Self-supervised learning is a type of machine learning that falls under the broader umbrella of artificial intelligence (AI). Unlike supervised learning, where models are trained with labeled data (data where the answer is already known), self-supervised learning doesn't require labeled datasets. Instead, it learns from the data itself by creating its own labels from the inherent structure of the data. This approach is particularly useful because it reduces the need for human effort in labeling the data, which can be time-consuming and expensive.

Imagine you have a book with lots of pictures and texts but without any captions to describe the images. In self-supervised learning, the AI might learn to understand the content of the pictures by reading the surrounding text and using it to predict what the pictures might represent. It essentially teaches itself by looking for patterns or by creating tasks where it predicts one part of the data using another part of the data.

This method is especially powerful in fields where large amounts of unlabeled data exist, such as natural language processing, where texts can be abundant but not always annotated. In self-supervised learning, AI models might predict the next word in a sentence or fill in missing words, using these tasks to learn about the language's structure and nuances.

Self-supervised learning can also be used in computer vision, where it might learn to recognize objects in images by predicting missing parts of those images or by distinguishing between altered and unaltered parts of the same image. The beauty of this approach is its flexibility and efficiency, enabling AI to learn from a vast array of unstructured data without extensive manual input.