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Nithin Palal: AI-Proof

AI-Proof

What Machines Will Never Be

Many existing textbooks in behavioural finance take only the perspective of users of financial information, but ignore the implications of judgments during the preparation of financial information. This book uses a holistic approach in examining behavioural accounting and bridges this gap.The book begins by introducing the current knowledge in the emerging area of behavioural accounting and also covers the entire spectrum from the preparation of financial information, auditing, and ultimately, to the interpretation and application of financial information of external users. The comprehensive overview of human elements in accounting makes this book an invaluable resource.

This book examines behavioural accounting and bridges the gap where existing textbooks in behavioural finance take only the perspective of users of financial information, but ignore the implications of judgments during its preparation.

Behavioural Accounting

Nicole will show you how to have a kid financially, how to raise that kid, and how to manage your newfound promotion to Mom status without going broke or insane.

The Guide to Generational Wealth

Rich Mom

This open access book critically analyses a wide range of consumer law issues generated or exacerbated by the Internet of Things. 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. 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. 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. 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).

Lessons from Germany and the United Kingdom

From Smart Technologies to Smart Consumer Law

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

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

**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

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

Banking, Money and International Finance

Human-Centred Intelligent Finance

AI, Fintech, and the Future of Global Financial Systems

Hart Studies in Comparative Public Law

Theory, Practice and Problems

Automation in Governance

Corporate Governance in the Age of Artificial Intelligence

<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> </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> </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>
</div>

Dieses Buch hilft dem Leser im Umfeld der Digitalisierung bei der Begriffsklärung und stellt einschlägiges Know-how zur Verfügung.&nbsp;Was bedeutet 42? Was ist eine Subchain? Was ist der Unterschied zwischen Robotics und Artificial Intelligence? Der Schwerpunkt liegt dabei auf den Entwicklungen, die seit 2010 mit der Digitalisierung unter dem Namen "Financial Technology" eine zunehmende Verbreitung erlebt haben. Die Begrifflichkeiten umfassen grundlegende Begriffe, die Eingang in die allgemeine Sprache gefunden haben, wie etwa Artificial Intelligence oder Blockchain, aber auch fachspezifische Termini, die für das Verständnis der einzelnen Technologien notwendig sind (zum Beispiel 42, Nonce). Die Fintech-Terminologie bietet dem Leser einen konzisen und gleichzeitig anschaulich dargestellten alphabetisch geordneten Überblick mit den darin enthaltenen praxisorientierten Antworten.

Begriffe für die digitalisierte Finanzwelt

Fintech-Lexikon

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.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.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.

Artificial Intelligence and the Transformation of Organizations

Algorithmic Management

AI-Proof

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