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Scott L: Business Machine Learning

Business Machine Learning

AN INSTANT NATIONAL BESTSELLER “Evenhanded and insightful.”&nbsp; —Publisher’s Weekly (starred review) “An accessible, often fascinating primer.... Essential, illuminating reading.” —Kirkus From the author of the New York Times bestseller The Digital Doctor comes an engaging, clear-eyed, and ultimately hopeful examination of healthcare’s efforts to embrace generative artificial intelligence. In A Giant Leap, physician and thought leader Robert Wachter navigates between hype and skepticism to make a compelling case for AI’s power to transform healthcare. He argues that, in a system buckling under the weight of bureaucratic pressures, soaring costs, and clinician burnout, AI doesn’t need to be perfect—it only needs to be better. Drawing on extensive research and more than 100 interviews with pioneers across medicine, technology, policy, and business, Wachter shows how AI is already entering hospitals and clinics to draft notes, field patient questions, recommend treatments, interpret images, and guide surgeries. He unflinchingly confronts risks like hallucinations, biases, and misinformation, while revealing how AI can now match, and sometimes surpass, physicians in areas ranging from diagnosis to empathy. But this isn’t simply a technology story. It’s about the human choices that will determine whether AI becomes healthcare’s salvation or another source of harm and frustration.&nbsp; Blending clinical insight, vivid storytelling, and journalistic precision, A Giant Leap offers an indispensable roadmap for healthcare leaders, clinicians, and patients. It&nbsp;is a vibrant and timely account of how AI is changing what it means to care—and be cared for—in this age of astonishing technology.

How AI Is Transforming Healthcare and What That Means for Our Future

A Giant Leap

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

'One of our finest thinkers on technology' - Jamie Bartlett, author of How to Talk to AI Discover the key to clear thinking in the age of AI In a world of alt facts, imperfect algorithms and information overload, thinking clearly has never been more crucial. But it's not easy. It asks us to slow down and reflect; to question assumptions; to be aware of blind spots. It takes practice - and the will to resist a whole host of distractions. How to Think Clearly is a practical primer that will help you: - develop the core habits that underpin critical thinking - master techniques like powerful pauses and killer questions - be data smart and tech savvy - turn these skills into daily practice, making you think twice - and think better

Cut through noise, see the big picture and make better decisions

How to Think Clearly

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.

A Tidy Approach

Business Analytics with R

Wiley Finance

The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance. Inside, you'll find over ten chapters which apply deep learning to multiple use cases across quantitative finance. You'll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text. Readers will be able to work through these examples directly. This book is a complete resource on how deep learning is used in quantitative finance applications. It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics. You'll also learn about the most important software frameworks. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts. <ul> <li>Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniques</li> <li>Offers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learning</li> <li>Demonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion website</li> <li>Introduces the most important software frameworks for applying deep learning within finance</li></ul> This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.

Deep Learning in Quantitative Finance

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

A NEW YORK TIMES &amp; SUNDAY TIMES BESTSELLER 'Extraordinary... beautifully written, clear-eyed and engaged in the deepest ethical questions of our day' Rory Stewart 'Mallaby has done full justice to his kaleidoscopically interesting subject ... expertly structured and vividly reported, the book presents the most insightful portrait of Hassabis to date' Financial Times A revelatory portrait of the visionary behind Google DeepMind, the race to control the future - and what it means to win Even in a tech world crowded with visionary leaders, Demis Hassabis is recognized as a special case. Born to working class, immigrant parents in North London, a chess prodigy by five and wizard coder in his teens, he turned down a seven-figure job offer from a video-game studio to study science at Cambridge. Long before the current obsession with AI, he founded the path-breaking company DeepMind in order to pursue a single, audacious goal: the dream of artificial superintelligence, which would solve humanity's hardest problems, change life and work as we know it, and perhaps even unlock the deepest mysteries of the Universe. For his scientific achievements, he won a Nobel Prize in 2024, and his company, now Google DeepMind, is considered the tech giant's engine room. For the past three years, Sebastian Mallaby has had unprecedented access to Hassabis and DeepMind, conducting hundreds of hours of interviews with him and his inner circle as well as detractors and rivals at other companies. The result is a revelation-packed portrait of a singular mind and a historic reckoning with the AI revolution, a shift potentially more significant than any since the dawn of complex thought 70,000 years ago. As Mallaby chronicles, DeepMind is locked in an arms race with Silicon Valley competitors to build artificial general intelligence, and thereby become the keeper of humanity's future. Yet this is not a Silicon Valley story. Hassabis has remained in Britain, and unlike his rivals, his aims are not wealth and power but scientific enlightenment. Like them, however, he is haunted by the memory of Robert Oppenheimer, the creator of the atom bomb. He aims to control the technology, but the technology may ultimately control him - and humanity writ large.

