Here's how these three concepts relate to each other:

## **Artificial Intelligence (AI)**
- **Broadest term** - encompasses any computer system that can perform tasks typically requiring human intelligence
- Includes everything from simple rule-based systems to complex neural networks
- Goal: Create machines that can think and learn like humans

## **Machine Learning (ML)**
- **Subset of AI** - specific approach where computers learn patterns from data without being explicitly programmed for each task
- Uses algorithms to identify patterns and make predictions or decisions
- Examples: recommendation systems, spam filters, image recognition

## **Deep Learning (DL)**
- **Subset of Machine Learning** - uses neural networks with many layers (hence "deep")
- Particularly good at handling unstructured data like images, audio, and text
- Examples: self-driving cars, voice assistants, medical imaging analysis

## **The Hierarchy**
```
Artificial Intelligence
├── Machine Learning
│   ├── Deep Learning
│   └── Other ML methods (decision trees, SVM, etc.)
└── Other AI approaches (rule-based systems, expert systems, etc.)
```

Think of it this way: All deep learning is machine learning, all machine learning is AI, but not all AI is machine learning. Deep learning is a specialized technique within the broader field of machine learning that has become very popular recently due to its remarkable performance on complex problems.