# AI vs Machine Learning

URL: https://softwaredictionary.org/compare/ai-vs-machine-learning
Last updated: 2026-10-03

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.

## What is the difference between AI and machine learning?

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.

| 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 |

## 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.

## Frequently asked questions

**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.

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Software Dictionary: https://softwaredictionary.org/ · https://softwaredictionary.org/llms.txt