What is Self-Supervised Learning?

Self-Supervised Learning is a training method where an AI model teaches itself by creating its own puzzles from raw data and then trying to solve them. For instance, the model might learn language by trying to predict missing words in sentences, or learn about images by guessing which pieces belong together. This technique has become essential for training large AI models because it allows them to learn from vast amounts of data—like all the text on the internet—without requiring expensive and time-consuming human annotation.