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Category: Emerging Concepts
Category: Emerging Concepts
Self-supervised learning enables AI to learn from unlabeled data by creating its own training signals from the data itself.
Instead of needing human-labeled examples, self-supervised systems create tasks from raw data. Language models predict missing words, vision models predict masked image patches, and audio models reconstruct corrupted sounds.
The AI learns useful representations without expensive human annotation.
Self-supervised learning unlocked the current AI revolution by making it possible to use the internet's vast unlabeled data. It's why we have GPT, BERT, and other breakthrough models.
This approach scales better than supervised learning because unlabeled data is abundant.
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