Demis Hassabis, DeepMind and the Quest for Superintelligence

The Infinity Machine

HBR's 10 Must Reads

How to stay ahead in a world transformed by AI.If you read nothing else on artificial intelligence, read this book. We've chosen a new selection of current and classic Harvard Business Review articles that will help you make sense of the evolving generative AI landscape, manage regulation and risk, and develop a strategy that keeps pace with technological change.This book will inspire you to: <ul><li>Help your people and technology work better together</li><li>Redesign roles, workflows, and capabilities</li><li>Keep your AI projects on track</li><li>Address gen AI's quality control and waste problems</li><li>Harness the potential of agentic AI</li><li>Embed AI in the core of your business</li></ul>This collection of articles includes "For Success with AI, Bring Everyone on Board," by David De Cremer; "Bring Human Values to AI," by Jacob Abernethy, Francois Candelon, Theodoros Evgeniou, Abhishek Gupta, and Yves Lostanlen; "From Prediction to Transformation," by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; "How AI Can Help Managers Think Through Problems," by Elisa Farri and Gabriele Rosani; "The Gen AI Playbook for Organizations," by Bharat N. Anand and Andy Wu; "Agentic AI Is Already Changing the Workforce," by Jen Stave, Ryan Kurt, and John Winsor; "Keep Your AI Projects on Track," by Iavor Bojinov; "How to Marry Process Management and AI," by Thomas H. Davenport and Thomas C. Redman; "The Hidden Penalty of Using AI at Work," by Oguz A. Acar, Phyliss Jia Gai, Yanping Tu, and Jiayi Hou; "Heavy Machinery Meets AI," by Vijay Govindarajan and Venkat Venkatraman; "AI Is Changing the Structure of Consulting Firms," by David S. Duncan, Tyler Anderson, and Jeffrey Saviano; "Stop Running So Many AI Pilots," by Goutam Challagalla, Mahwesh Khan, and Fabrice Beaulieu; "Addressing Gen AI's Quality-Control Problem," by Stefan Thomke, Philipp Eisenhauer, and Puneet Sahni; "AI's Growing Waste Problem--and How to Solve It," by Samsurin Welch, Khaled Soufani, and Eva Morales; and "Don't Let an AI Failure Harm Your Brand," by Julian De Freitas.HBR's 10 Must Reads are definitive collections of classic ideas, practical advice, and essential thinking from the pages of Harvard Business Review. Exploring topics like disruptive innovation, emotional intelligence, and new technology in our ever-evolving world, these books empower any leader to make bold decisions and inspire others.This Updated and Expanded edition features new, breakthrough articles, additional short-form pieces, and a detailed discussion guide to give you and your team the tools you need for sustained success.

HBR's 10 Must Reads on Artificial Intelligence

What AI Can’t Do Without You

The Human Advantage

'Engaging, insightful, panoramic' Mustafa Suleyman, CEO of Microsoft AI &amp; Cofounder of DeepMind 'An eye-opening exploration of a revolution unfolding before our eyes' New York Journal of Books Stunning advances in digital technology have introduced a new wave of human-like AI systems. Chatbots like ChatGPT, Claude, and Gemini are already reshaping economies, challenge democracies, and reshaping society in unpredictable ways. And soon, these AI systems could make autonomous decisions on their users' behalf, transforming everything we do. Understanding how they work is crucial. Can AI systems think, know, and understand?Could they manipulate or deceive you, and if so, what might they make you do? Whose interests do they represent? When will they be able to move beyond words and take action in the real world? Neuroscientist and AI researcher Christopher Summerfield explores these questions, charting AI's evolution from early ideas in the seventeenth century to today's deep neural networks. His book is the most accessible, up-to-date, and authoritative exploration of this radical technology. With an understanding of AI's inner workings, we can address the existential question of our age: can we look forward to a technological utopia, or are we writing ourselves out of history? 'As a leading authority...Summerfield is perfectly situated to explore the meaning and implications of these machines that are so uncannily like - and unlike - ourselves' Brian Christian, co-author of Algorithms to Live by 'You might choose to be alarmed, excited, or indifferent to LLMs, but you should read Chris's book before you decide' Mike Woolridge, author of The Road to Conscious Machines

How AI Learned to Talk and What It Means

These Strange New Minds

Berichte aus der Elektrotechnik

Methods of Machine Learning for Analysis and Decoupling of Power Delivery Networks on Printed Circuit Boards

Technology in Supply Chain Management and Logistics: Current Practice and Future Applications, Second Edition analyzes the implications of these technologies in a variety of supply chain settings, including block chain, Internet of Things (IoT), inventory optimization, and medical supply chain. This book outlines how technologies are being utilized for product planning, materials management and inventory, transportation and distribution, workflow, maintenance, the environment, and in health and safety. Readers will gain a better understanding of the implications of these technologies with respect to value creation, operational effectiveness, investment level, technical migration and general industry acceptance. The book identifies emerging supply chain technologies, and trends in technology acceptance and utilization levels across various industry sectors. It includes contributions from experts in their fields to illustrate how technology can be applied in a variety of areas including manufacturing, equipment distribution, retail, aerospace repair and maintenance, and medical supply chain. Also included are chapters on cutting edge exponential technologies, principals for implementing technological change in supply chain management, and future estimates of technology in supply chain. This second edition updates this constantly changing area.

Current Practice and Future Applications

Technology in Supply Chain Management and Logistics

Business Machine Learning

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