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Huyen C: AI Engineering

AI Engineering

Building Applications with Foundation Models

Towards AI-based Strategy Planning

Strategic Analytics

This book introduces the subject of Artificial Intelligence (AI), its structures, its uses, its application to the Artificial Neural Network, its technology applications, and the advantages and disadvantages of its use, including its potential weaknesses and drawbacks. Artificial Intelligence, 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 facilities 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: For example, a lot of forefronts and cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough, it is no longer labeled as AI anymore.The growing use of artificial intelligence 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, impacting job markets, healthcare, government, industry, education, propaganda, and disinformation and deception, whether deliberate of 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

This book presents a cohesive collection of cutting-edge research focused on advanced decision-support methodologies for complex and ambiguous operational contexts. This volume systematically assembles ten pivotal studies employing uncertain decision-making, multi-criteria decision analysis (MCDA), and tailored optimization models to address substantive challenges in supply chain management, sustainable operations, and strategic planning.The contributions span a rigorous methodological spectrum, including fuzzy PDCA cycles for green performance evaluation, hybrid MCDM frameworks for lean tool selection, and robust optimization for resilient network design. Applications are drawn from critical domains such as healthcare logistics during epidemic response, sustainable supply chain configuration, and large-scale project trade-off analysis under resource constraints. Each chapter demonstrates the translation of theoretical computational models into applied solutions for decision-making under volatility, incomplete information, and multi-objective pressures.This volume will serve as a significant reference for academics and graduate researchers in management science, operations research, and industrial engineering. It provides a structured examination of how contemporary soft computing and operational research techniques can be leveraged to enhance analytical rigor and strategic outcomes in the face of pervasive environmental uncertainty.The chapters in this book were published in International Journal of Management Science and Engineering Management.

Innovation Decision-Making Under Uncertain Environment

This book examines the impact of artificial intelligence (AI) on globalization, in relation to economics, society, and policy. It explores the broad and diverse ways that AI is changing the global ecosystem, including firm behaviour changes, labour market disruption, reduced production costs, and climate change concerns, to highlight both the positive and negative impacts of AI. By anticipating the potential impacts of AI, a framework is presented to combat the negative aspects and maximise the benefits.This book offers a balanced and considered perspective on AI and the role it will play in the global economy. It will be of interest to students, researchers, and policymakers working in international economics, the political economy, and economic policy, as well as general readers interested in AI.Nicola Acocella
 is Emeritus Professor of Economics at Sapienza University of Rome.

This book examines the impact of artificial intelligence (AI) on globalization, in relation to economics, society, and policy. It explores the broad and diverse ways that AI is changing the global ecosystem, including firm behaviour changes, labour market disruption, reduced production costs, and climate change concerns, to highlight both the positive and negative impacts of AI. By anticipating the potential impacts of AI, a framework is presented to combat the negative aspects and maximise the benefits.This book offers a balanced and considered perspective on AI and the role it will play in the global economy. It will be of interest to students, researchers, and policymakers working in international economics, the political economy, and economic policy, as well as general readers interested in AI.

Utilizing AI for the Benefit of Humanity

The Economics of Globalization and Artificial Intelligence

Harness the power of taxonomies for better human capital management and employee wellbeing In Job Architecture, economist and entrepreneur Ben Zweig offers a revolutionary approach to transforming human capital management through the power of taxonomies. The book follows the experience and ideas of key individuals-from the founders of Wall Street, to the original management consultant, to a young data scientist just out of grad school looking to make sense of the modern workforce-in order to illustrate why our current human capital infrastructure is not serving employees well and what we can do to change that. By categorizing and organizing workforce data, Zweig provides a practical roadmap for creating a more efficient and data-driven labor market. This book includes key insights on how to: <ul> <li>Use AI and similar large language model technologies to support businesses with appropriate categorization and regimentation of data</li> <li>Know whether or not a taxonomy can be useful and functional for an organization in their ability to be flexible, auditable, and adaptable</li> <li>Build a taxonomy that meets the needs of a workforce or organization through clustering, labeling, and production</li> </ul> Combining storytelling with real-world examples, theoretical analysis, and a practical framework, Job Architecture is an essential guide for companies to manage a competitive, modern workforce that improves the working experience for all employees.

Building a Language for Workforce Intelligence

Job Architecture

In a world of alt facts, imperfect algorithms and information overload, the ability to think clearly has never been more crucial. But it's not easy. Critical thinking asks us to slow down and reflect; to question assumptions; to be aware of blind spots. It takes time - which can be tough to find when we're distracted or under pressure. How to Think Clearly is a practical primer for developing the habits that underpin critical thinking. In life or at work, whether weighing up options or choosing what to believe, it offers a toolkit to turn these skills into daily practice. From powerful pauses and killer questions to being data smart, it will help you think twice - and think better.

