Artificial Intelligence
AI
- 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
# 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"])))Readers ask
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.
Often compared
See also
- Machine LearningAI & Machine Learning, p. 27Machine learning is a branch of artificial intelligence in which computers learn patterns from data to make predictions instead of following hand-written rules.
- Deep LearningAI & Machine Learning, p. 14Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns from raw data such as images and text.
- Neural NetworkAI & Machine Learning, p. 33A neural network is a machine learning model made of layers of connected artificial neurons that learn patterns from data by adjusting numeric weights.
- Generative AIAI & Machine Learning, p. 20Generative AI is artificial intelligence that creates new content, such as text, images, code, or audio, based on patterns learned from existing data.
- LLMAI & Machine Learning, p. 25An LLM is a machine learning model trained on huge amounts of text that generates language by repeatedly predicting the next most likely piece of text.
- AGIAI & Machine Learning, p. 1AGI (artificial general intelligence) is a hypothetical AI system that could learn and do any intellectual task a person can, not just a narrow set of tasks.
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