How does AI learn? (Machine Learning)
What “learning” means for a machine:
Data-driven AI adjusts internal parameters to predict outcomes.
Example: Google Maps learns traffic patterns to forecast congestion.
Data as the basis for learning:
Models are fed examples to recognize patterns. Example: Spotify analyzes playlists to recommend songs.
Training vs real-world use:
Supervised AI learns patterns before being used in real situations.
Example: Bank fraud detection models trained with transaction history.
Types of learning:
Supervised: receives correct answers to learn (e.g., classifying emails as “spam” or “not spam”).
Unsupervised: detects patterns without labels (e.g., clustering customers for marketing).
Reinforcement: learns through trial and error (e.g., AlphaGo learning Go strategies).
Errors and adjustments (feedback):
AI improves accuracy based on previous results. Example: Netflix adjusts recommendations after user ratings.
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