Book Image

AI & Data Literacy

By : Bill Schmarzo
Book Image

AI & Data Literacy

By: Bill Schmarzo

Overview of this book

AI is undoubtedly a game-changing tool with immense potential to improve human life. This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you’ll need to exploit to thrive amid the current and future developments in the AI landscape. We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions. Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed. Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and how best to leverage it and thrive alongside it.
Table of Contents (14 chapters)
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What this book covers

Chapter 1, Why AI and Data Literacy?, sets the groundwork for understanding why AI and data literacy is a conversation that must include everyone. The chapter highlights the rapid growth of AI in our everyday lives that impact society. The chapter also introduces the AI and Data Literacy Educational Framework that we will use throughout the book to guide our AI and data literacy education.

Chapter 2, Data and Privacy Awareness, ensures that everyone has a shared understanding of what we mean by the term big data and why it’s more valuable (and dangerous) than regular data. We also outline new technology developments with the Internet of Things (IoT) and how your data is captured and used in real time to monitor and influence your decisions. Discussing some regulatory efforts to protect your data and preserve your privacy, we will also learn how organizations monetize personal data for their benefit.

Chapter 3, Analytics Literacy, is one of the more technical chapters in the book. But everyone must understand the different levels of analytics and how they can be used to uncover market, society, environmental and economic insights that can lead to better, more informed decisions. If data is the new oil, then analytics is the exploration, mining, extraction, and production tools we use to convert raw oil into products of value.

Chapter 4, Understanding How AI Works, like the title suggests, dives deep into AI and how it works. We will discuss the importance of ascertaining or determining user intent to frame your AI model development and provide a conceptual understanding of the AI utility function – the weighted portfolio of variables and metrics that the AI models will use to guide its relentless optimization efforts.

Chapter 5, Making Informed Decisions, explores the decision-making traps we fall into that lead to suboptimal, bad, and even dangerous choices. As a solution, we will introduce decision-making strategies, like the decision matrix, OODA, and so on, that everyone can and should use to leverage AI and data to make more informed decisions.

Chapter 6, Prediction and Statistics, provides a short primer on statistics, probabilities, predictions, and confidence levels. We will discuss how we can use statistics to help us improve the odds of making more effective and safer decisions in a world of constant economic, environmental, political, societal, and healthcare disruption.

Chapter 7, Value Engineering Competency, will explore how organizations of all sizes can leverage AI and data to engineer or create “value.” We will present a framework for understanding how organizations define value and then identify the KPIs and use to measure their value creation effectiveness. We will also discuss why “economies of learning” are more powerful than “economies of scale” in a digital-centric world.

Chapter 8, Ethics of AI Adoption, describes some leading-edge ideas on how organizations and society can leverage economic concepts to transparently instrument and measure ethics and ensure that AI-based machines are working for humans rather than the other way around.

Chapter 9, Cultural Empowerment, will delve into the power and importance of empowerment to ensure that everyone has a voice in deciding and defining how best to leverage AI and data from a personal perspective. We will discuss how we must become “more human” to thrive alongside AI.

Chapter 10, ChatGPT Changes Everything, will provide a short primer on Generative AI (GenAI) products such as OpenAI ChatGPT, Microsoft Bing, and Google Bard. We will discuss how GenAI products work and the underlying technologies that make GenAI so effective in replicating human intelligence. Finally, we’ll assess how one can apply the 6 components of the AI and Data Literacy Framework to use GenAI to deliver more relevant, meaningful, responsible, and ethical outcomes.