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Navarro R: Work Reimagined

Work Reimagined

The AI Era

Equips legal professionals and stakeholders with a clear, authoritative, and practical guide to the UK legal treatment of digital payments and tokenised assets. The Law of Digital Payments explores the evolving landscape of UK financial law as it adapts to innovations in digital finance. With fintech disrupting traditional models of value exchange, legal professionals must navigate complex regulatory challenges concerning digital wallets, open banking APIs, smart contracts, decentralised finance (DeFi), and the treatment of tokenised assets under common law. This work delves into how UK courts and regulators are addressing these innovations. It provides clarity on the current legal treatment of payment tokens, security tokens, cryptoassets and non-fungible tokens (NFTs). It also investigates the impact of the Financial Services and Markets Act 2023, the Bank of England's stance on central bank digital currencies (CBDCs), and how the Payment Services Regulations and E-Money Regulations are applied to innovative business models. Drawing upon regulatory guidance, case law, and international developments, the book is designed to help practitioners, regulators, academics, and financial institutions understand and navigate the complex intersection of law and digital finance. This title is included in Bloomsbury Professional's Banking and Finance Law online service.

The Law of Digital Payments

This book introduces the subject of artificial intelligence (AI), its structures, uses, application to the artificial neural network, technology applications, and the advantages and disadvantages of its use, including its potential weaknesses and drawbacks. AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs. AI technology is widely used throughout industry, government, and science.Some high-profile applications include advanced web search engines (e.g., Google Search); recommendation systems (used by platforms such as YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT, Apple Intelligence, and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI. It would appear that much of specific AI has filtered into general applications, often without being called AI because once something becomes useful and common enough, it is no longer labelled as AI.The growing use of AI in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic and commercial sectors and areas of everyday life. Its impact is felt across job markets, healthcare, government, industry, education, propaganda, and disinformation and deception, whether deliberate or otherwise. This raises questions about the long-term effects, ethical implications, and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

Advantages, Disadvantages, Benefits, and Threats

Artificial Intelligence

A dazzling exploration of the phenomenon of prediction betting markets that asks: what happens when democracy becomes another commodity? For many, Donald Trump's decisive election victory over Kamala Harris in 2024 was a shock. Traditional polls had the race on a razor's edge. But, for those with eyes on prediction betting markets, a very different story was being told. In recent years, prediction markets have swept the world, fundamentally changing the nature of the gambling market, and making their youthful pioneers billionaires. Posed as binary 'Yes/No' questions about future events, prediction bets allow any difference of opinion - whether on politics, wars, assassinations or popular culture - to be financialised. But are these markets really a new phenomenon? What does it mean when markets get closer to the truth than the institutions we built to protect us? And what happens when those tasked with safeguarding democracy possess financial stakes in its volatility? From Renaissance Rome, where merchants wagered on papal conclaves, through London coffee house gossip during the Napoleonic Wars, to the Pentagon's ill-fated terrorism futures exchange, The Big Bet describes the full five-hundred-year arc of a permissive regime which no one designed and no one voted for. Drawing on deep expertise, Michael Auerbach examines how these markets are changing our world, and what they mean for the future of our democratic self-governance. Because they 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 when everything is a wager and nothing is off limits, knowing where the chips will fall matters more than ever.

Prediction Markets and the Future of Democracy

The Big Bet

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Build AI Models from Scratch (No PhD Required) Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch. No experience with deep learning required! Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory. You’ll start from the basics, and using PyTorch with real datasets, you’ll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub. You’ll build and train models to:&nbsp; <ul><li>Classify and analyze images, sequences, and time series</li><li>Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models</li><li>Process natural language with recurrent neural networks and transformers</li><li>Model molecules and physical systems with graph neural networks</li><li>Improve continuously through reinforcement and active learning</li><li>Predict chaotic systems with reservoir computing</li></ul> Whether you’re an engineer, scientist, or professional developer, you’ll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you’ll move from using AI tools to creating them.

Deep Learning Crash Course

This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)—from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level—an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:
<ul>
<li>Pretraining - Building general linguistic and semantic understanding.

</li><li>Midtraining - Expanding domain-specific capabilities and adaptability.

</li><li>Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.

</li><li>Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.



The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.
By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.
What You’ll Learn
<ul>
<li>How to configure and optimize your development environment using PyTorch

</li><li>The mechanics of tokenization, embeddings, normalization, and attention mechanisms.

</li><li>How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.

</li><li>How to integrate custom CUDA kernels to accelerate transformer computations.

</li><li>The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.

</li><li>Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.

</li><li>How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.





