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Chapters

This textbook is organized into 11 chapters covering 200 concepts on Digital Transformation 2.0 with Generative AI.

Chapter Overview

  1. Digital Transformation and AI Foundations - Core digital transformation concepts and introduction to AI, ML, and generative AI fundamentals.

1b. Tracking AI Progress and Trends - Understanding how to measure, benchmark, and project AI capabilities through data-driven analysis and interactive visualizations including the METR research, Moore's Law, and AI benchmarking.

  1. Large Language Model Architecture - How LLMs work including transformer architecture, attention mechanisms, training methods, and model parameters.

  2. AI Platform Landscape - Overview of major AI platforms: OpenAI, Anthropic, Google, and open-source models.

  3. Prompt Engineering - Zero-shot, few-shot, chain-of-thought prompting, and optimization techniques.

  4. Custom GPTs, Agents, and RAG Systems - Building custom GPTs, AI agents, and retrieval-augmented generation.

  5. LLM API Integration - REST APIs, authentication, parameters, and cost optimization.

  6. Multimodal AI - Text-to-image, vision capabilities, and multimodal applications.

  7. AI Governance, Ethics, and Responsible AI - GAICoE design, governance frameworks, bias mitigation, and safety.

  8. Future of Work and Workforce Transformation - AI-augmented workforce, skill transformation, and human-AI collaboration.

  9. Business Applications and AI Transformation - Use cases, industry applications, and capstone project integration.

How to Use This Textbook

Chapters are organized to respect concept dependencies - each chapter builds on concepts introduced in previous chapters. For the best learning experience:

  1. Complete chapters in order, as later concepts depend on earlier foundations
  2. Use the Learning Graph viewer to explore concept relationships
  3. Practice with MicroSims to reinforce understanding
  4. Complete chapter quizzes to assess your progress

Note: Each chapter includes a list of concepts covered. The course follows a 14-week schedule with this textbook supporting all lecture topics and lab activities.