Definition

Self-supervised learning is a training method where AI models generate their own labels from raw data. Instead of relying on human-annotated examples, the system learns patterns by predicting parts of the data from other parts—for example, predicting missing words in a sentence.

Example

“When GPT learns by filling in the next word of a sentence during training, that’s self-supervised learning.”

How It’s Used in AI

This method powers most LLMs, image models, and audio models. It enables massive-scale training without needing labeled datasets. Tools like BERT, GPT, and CLIP rely on this approach to understand language, visuals, or both.

Brief History

The concept grew popular in the 2010s as a solution to the limited supply of labeled data. It became the foundation for modern foundation models like GPT-3, BERT, and SimCLR.

Key Tools or Models

GPT, BERT, CLIP, SimCLR, DINO

Used in text, image, and multimodal models

Often paired with pretraining and transfer learning

Pro Tip

Self-supervised learning works best with huge datasets. The model learns general knowledge first, which can be refined later through fine-tuning.