How do AI models learn?
Models “learn” by seeing many examples: compare outputs to targets, compute loss, then update weights with gradient descent (or variants) to reduce loss.
This repeats over many epochs on a dataset. For language models, a typical objective is next-token prediction (self-supervised).
See training animations and loss curves in the mej7.com lab — simplified numbers for teaching, not a production training run.