Artificial intelligence built by Facebook has learned to classify images from 1 billion Instagram photos. The AI used a different learning technique to many other similar algorithms, relying less on input from humans. The team behind it says the AI learns in a more common sense way.
Conventionally, computer vision systems are trained to identify specific things, such as a cat or a dog. They achieve this by learning from a large collection of images that have been annotated to describe what is in them. After doing this enough, the AI can then identify the same things in new images, for example, spotting a dog in an image it has never seen before.
This process is effective, but must be done afresh with every new thing the AI needs to identify, otherwise performance can drop.
By contrast, the approach used by Facebook is a technique called self-supervised learning, in which the images don’t come with annotations. Instead, the AI first learns just to identify differences between images. Once it is able to do this, it sees a small number of annotated images to match the names with the characteristics it has already identified.
"Artificial intelligence built by Facebook has learned to classify images from 1 billion Instagram photos. The AI used a different learning technique to many other similar algorithms, relying less on input from humans."
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— Money Miles (@niftydunk) March 4, 2021