AI vs ML vs Deep Learning
The nested hierarchy — AI encompasses ML which encompasses Deep Learning
AI, Machine Learning, and Deep Learning are often used interchangeably but they represent a nested hierarchy. Artificial Intelligence is the overarching goal; Machine Learning is one way to achieve it; Deep Learning is a powerful subfield of ML using neural networks.
Key Points
- AI (outermost): any technique that enables machines to mimic human intelligence
- ML (subset of AI): systems that learn from data without being explicitly programmed
- Deep Learning (subset of ML): multi-layer neural networks that automatically extract features
- Generative AI (subset of Deep Learning): models that create new content (text, images, code)
- ML requires hand-crafted features; Deep Learning learns features automatically from raw data
- Deep Learning excels when you have large datasets and sufficient compute (GPUs/TPUs)
- Not all AI uses ML — rule-based expert systems are AI but not ML
The AI hierarchy: Generative AI is a subset of Deep Learning, which is a subset of Machine Learning, which is a subset of AI
Real-World Example
A traditional rule-based chatbot is AI but not ML. A spam filter trained on email data is ML. GPT-4 generating text is both Deep Learning and Generative AI — sitting at the innermost circle of this hierarchy.