Machine Learning concept

AI versus machine learning

Reference

AI vs machine learning: artificial intelligence (AI) is the broad field of building systems that perform tasks such as reasoning or decision-making; machine learning (ML) is an approach within AI that learns patterns from data. Some AI systems use rules written by people instead. The difference is mainly in how a system is built, and a product label alone may not explain which approach it uses.

Reviewed 2026-10-09

The two ideas

Artificial intelligence is a broad field concerned with systems that perform tasks such as perception, planning, learning, or decision-making. Sources define its boundaries in different ways, so AI can refer to a field of study or to systems built using different techniques. NIST’s glossary lists several definitions.

Machine learning is one approach within AI: a system uses data to learn patterns that can help it make predictions or decisions about new inputs. In a rule-based system, people specify the rules directly. A system that learns from training examples illustrates machine learning; a system that follows rules specified in advance illustrates a rule-based approach. Both approaches can be used to build AI systems, but only the first learns patterns from data.

What differs

The main difference is how the system’s behavior is specified. With machine learning, a training process uses data to fit a model, which then applies learned patterns to new inputs. In a rule-based approach, people write the logic the system follows. Machine learning is therefore one family of AI methods, not another name for all of AI.

A complete system can combine learned models with rules written by people or other software components. The words “AI” and “machine learning” can describe different aspects of a system: AI names the broader field or system, while machine learning identifies an approach that learns from data. To understand a particular system, look for an explanation of its method: does it learn patterns from data, follow explicit rules, or combine approaches?

When each approach fits

A rule-based approach can fit a task when its decision rules are clear enough to write down. For example, a simple controller can follow conditions specified in advance to decide what action to take. This illustrates a specified rule; it does not establish how every controller or automated system works.

Machine learning can fit a task when examples are available and the goal is to learn patterns that support predictions or decisions on new inputs. For example, a learner can use training examples to predict a target for new examples. Which approach fits depends on the task and available data, and a system can combine both.

Common confusions

AI and machine learning are related but not interchangeable terms. Saying a system uses AI does not by itself tell you whether it learns from data or follows written rules. Saying it uses machine learning indicates that learning patterns from data is part of its method.

AI includes approaches that represent knowledge and use rules, as well as approaches that learn from data. A system can also combine them: for example, it may use a learned model and consult rules or a knowledge base. The practical question is what method the system uses, rather than whether its label includes the word “AI.”

Questions people ask

Is machine learning the same as AI?

No. Machine learning is one approach within AI. Some AI systems represent knowledge with rules instead of learning patterns from data.

What is the difference between AI and ML?

AI is the wider field. ML is an approach within it that learns patterns from data to make predictions about new examples.

Can AI work without machine learning?

Yes. Knowledge-based AI can represent facts and rules, then use search and reasoning to draw conclusions.

Can one system use both rules and machine learning?

Yes. A system can combine a learned model with rules or other knowledge supplied by people.

Learn more

Artificial Intelligence: Foundations of Computational Agents
Its overview places AI agents, explicit reasoning, and learning from data in the same framework.
Machine Learning Crash Course
Its modules introduce machine learning concepts, including datasets, generalization, and overfitting.

What changed

Sources

Part of Machine Learning: How models learn from data Fit, overfit, and generalize