Explanation
Imagine learning to ride a bicycle without anyone directly teaching you. You might wobble, fall, and gradually figure it out through trial and error, using your own sense of balance and observation to improve. Self-supervised learning is akin to that.
It's a type of machine learning where the AI learns from unlabelled data by creating its own 'supervisory' signals.
Instead of relying on humans to label the data, the AI cleverly extracts information from the data itself to generate tasks to learn from.
For instance, an AI might be shown a picture and tasked with predicting what part of the image is missing. Or, it might listen to a piece of music and be asked to predict the next note.
By solving these self-generated tasks, the AI develops a deeper understanding of the underlying data, learning patterns and relationships without explicit human guidance.
It's like the AI is teaching itself by exploring and experimenting with the world around it.
Examples
Consumer Example
Consider an AI image generator like DALL-E or Midjourney.
These AI systems are trained on massive datasets of unlabelled images and text. Through self-supervised learning, they learn the relationships between words and images.
For example, they might be shown an image and asked to predict the corresponding text description, or vice versa.
This enables them to generate incredibly realistic and creative images from text prompts.
Business Example
Imagine a manufacturing company using self-supervised learning to improve quality control.
They could feed the AI a large dataset of unlabelled images of manufactured products. The AI could then learn to identify defects by predicting which parts of the images are unusual or inconsistent.
This allows the company to automate the inspection process, identify defects early on, and improve product quality, all without needing humans to manually label thousands of images.