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

How to Think Clearly

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Whilst there are many books which deal with decision making in a general, process or mathematical sense, there are very few books which address the nature of poor decision making and particularly how to avoid decisions with unintended consequences and surprises. This book is unique in that it has taken a military intelligence concept from the CIA and applied it to the corporate world. As such it offers a fresh perspective on the decision making process. This book will provide the reader with a clear understanding of the nature of blowback, how to assess it and how to avoid it.

Whilst there are many books which deal with decision making in a general, process or mathematical sense, there are very few books which address the nature of poor decision making and particularly how to avoid decisions with unintended consequences and surprises.   There are plenty of sources of influence on any significant decisions within business; the politics or culture of the organization (or of the environment in which it operates); the passage of time following the decision; the availability of information; the behaviour of customers, stakeholders or activitists and the impact of fear, greed or panic on their view of the situation.  Andrew Holmes provides readers with the means to understand the nature of this influence; how to model the potential for blowback on any decisions that you may be planning, along with a series of tools and techniques for mitigating or avoiding blowback.  Every week all sorts of organizations find their reputation, their share price or even their very business, under pressure because of the consequences or blowback from poor or hasty decisions. Andrew Holmes book provides you with an understanding of the phenomenon as well as a very practical guide to more resilient decision making.  This book will provide the reader with a clear understanding of the nature of blowback, how to assess it and how to avoid it.

How to Avoid Unintended and Undesired Consequences in Decision-Making

Blowback in Business

Synthesis Lectures on Digital Circuits & Systems

This book is about the Arduino microcontroller and the Arduino concept. The visionary Arduino represented a new innovation in microcontroller hardware in 2005, the concept of open source hardware, making a broad range of computing accessible for all.

This book, "Arduino V: AI and Machine Learning," second edition, is an accessible primer on Artificial Intelligence and Machine Learning for those without a deep AI and ML background. The author concentrates on Artificial Intelligence (AI) and Machine Learning (ML) applications for microcontroller–based systems. The intent is to introduce the concepts and allow readers to practice on low cost, accessible Arduino hardware and software. Readers should find this book a starting point, an introduction, to this fascinating field. A number of references are provided for further exploration.

Arduino V: Machine Learning, 2ed

Elements in Law, Economics and Politics

This Element provides an overview of FinTech branches and analyzes the associated institutional forces and economic incentives, offering new insights for optimal regulation. First, it establishes a fundamental tension between addressing existing financial inefficiencies and introducing new economic distortions. Second, it demonstrates that today's innovators have evolved from pursuing incremental change through conventional Fin-Tech applications to AI × crypto as the fastest-growing segment. The convergence of previously siloed areas is creating an open-source infrastructure that reduces entry costs and enables more radical innovation, further amplifying change. Yet this transformation introduces legal uncertainty and risks related to liability, cybercrime, taxation, and adjudication. Through case studies across domains, the Element shows that familiar economic tradeoffs persist, suggesting opportunities for boundary-spanning regulation. It offers regulatory solutions, including RegTech frameworks, compliance-incentivizing mechanisms, collaborative governance models, proactive enforcement of mischaracterizations, and alternative legal analogies for AI × crypto.

Past, Present, and Future

FinTech Regulation in the United States

The book presents real-life applications of Data Science and Big Data for Sustainable Development Goals. It includes a list of case studies from different regions across India. The book uses both structured and unstructured data like numeric data, textual data, video/image data, etc., across the various chapters for the analysis. It further delves into various data science techniques, starting from data collection on the ground in the unavailability of data to a dashboard, reporting, unsupervised methods like clustering, artificial intelligence, machine learning and deep learning, search or information retrieval, time series forecasting, optimisation, etc.¿ Showcase data science decision-making processes, driving innovation, and solving complex problems in real-case scenarios from across sectors like finance, healthcare, and e-commerce.¿ The SDGs provide a framework for societal development and well-being for all; the data science and big data interventions in this book are aligned towards mapping the various SDG goals.¿ Most of the data science use cases and initiatives projects covered in this book have been implemented by the central or state governments across different states of India.¿ Shows how data science intervention can transform the social sector, potentially driving positive change and addressing critical societal challenges.¿ Explained the fundamentals of data science theories, including concepts like classification, classification, regression, predictive analytics, optimisation, artificial intelligence, deep learning, time series forecasting, etc. for readers of different disciplines.It serves as a valuable reference for graduate students, researchers, and scholars seeking to deepen their knowledge and engage with real life applications of Data Science. This will also serve as a valuable resource for government officers and policy practitioners, providing a range of cases on the use of data-based methods for improving governance and policy making.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license funded by by the Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, India.

Indian Case Studies

Data Science for Sustainable Development Goals

An Executive Guide to Balancing AI Innovation and Safety

The AI Precipice

AI Engineering

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