia en contextos inciertos - Cómo tomar decisiones en entornos cambiantes - Cómo desarrollar una mentalidad adaptable - Cómo convertir el cambio en una ventaja Una guía esencial para quienes buscan no solo adaptarse al cambio, sino aprovecharlo para crecer.A new way to understand intelligence in a constantly changing world. For years, success has been defined by IQ and emotional intelligence (EQ). But in today's fast-changing and uncertain environment, these are no longer enough. In AQ, Liz Tran introduces the concept of the Adversity Quotient--the ability to adapt, endure, and grow through challenges. Drawing on her experience coaching leaders and founders of some of the world's fastest-growing companies, she presents a practical framework for developing this essential skill. Combining research, real-world examples, and actionable tools, the book helps you navigate uncertainty, make better decisions, and build lasting resilience. In this book you will learn: - How to build resilience in uncertain environments - How to make better decisions under pressure - How to develop a more adaptable mindset - How to turn change into an advantage An essential guide for anyone who wants not just to survive change, but to grow because of it.

Una Nueva Forma de Inteligencia Para Un Mundo En Constante Cambio

Aq (Aq Spanish Edition)

Banking, Money and International Finance

Human-Centred Intelligent Finance

AI, Fintech, and the Future of Global Financial Systems

International Series in Operations Research & Management Science

This book shows how to weld deep learning to the laws that govern heavy-industry assets. Instead of treating sensor readings as detached numbers, the book teaches models to obey conservation of energy, fluid flow, and material stress. The storyline follows three recurring settings—a regional power grid, an offshore drilling head, and a 2000-hectare almond farm—to prove the concept across energy, manufacturing, and agriculture.Readers watch plain neural nets stumble when they predict outputs that break physics, then see PENN correct the error with loss penalties and custom layers that bake in the Navier-Stokes, Hooke, and Fourier equations. In benchmark tests the approach lifts predictive accuracy by up to 30 percent while trimming training energy by two-thirds. Case walk-throughs document a 25 percent cut in turbine downtime, a 50 percent reduction in drilling vibration peaks, and a 15 percent saving in irrigation power.Special features include code snippets ready to paste into PyTorch, side-by-side plots that compare raw and physics-aware outputs, and full-color schematics that trace data from sensor to dashboard. Each chapter ends with a one-page readiness checklist so teams can turn lessons into pilots without stalling in proof-of-concept limbo. A consistent American spelling palette avoids cross-Atlantic edits.By the final page a plant manager, data scientist, or graduate student can design, train, and deploy a model that predicts real-world behavior without ignoring the rules of nature. The payoff is lower energy bills, fewer unplanned shutdowns, and a faster path to net-zero targets—results that speak to both profit and sustainability.&nbsp;

This book shows how to weld deep learning to the laws that govern heavy-industry assets. Instead of treating sensor readings as detached numbers, the book teaches models to obey conservation of energy, fluid flow, and material stress. The storyline follows three recurring settings—a regional power grid, an offshore drilling head, and a 2000-hectare almond farm—to prove the concept across energy, manufacturing, and agriculture.Readers watch plain neural nets stumble when they predict outputs that break physics, then see PENN correct the error with loss penalties and custom layers that bake in the Navier-Stokes, Hooke, and Fourier equations. In benchmark tests the approach lifts predictive accuracy by up to 30 percent while trimming training energy by two-thirds. Case walk-throughs document a 25 percent cut in turbine downtime, a 50 percent reduction in drilling vibration peaks, and a 15 percent saving in irrigation power.Special features include code snippets ready to paste into PyTorch, side-by-side plots that compare raw and physics-aware outputs, and full-color schematics that trace data from sensor to dashboard. Each chapter ends with a one-page readiness checklist so teams can turn lessons into pilots without stalling in proof-of-concept limbo. A consistent American spelling palette avoids cross-Atlantic edits.By the final page a plant manager, data scientist, or graduate student can design, train, and deploy a model that predicts real-world behavior without ignoring the rules of nature. The payoff is lower energy bills, fewer unplanned shutdowns, and a faster path to net-zero targets—results that speak to both profit and sustainability.