Who this book is for:
Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.
</li></ul></li></ul>

Design, Train, and Deploy LLMs with PyTorch

Building Large Language Models from Scratch

Routledge Studies in Innovation, Organizations and Technology

Digital platform organizations have become integral to everyday life, delivering services and products while reshaping entire industries. Yet platforms are only one manifestation of a broader phenomenon: digital business ecosystems. These ecosystems extend beyond single firms or platforms, encompassing networks of users, complementors, institutions, and infrastructures that together create, transform, and distribute value.Value Creation in Digital Platforms and Business Ecosystems addresses the complexity of how both digital platforms and business ecosystems create and deliver value in the networks they construct, facilitate and control. Drawing on cutting-edge research from the international academic community, it provides an in-depth investigation of the mechanisms, dimensions, and challenges of value creation in digital contexts. This timely volume explores questions such as: How do digital platforms reconfigure traditional value chains? In what ways do ecosystems enable new forms of collaboration, entrepreneurial activity, and cultural participation? What tensions emerge when ecosystems foster innovation but also create dependencies, exclusion, or algorithmic bias? How do ecosystems affect labor markets, sustainability, and the blurring of boundaries between the physical and virtual?It is an essential reference text for researchers, students, and practitioners interested in the theoretical aspects and practical applications of value creation in digital platforms and business ecosystems.

Networks, Relationships and the Platform Markets

Value Creation in Digital Platforms and Business Ecosystems

Machine learning has become an essential component of modern business analytics -- driving insights, optimizing operations, and supporting strategic, data-informed decisions. This textbook offers a hands-on and accessible introduction to applying machine learning in business settings using R, with a strong emphasis on the tidy ecosystem. Key packages such as tidyverse, tidymodels, and tidytext provide a cohesive and efficient framework for data analysis, modeling, and communication.Balancing theoretical foundations with applied practice, the book introduces mathematical ideas in a clear, intuitive manner, always anchored in realistic business scenarios. Suitable for the classroom and independent research, readers will gain the confidence and tools to apply the tidy ecosystem effectively, from data preparation to modeling to interpretation, enabling reproducible, interpretable, and impactful analytics in dynamic business environments.Bowei Chen
 is a Professor of Business Analytics and Artificial Intelligence at the Adam Smith Business School, University of Glasgow, UK, and the Founding Programme Director of the MSc in Business Analytics programme. He received his PhD in Computer Science from the University College London. His research interests include business analytics, artificial intelligence, machine learning, and data science applications in management, finance, and the social sciences. He has published in leading academic conferences, journals, and books in these fields, and has undertaken international research collaborations and academic appointments across different institutions.
 Tianyuan Huang
 is a Lecturer of Data Science and Big Data Technologies at the School of Data Science, Zhejiang University of Finance &amp; Economics, China. He earned his PhD from Fudan University and has authored three R programming books in China. His current research mainly focuses on quantitative science studies, and he is also dedicated to leveraging data science to rapidly build domain expertise and advance scientific knowledge discovery. In addition to his publications, he supports the R community by maintaining several CRAN packages, such as akc for automated knowledge classification and tidyfst for efficient tidy data manipulation.

Machine learning has become an essential component of modern business analytics -- driving insights, optimizing operations, and supporting strategic, data-informed decisions. This textbook offers a hands-on and accessible introduction to applying machine learning in business settings using R, with a strong emphasis on the tidy ecosystem. Key packages such as tidyverse, tidymodels, and tidytext provide a cohesive and efficient framework for data analysis, modeling, and communication.Balancing theoretical foundations with applied practice, the book introduces mathematical ideas in a clear, intuitive manner, always anchored in realistic business scenarios. Suitable for the classroom and independent research, readers will gain the confidence and tools to apply the tidy ecosystem effectively, from data preparation to modeling to interpretation, enabling reproducible, interpretable, and impactful analytics in dynamic business environments.

A Tidy Approach

Business Analytics with R

A frank, hands-on guide to using AI at work, unpacking for the curious and skeptical alike the “24 Laws” of AI and revealing strategies that businesses of every size can use to free up time, innovate, and add to the bottom line—from a Wall Street Journal tech columnist “The antidote to AI panic. Read it. You’ll breathe easier.”—Scott Galloway, NYU Stern School of Business professor and co-host of Pivot with Kara Swisher “A clear, practical, and hype-free guide to the AI revolution that will resonate with anyone trying to figure out the how to make AI deliver real value.”—Ethan Mollick, Wharton professor and New York Times bestselling author of Co-Intelligence AI is nothing to be afraid of. After all, AI is merely software. It’s great at some things and (at least right now) terrible at others. But for workers who take time to experiment with AI and develop expertise, AI will make them more productive and more creative, saving them time, giving them job security, and boosting their income. In How to AI, Wall Street Journal columnist Christopher Mims introduces readers to people just like them who are at the forefront of using AI in the world of work. Imagine a freelance lawyer who suddenly has a whip-smart assistant to help her nail every deposition. Or a mom-and-pop contractor whose new software tool is automating construction bids that used to eat up hundreds of hours. But even as half a billion people around the world have leapt at the chance to use ChatGPT and other tools, millions of us have stayed on the sidelines. Are you one of them? Maybe you feel you should be using AI tools, but you don’t know where to begin. Or maybe you love AI but find yourself struggling to get your co-workers or employees on board. In How to AI, Mims teaches readers twenty-four simple but eye-opening “laws” about AI and how we should approach it, including: • AI is an assistant, not a replacement. • AI isn’t creative, but it can help you be. • Give AI your least favorites things to do. • AI can’t create finished products, but it’s great at prototypes. Animated by the wit and brilliant explanatory power that have earned Mims’s Wall Street Journal columns a devoted following, How to AI will prepare readers to become a part of the AI revolution—and, most important, arm them with the tools to make it work for them.

