# Artificial Intelligence (AI)

URL: https://softwaredictionary.org/terms/artificial-intelligence
Category: AI & Machine Learning
Last updated: 2026-10-03
In Turkish: Yapay Zekâ
Pronunciation: ar-tuh-FISH-ul in-TEL-uh-junss

In short: Artificial intelligence (AI) is the field of computer science that builds systems able to do tasks that normally require human intelligence.

## What is artificial intelligence?

The name was coined in 1955 by John McCarthy for a summer workshop at Dartmouth College, held in 1956, which is often called the birth of the field. Early AI was mostly symbolic: people wrote rules and logic by hand, as in chess programs and expert systems that encoded what a specialist knew as long lists of if-then rules.

Modern AI is dominated by machine learning, where a program learns patterns from examples instead of following hand-written rules. Deep learning, machine learning with large neural networks, made the big leaps of the last decade possible: speech recognition, image recognition, translation and, since the late 2010s, large language models that write text and code.

Almost every AI system in use today is narrow: it does one kind of task, such as recommending videos, spotting fraud or answering questions, even if it does that task very well. A system that could learn and reason across any task the way people do is called artificial general intelligence (AGI), and it remains a goal and a subject of debate rather than something that exists.

A common misconception is that AI and machine learning mean the same thing. AI is the broad goal of making machines act intelligently; machine learning is one way to get there, and today the most successful one. A route planner or a chess engine built from search algorithms is AI without any learning at all.

## Key takeaways

- AI builds systems that do tasks that normally need human intelligence.
- The term dates from 1955, for a workshop at Dartmouth held in 1956.
- Early AI used hand-written rules; modern AI mostly learns from data.
- Machine learning and deep learning are the main ways AI is built today.
- Today's AI is narrow; general intelligence (AGI) does not exist yet.

## Example: Rules versus learning: two ways to flag spam

```python
# Symbolic AI: a person writes the rule
def is_spam_rule(text):
    return "free money" in text.lower()

# Machine learning: the rule is learned from labeled examples
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

texts = ["Free money now", "Lunch at noon?", "Win free money", "Meeting moved"]
labels = [1, 0, 1, 0]  # 1 = spam

vectorizer = CountVectorizer()
model = MultinomialNB().fit(vectorizer.fit_transform(texts), labels)
print(model.predict(vectorizer.transform(["Claim your free prize"])))
```

## Frequently asked questions

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

AI is the overall goal of making machines behave intelligently. Machine learning is a subset of AI in which systems learn from data instead of following rules written by hand.

**Is ChatGPT artificial intelligence?**

Yes. ChatGPT is an AI chatbot built on a large language model, a kind of deep learning model trained on huge amounts of text. It is still narrow AI, not general intelligence.

**What are the main types of AI?**

A common split is by capability: narrow AI, which handles specific tasks and is what exists today, and general AI, a hypothetical system as capable as a person across tasks. By technique, AI ranges from rule-based systems to machine learning and deep learning.

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