The Next Frontier of Artificial Intelligence

Physics-Informed Neural Networks for Industrial Applications

Turn AI investment into scalable business impact by enabling your workforce to build the solutions they actually need. From AI Users to Solution Builders is a strategic guide for senior AI, technology, and transformation leaders who want to move beyond underperforming, centralized AI initiatives, and unlock real enterprise value. As organizations struggle to translate AI into measurable results, this book shows how shifting from top-down deployment to workforce-led solution building creates faster adoption, stronger ROI, and sustained innovation. Written by experienced AI leader Wei Manfredi, this book combines strategic insight with practical execution. It draws clear parallels between failed digital transformation efforts and current AI adoption challenges, while demonstrating how natural language interfaces are reshaping access to AI. By enabling non-technical professionals to design their own tools, organizations can embed AI directly into workflows, decision-making, and daily operations. You'll learn how to: - Shift from centralized AI models to workforce-driven innovation - Enable employees to build AI solutions for real workflows - Design scalable infrastructure for AI solution development - Apply change management to overcome resistance and drive adoption &gt;Featuring real-world examples from organizations including Lululemon, Google, Oracle, and JPMorgan Chase, this book provides a proven framework for embedding AI into everyday collaboration, accelerating innovation, and future-proofing your organization's competitive advantage. Themes include: AI strategy, AI adoption, workforce transformation, AI governance, enterprise AI, change management

Scaling AI Across Your Organization

From AI Users to Solution Builders

Leveraging Data Science and Machine Learning

Predictive Intelligence for Transforming Healthcare Insurance

Das moderne Recht wirft viele grundsätzliche Fragen auf: Ist Recht mit Gerechtigkeit notwendig verbunden? Was ist überhaupt Recht und was Gerechtigkeit? Wie verhalten sich Recht und Moral zueinander? Warum und unter welchen Bedingungen ist Recht verbindlich?
Diesen und zahlreichen anderen Fragen zu Recht, Ethik und ihrer Bedeutung für Staat und Gesellschaft geht Mahlmann in seinem Lehrbuch anhand von vielen Beispielen nach - von der Folter über die neuen Herausforderungen Künstlicher Intelligenz für das Recht bis hin zu Perspektiven einer internationalen Friedensordnung in einer Epoche neuer Kriege. Dabei schlägt er einen Bogen von der Antike bis in die Gegenwart und entwickelt vor diesem historischen Hintergrund Antworten auf zentrale systematische Fragen der Rechtsphilosophie und Rechtstheorie.

Das moderne Recht wirft viele grundsätzliche Fragen auf: Ist Recht mit Gerechtigkeit notwendig verbunden? Was ist überhaupt Recht und was Gerechtigkeit? Wie verhalten sich Recht und Moral zueinander? Warum und unter welchen Bedingungen ist Recht verbindlich?
Diesen und zahlreichen anderen Fragen zu Recht, Ethik und ihrer Bedeutung für Staat und Gesellschaft geht Mahlmann in seinem Lehrbuch anhand von vielen Beispielen nach – von der Folter über die neuen Herausforderungen Künstlicher Intelligenz für das Recht bis hin zu Perspektiven einer internationalen Friedensordnung in einer Epoche neuer Kriege. Dabei schlägt er einen Bogen von der Antike bis in die Gegenwart und entwickelt vor diesem historischen Hintergrund Antworten auf zentrale systematische Fragen der Rechtsphilosophie und Rechtstheorie.

