Generative AI

Generative AI is a branch of artificial intelligence that focuses on creating new, original content rather than simply analyzing or classifying existing data. By learning the underlying patterns and structures from vast datasets of text, images, sounds, or code, these systems can produce novel outputs—such as writing essays, composing music, designing images, or generating software—that mimic the characteristics of the data they were trained on. This creative capability distinguishes it from other forms of AI that are primarily designed to recognize, classify, or analyze existing information.

1. Introduction to Generative AI
   1. Defining Generative AI
      1. Core Definition and Scope
      2. Key Characteristics
      3. Probabilistic Nature of Generation
   2. Distinction from Discriminative AI
      1. Generative vs. Discriminative Tasks
      2. Model Objectives and Outputs
      3. Use Case Comparisons
   3. Historical Evolution
      1. Early Rule-Based Systems
         1. Expert Systems
         2. Symbolic AI Approaches
         3. Template-Based Generation
      2. Statistical Foundations
         1. Markov Chains
         2. Hidden Markov Models
         3. Bayesian Networks
         4. N-gram Models
      3. Neural Network Revolution
         1. Perceptron and Early Networks
         2. Backpropagation Breakthrough
         3. Deep Learning Emergence
         4. Modern Transformer Era
   4. Core Capabilities and Applications
      1. Content Creation
         1. Text Generation
         2. Image Synthesis
         3. Audio Generation
         4. Video Creation
         5. Code Generation
      2. Data Augmentation
         1. Synthetic Training Data
         2. Dataset Balancing
         3. Privacy-Preserving Data Generation
      3. Simulation and Modeling
         1. Scenario Generation
         2. Virtual Environment Creation
         3. Predictive Modeling