A frank, hands-on guide to using AI at work, unpacking for the curious and skeptical alike the “24 Laws” of AI and revealing strategies that businesses of every size can use to free up time, innovate, and add to the bottom line—from a Wall Street Journal tech columnist “The antidote to AI panic. Read it. You’ll breathe easier.”—Scott Galloway, NYU Stern School of Business professor and co-host of Pivot with Kara Swisher “A clear, practical, and hype-free guide to the AI revolution that will resonate with anyone trying to figure out the how to make AI deliver real value.”—Ethan Mollick, Wharton professor and New York Times bestselling author of Co-Intelligence AI is nothing to be afraid of. After all, AI is merely software. It’s great at some things and (at least right now) terrible at others. But for workers who take time to experiment with AI and develop expertise, AI will make them more productive and more creative, saving them time, giving them job security, and boosting their income. In How to AI, Wall Street Journal columnist Christopher Mims introduces readers to people just like them who are at the forefront of using AI in the world of work. Imagine a freelance lawyer who suddenly has a whip-smart assistant to help her nail every deposition. Or a mom-and-pop contractor whose new software tool is automating construction bids that used to eat up hundreds of hours. But even as half a billion people around the world have leapt at the chance to use ChatGPT and other tools, millions of us have stayed on the sidelines. Are you one of them? Maybe you feel you should be using AI tools, but you don’t know where to begin. Or maybe you love AI but find yourself struggling to get your co-workers or employees on board. In How to AI, Mims teaches readers twenty-four simple but eye-opening “laws” about AI and how we should approach it, including: • AI is an assistant, not a replacement. • AI isn’t creative, but it can help you be. • Give AI your least favorites things to do. • AI can’t create finished products, but it’s great at prototypes. Animated by the wit and brilliant explanatory power that have earned Mims’s Wall Street Journal columns a devoted following, How to AI will prepare readers to become a part of the AI revolution—and, most important, arm them with the tools to make it work for them.

Cut Through the Hype. Master the Basics. Transform Your Work

How to AI

Revolution of AI and Machine Learning for Design and Automation

Masterarbeit aus dem Jahr 2021 im Fachbereich BWL - Sonstiges, Note: 1.0, Fachhochschule Koblenz - Standort RheinAhrCampus Remagen, Sprache: Deutsch, Abstract: Die Bekämpfung der Geldwäsche stellt die Kreditinstitute vor große Herausforderungen. Kreditinstitute können derzeit neben den "klassischen" Verfahren wie der Implementierung von "unflexiblen" IT-basierten AML-Lösungen (Anti-Money-Laundering) nur mit immer größeren Personalressourcen auf die Herausforderungen reagieren. Das Forschungsziel der vorliegenden Arbeit ist es, zu untersuchen, ob der Einsatz der künstlichen Intelligenz (Artificial Intelligence) bei der Bekämpfung der Geldwäsche wirkungsvoll unterstützen kann. Dazu wird die folgende Forschungsfrage gestellt: "Inwieweit können Kreditinstitute durch den Einsatz von KI-Technologien auffällige Muster und Hinweise auf Geldwäsche effizienter als mit bestehenden IT-Systemen entdecken?" Um die Forschungsfrage zu beantworten, wird eine qualitative Forschungsmethode gewählt. Dabei wird die vorhandene akademische Literatur zum Einsatz künstlicher Intelligenz bei der Bekämpfung der Geldwäsche untersucht und werden qualitative Interviews mit Branchenexperten als Instrument zur Datenerhebung mit Branchenexperten durchgeführt. Als Resultat zeigt sich, dass die Lücke zwischen den aktuellen AML-Methoden und der hochmodernen künstlichen Intelligenz (KI) geschlossen werden kann. Durch die neuen Trends und Richtungen in der KI-Technologie ist es möglich, skalierbare AML-Lösungen zu entwickeln.

Qualitative Analyse und Betrachtung

Einsatz von Artificial Intelligence zur Bekämpfung der Geldwäsche in Kreditinstituten

How Privacy Panic Led Tech Regulation Astray—and How to Fix It

Fear of Data

Work Reimagined

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