Part I: Foundational and Generative AI
This section establishes the historical and technical bedrock of modern AI. It explores the evolution of AI, differentiates key terms, and highlights the hardware breakthroughs that fueled the current generative era.
A Historical Trajectory
Core Concepts
Artificial Intelligence
Any technique enabling computers to mimic human intelligence.
Machine Learning
AI subfield where systems learn from data, not explicit rules.
Deep Learning
ML subfield using multi-layered neural networks.
Generative AI
AI that *creates* new content (text, images).
The Generative Toolkit
Explore the core architectures powering generative AI. Each model has unique strengths and theoretical underpinnings. Click on a model to understand its core concept.
Part II & III: The Emergence of AI Agents
This section explores the shift from content generation to goal-oriented action. We define what an AI agent is, dissect its core components, and compare the key frameworks used to build them.
Simple Reflex
Acts on current percept only (if-then).
Model-Based
Maintains internal world state.
Goal-Based
Chooses actions to achieve goals.
Utility-Based
Maximizes "happiness" or utility.
Learning
Improves performance over time.
Agentic Architecture
Framework Comparison
Part IV: Applications, Ethics & Assessment
Explore how Agentic AI is applied in the real world, the critical ethical frontiers we must navigate, and how learning is assessed in the age of AI.