What is difference between ML and AI?

The difference between Artificial Intelligence (AI) and Machine Learning (ML) lies in their scope and functionality:
1. Artificial Intelligence (AI):
Definition: AI is a broad field of computer science that aims to create systems capable of performing tasks that normally require human intelligence.
Examples of AI Tasks:
Understanding language (like ChatGPT)
Recognizing images or speech
Planning and decision-making
Playing games like chess or Go
Goal: To build smart systems that can simulate human thinking and behavior.
2. Machine Learning (ML):
Definition: ML is a subset of AI. It focuses on developing algorithms that allow computers to learn from data and improve over time without being explicitly programmed.
How it Works: ML models find patterns in data and use those patterns to make predictions or decisions.
Examples of ML Use:
Email spam detection
Movie recommendations (like on Netflix)
Fraud detection in banking
Key Differences
Aspect AI ML
Scope Broad – includes reasoning, planning, etc. Narrower – focuses on learning from data
Approach Mimics human intelligence Learns from patterns in data
Examples Chatbots, self-driving cars Face recognition, recommendation systems
Relation to Each Other AI is the broader field ML is a subfield within AI
Summary:
AI is the goal: make machines intelligent.
ML is one way to achieve that goal, by letting machines learn from data.


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