Self-supervised learning is a special type of machine learning where a computer learns from its own data without anyone telling it what is right or wrong.
Imagine you get a puzzle, but without a picture on the box. You try to put the pieces together by giving yourself clues.
That's how self-supervised learning works: the system creates its own tasks from the data to learn something new.
This is practical because there is a lot of data that is not specially labeled with explanations, and the computer can still learn from it.
Self-supervised learning thus helps computers handle large amounts of data better and independently recognize patterns.