Artificial Intelligence
AI vs Machine Learning
People use "AI" and "machine learning" interchangeably, but one contains the other. Getting the relationship straight makes a lot of the hype easier to read.
| Aspect | Artificial intelligence | Machine learning |
|---|---|---|
| What it is | The whole field of building intelligent systems | A branch of AI that learns from data |
| Scope | Broad — includes ML and rule-based approaches | Narrower — one approach within AI |
| How it works | Any method that performs intelligent-seeming tasks | Finds patterns in data instead of following hand-written rules |
| Relationship | The umbrella | A subset under it (with deep learning inside ML) |
| Example | A chess engine that searches possible moves | A spam filter that learns from labeled emails |
When to use artificial intelligence
"AI" is the right word for the overall field or goal — systems that perform tasks we associate with intelligence, by whatever method.
When to use machine learning
"Machine learning" is the right word when the system specifically learns from data rather than following explicit rules.
Frequently asked questions
- Is all AI machine learning?
- No. Machine learning is one branch of AI. AI also includes approaches that learn nothing at all, such as a chess engine that searches moves by rules. All machine learning is AI, but not all AI is machine learning.
- Where does deep learning fit?
- Deep learning is a subset of machine learning that uses many-layered neural networks. So the nesting is: AI contains machine learning, which contains deep learning.
- Why does the distinction matter?
- Because "AI" claims are often really about machine learning — or even just one narrow model. Knowing the hierarchy helps you ask what a system actually does rather than accepting the buzzword.