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Artificial IntelligencevsMachine Learning
What is the difference between AI and machine learning?
Updated 2 min read6 differences
In short
AI aims to make machines do tasks that need human intelligence; machine learning is one way to get there, learning from data instead of hand-written rules.
Artificial Intelligence
AI
Artificial intelligence (AI) is the field of computer science that builds systems able to do tasks that normally require human intelligence.
Read the page on Artificial IntelligenceMachine Learning
Machine learning is a branch of artificial intelligence in which computers learn patterns from data to make predictions instead of following hand-written rules.
Read the page on Machine LearningArtificial Intelligence and Machine Learning compared
| Aspect | Artificial Intelligence | Machine Learning |
|---|---|---|
| Scope | The whole field of intelligent machines | A subset of AI |
| Approach | Any technique: rules, search, learning | Learning patterns from data |
| Needs training data | Not always | Yes |
| Origin of the term | Dartmouth workshop, 1956 | Popularized by Arthur Samuel, 1959 |
| Examples | Chess engines, route planners, expert systems, chatbots | Spam filters, recommendations, fraud detection, LLMs |
| Includes | Machine learning and deep learning | Deep learning |
The difference, explained
AI is the umbrella. It covers any technique that lets a computer reason, plan, understand language, recognize images or make decisions, from rule-based expert systems and search algorithms that play chess to today's large language models. The field takes its name from a 1956 workshop at Dartmouth College.
Machine learning is a subset of AI that has become its dominant approach. Instead of programming the rules, you show the system many examples, and it learns a model that makes predictions on new data: spam or not spam, the price of a house, the next word in a sentence. Deep learning is in turn a subset of machine learning that uses large neural networks.
The relationship is easiest to see as nested circles: deep learning inside machine learning inside AI. A route planner using a search algorithm is AI without machine learning; a model that predicts customer churn from past data is machine learning; a chatbot built on a large language model is deep learning, and therefore also machine learning and AI.
A common misconception is that the terms are interchangeable marketing words. They describe different scopes, and the difference matters in practice: machine learning needs data and training, can be evaluated with metrics on held-out data, and can be wrong in ways rule-based systems aren't, which affects how products are built and tested.
Which one should you use?
Choose Artificial Intelligence when…
- You talk about the overall goal or field.
- The solution may use rules, search or optimization, not just learning.
- You describe systems that combine several techniques.
Choose Machine Learning when…
- The system learns from examples or historical data.
- You need predictions, classifications or recommendations.
- You can collect labeled data and measure accuracy.
Readers ask
Is all AI machine learning?
No. Rule-based systems, planning and search algorithms are AI without machine learning. Machine learning is simply the most successful approach today.
What is the difference between machine learning and deep learning?
Deep learning is a kind of machine learning that uses neural networks with many layers. It powers image recognition, speech and large language models, but needs a lot of data and computing power.
Is ChatGPT AI or machine learning?
Both. It is a large language model, a deep learning system, which makes it machine learning and therefore AI.