1. Definition
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Artificial Intelligence (AI) is the broader concept of creating machines or systems that can perform tasks that would typically require human intelligence. These tasks include problem-solving, reasoning, understanding language, recognizing patterns, and decision-making.
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Machine Learning (ML) is a subset of AI that focuses on the idea that machines can learn from data, improve their performance over time without being explicitly programmed, and make predictions or decisions based on that learning.
2. Scope
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AI is a wide field that encompasses not only machine learning but also other techniques like symbolic reasoning, knowledge representation, expert systems, robotics, and more.
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ML is specifically focused on the development of algorithms that allow a system to learn from and make predictions or decisions based on data. ML is one way of achieving AI, but it is not the only way.
3. Goal
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AI aims to create systems that can perform any cognitive function that a human can. This includes tasks like planning, learning, understanding natural language, speech recognition, and visual perception.
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ML aims to develop models and algorithms that enable a machine to learn from historical data and improve its performance on tasks like classification, regression, clustering, etc., without being directly programmed for every specific situation.
4. Techniques and Methods
- AI includes methods like:
- Rule-based systems
- Expert systems
- Search algorithms
- Natural language processing
- Robotics
- ML specifically uses:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Neural networks
- Deep learning (which is a subset of ML)
