| Description |
1 online resource (239 p.) |
| Note |
Description based upon print version of record. |
| Contents |
Cover -- Copyright -- Endorsements -- Contributor -- Table of Contents -- Preface -- Chapter 01: Why AI and Data Literacy? -- History of literacy -- Understanding AI -- Dangers and risks of AI -- AI Bill of Rights -- Data + AI: Weapons of math destruction -- Importance of AI and data literacy -- What is ethics? -- Addressing AI and data literacy challenges -- The AI and Data Literacy Framework -- Assessing your AI and data literacy -- Summary -- References -- Chapter 02: Data and Privacy Awareness -- Understanding data -- What is big data? -- What is synthetic data? |
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How is data collected/captured? -- Sensors, surveillance, and IoT -- Third-party data aggregators -- Understanding data privacy efforts and their efficacy -- Data privacy ramifications -- Data privacy statements -- How organizations monetize your personal data -- Summary -- References -- Chapter 03: Analytics Literacy -- BI vs. data science -- What is BI? -- What is data science? -- The differences between BI and data science -- Understanding the data science development process -- The critical role of design thinking -- Navigating the analytics maturity index -- Level 1: Operational reporting |
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Level 2: Insights and foresight -- Statistical analytics -- Exploratory analytics -- Diagnostic analytics -- Machine learning -- Level 3: Augmented human intelligence -- Neural networks -- Regression analysis -- Recommendation engines -- Federated learning -- Level 4: Autonomous analytics -- Reinforcement learning -- Generative AI -- Artificial General Intelligence -- Summary -- Chapter 04: Understanding How AI Works -- How does AI work? -- What constitutes a healthy AI utility function? -- Defining "value" -- Understanding leading vs. lagging indicators |
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How to optimize AI-based learning systems -- Understand user intent -- Build diversity -- Summary -- Chapter 05: Making Informed Decisions -- Factors influencing human decisions -- Human decision-making traps -- Trap #1: Over-confidence bias -- Trap #2: Anchoring bias -- Trap #3: Risk aversion -- Trap #4: Sunk costs -- Trap #5: Framing -- Trap #6: Bandwagon effect -- Trap #7: Confirmation bias -- Trap #8: Decisions based on averages -- Avoiding decision-making traps -- Exploring decision-making strategies -- Informed decision-making framework -- Decision matrix -- Pugh decision matrix |
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OODA loop -- Critical thinking in decision making -- Summary -- References -- Chapter 06: Prediction and Statistics -- What is prediction? -- Understanding probabilities and statistics -- Probabilities are still just probabilities, not facts -- Introducing the confusion matrix -- False positives, false negatives, and AI model confirmation bias -- Real-world use case: AI in the world of job applicants -- Summary -- References -- Chapter 07: Value Engineering Competency -- What is economics? What is value? -- What is nanoeconomics? -- Data and AI Analytics Business Model Maturity Index -- Stages |
| Note |
Inflection points |
| Summary |
Learn the key skills and capabilities that empower Citizens of Data Science to not only survive but thrive in an AI-dominated world. Purchase of the print or Kindle book includes a free PDF eBook Key Features Prepare for a future dominated by AI and big data Enhance your AI and data literacy with real-world examples Learn how to leverage AI and data to address current and future challenges Book Description 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. What you will learn Get to know the fundamentals of data literacy, privacy, and analytics Find out what makes AI tick and the role of the AI utility function Make informed decisions using prominent decision-making frameworks Understand relevant statistics and probability concepts Create new sources of value by leveraging and applying AI and data Apply ethical parameters to AI development with real-world examples Find out how to get the most out of ChatGPT and its peers Who this book is for This book is designed to benefit everyone from students to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their AI and Data literacy. |
| Subject |
Artificial intelligence.
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Big data.
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Information literacy.
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Intelligence artificielle. |
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Données volumineuses. |
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Culture de l'information. |
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artificial intelligence. |
| Other Form: |
Print version: Schmarzo, Bill AI and Data Literacy Birmingham : Packt Publishing, Limited,c2023 |
| ISBN |
1835087949 |
|
9781835087947 (electronic bk.) |
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