AI Development Lesson 1: What is AI & ML?

Artificial Intelligence is making computers perform tasks that normally require human intelligence. Machine Learning is a subset where computers learn from data without being explicitly programmed.

AI Hierarchy

// AI (Artificial Intelligence)
// └── ML (Machine Learning) — learns from data
//     └── Deep Learning — neural networks with many layers
//         └── LLMs (Large Language Models) — GPT, Claude, Gemini

// Types of ML:
// Supervised Learning: learn from labeled data (spam detection, image classification)
// Unsupervised Learning: find patterns without labels (clustering, anomaly detection)
// Reinforcement Learning: learn from rewards/penalties (game AI, robotics)
// Generative AI: create new content (images, text, code, music)

ML Pipeline

// 1. Problem Definition
//    What are we predicting? What data do we have?

// 2. Data Collection & Cleaning
//    Garbage in, garbage out — data quality matters most

// 3. Feature Engineering
//    What inputs does the model need?

// 4. Model Selection
//    Linear regression? Decision tree? Neural network?

// 5. Training
//    Model learns from training data

// 6. Evaluation
//    How accurate is it on unseen data?

// 7. Deployment
//    Make it available as an API or product

// 8. Monitoring
//    Performance degrades over time — retrain regularly

Where AI is Now

2024 AI landscape: GPT-4/Claude 3.5 do complex reasoning. Midjourney/DALL-E generate images. Sora creates video. GitHub Copilot writes code. AI is a tool, not a replacement — developers who use AI tools are 55% more productive than those who don’t (McKinsey 2024).