Short answer
Artificial intelligence is the overarching field concerned with building systems that perform tasks associated with intelligence, while machine learning is a collection of techniques within that field that allow computers to learn patterns and make decisions based on data. 1 2
Machine learning is a specialized part of artificial intelligence, focusing on data-driven learning methods.
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At a glance
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| Question | Artificial intelligence | Machine learning |
|---|---|---|
| Scope | Broad field of intelligent systems | Specific learning algorithms |
| Approach | May use logic, rules, or learning | Relies on statistical data analysis |
| Programming | Can include explicitly programmed logic | Learns from data, less explicit programming |
What each thing is
Artificial intelligence. Artificial intelligence refers to the broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. 1
Machine learning. Machine learning is a subset of artificial intelligence involving methods that enable computers to learn from data and improve their performance without being explicitly programmed for each task. 2
Key differences
A defining axis is scope: artificial intelligence covers many approaches to tasks associated with intelligence, including rule-based and logic-driven systems, while machine learning specifically refers to techniques that adapt and improve through data exposure. 1 2
How to tell them apart
To tell them apart, look for systems that improve with experience—these use machine learning. If the system follows fixed rules or logic without adapting from data, it may be artificial intelligence but not machine learning. Some systems combine both approaches, making the boundary less clear. 1 2
Where they overlap
Many modern intelligent applications, such as image recognition or speech processing, use both artificial intelligence concepts and machine learning techniques. Machine learning provides the adaptive capability, while the overall system design falls under artificial intelligence. 1 2
Edge cases
Expert systems, which use predefined rules to mimic decision-making, are considered artificial intelligence but do not use machine learning. Conversely, some applications use machine learning for narrow tasks within broader artificial intelligence systems. 1 2
Why the distinction exists
Artificial intelligence emerged as a broad goal to replicate intelligent behavior in machines. Machine learning developed as a practical way to achieve some of these goals by enabling systems to learn from data rather than relying solely on hard-coded rules. 1 2
Common misconceptions
A common misconception is that all artificial intelligence systems are chatbots or that every AI uses machine learning. In reality, not all AI systems interact conversationally or learn from data; some use fixed logic or rules. 1 2
Examples
A spam filter that adapts to new types of unwanted messages using data-driven methods employs machine learning. In contrast, a chess program that relies on a fixed set of strategies and rules, without learning from games, is an example of artificial intelligence without machine learning. 1 2
Sources
Sources checked October 3, 2026.
- NIST CSRC — Artificial intelligence. AI definition.
- NIST CSRC — Machine learning. ML definition.