I didn’t know. Are you somewhat informed about the history of models? I’d love to hear it from you instead of a random crypto bros’ LLM summary. Thanks!
I don’t remember too much tbh, just that we heard about the theory at university and tried out some of the mathematical methods. They were tiresome ;)
Today I would recommend to start your studies on the wikipedia pages about Markov models and about machine learning.
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.
As a transmillenial student of AI / ML, great write-up.
howrar@lemmy.ca 8 months ago
Yann Lecun gave us convolutional neural networks (CNNs) in 1998. These are the models that are used for pretty much all specialized computer vision tasks even today. TinyEye came into existence ten years later in 2008. I can’t tell you if they used CNNs, but they were certainly available.