Machine learning has been a field for years, as others said, yeah, but Wikipedia would be a better expansion of the topic. In a nutshell, it’s largely about predicting outputs based on trained input examples.

It doesn’t have to be text. For example, astronmers use it to find certain kinds of objects in raw data feeds. Object recognition (identifying things in pictures with little bounding boxes) is an old art at this point. And yes, image similarity is another, though not entirely machine learning based. IDK what Tineye does in their backend, but there are some more “oldschool” approaches using more traditional programming techniques, generating signatures for images.

Seperately, image similarity metrics (like lpips or SSIM), that measure the difference between two images as a number (where, say, 1 would be a perfect mach and 0 totally unrelated) are common components in machine learning pipelines. So are text embedding models, which do the same with text.