Rechtsphilosophie und Rechtstheorie

**This edition will be released in March 2027. If you want to get a copy ahead of then, have a look at our hardback edition, released in May 2026.** 'If you're worried about wasting your life, read this book.' Rutger Bregman, author of Humankind A ridiculously in-depth guide to finding a fulfilling and impactful career in an age of AI. You have about 80,000 hours in your career: 40 hours a week, 50 weeks a year, for 40 years. This means your choice of career is the most important decision you'll ever make. Choose well, and you can have a more rewarding, interesting life, and also help solve some of the world's most pressing problems. Choose poorly, and you could waste decades. So what should you do? Most advice is based on no research, instead offering (misleading) platitudes like 'follow your passion'. It's surprisingly out of date, focusing on traditional paths rather than those most likely to thrive in an age of AI. And if you want to help others, the advice is even more simplistic: telling you to choose careers like nurse or doctor, when there are paths in which almost anyone can save more lives. In fact, some careers have hundreds of times more impact than others, but most of us have no idea which ones. Benjamin Todd is the founder of 80,000 Hours, a non-profit that's spent over ten years researching how to have a meaningful and impactful career. Here he introduces a tried-and-tested toolkit framework that has already led thousands of people to change their path. It covers:<ul><li>What really makes for a dream job, and why finding it probably doesn't involve 'following your passion'.</li><li>How to identify which global problems are most pressing, and why they might involve AI as much as climate change.</li><li>Which roles are likely to be automated, and how to ensure yours isn't among them.</li></ul> It's also full of practical tips and tools. You'll come away with a plan to use your 80,000 hours in a way that's rewarding and fulfils your potential to make a difference. 'This career guide is among the most thoughtful and grounded I've seen.' Cal Newport, author of Deep Work 'A remarkably rigorous guide about how to find that perfect career for you.' Ali Abdaal, author of Feel-Good Productivity 'Based on evidence and good sense, not platitudes.' Steven Pinker, author of The Better Angels of our Nature 'The most informative book you can read if you're stuck trying to make one of life's most important decisions.' Hannah Ritchie, author of Not the End of the World

How to Have a Fulfilling Career That Does Good

80,000 Hours

Demis Hassabis, DeepMind and the Quest for Superintelligence

The Infinity Machine

Artificial intelligence has crossed the liability Rubicon. It is no longer a backend IT experiment governed by soft ethics, voluntary frameworks, and vague policy statements; it is a material financial asset driving enterprise revenue and unprecedented legal exposure. As we transition into Industry 5.0, algorithms are making autonomous, consequential decisions at machine speed. Yet, corporate boards and risk committees are attempting to govern these gigahertz systems using static compliance checklists designed for a slower, simpler era. The result is a looming crisis of algorithmic discrimination, regulatory friction, and catastrophic reputational decay. When an opaque, black-box model hallucinates financial advice or leaks proprietary trade secrets, the ensuing failure cascade can destroy billions in market capitalization overnight.AI Governance and Risk Management provides the definitive, boardroom-ready blueprint for surviving the fragmented global regulatory dragnet. This book dismantles the illusion of traditional "Human-in-the-Loop" oversight, proving that true algorithmic safety requires hard, architectural constraints. Drawing on decades of technology operations and academic leadership, the text translates classical financial auditing principles into executable machine logic. The author brings unparalleled practical experience-from supporting six Silicon Valley startups in client-facing roles to develop computer vision use cases, to leading over 30 continuous improvement projects for Cisco Systems. Having authored foundational texts on Segregation of Duties (SOD) and Hierarchical SOD two decades ago, and backed by 15 years of teaching financial risk management, this expertise culminates in the AI Integrity Framework (AIIF). This Hamiltonian approach empowers organizations to embed continuous improvement cycles and strict SOD directly into their deployment pipelines.Readers will discover how to engineer automated "Build Breaks," architect deterministic kill switches, and secure the unstructured data supply chain against copyright contamination and intellectual property theft. By shifting from reactive public relations to proactive "Governance as Code," Chief Risk Officers, legal counsel, and audit committees can mathematically prove fiduciary control to regulators across overlapping jurisdictions, from California to the European Union.Authored by a veteran industry practitioner, creator of five previous Wiley &amp; Sons texts, and current developer of the Industry 5.0 and AI curriculum at Santa Clara University, this release is not a theoretical ethics manual. It is an uncompromising operational survival guide. It provides the exact structural tools necessary to transform artificial intelligence from an unquantified off-balance-sheet liability into a fully auditable, legally defensible, and highly profitable enterprise asset.

The AI Integrity Framework (AIIF) for Enterprise Control and Regulatory Compliance

Auditable AI Governance and Risk Management

Corporate Governance in the Age of Artificial Intelligence

READY for the future

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