Every large language model, image generator, and modern recommender shares one idea at its core: self-attention. Strip away the math and it's surprisingly intuitive.
The 2017 paper that introduced it was titled "Attention Is All You Need," and a decade of AI has more or less proven the claim. But most explanations drown the intuition in linear algebra. Let me give you the mental model first; the equations make sense only after.
The problem attention solves
Read this sentence: "The trophy didn't fit in the suitcase because it was too big." What does "it" refer to — the trophy or the suitcase? Obviously the trophy. You resolved that by letting the word "it" look back at the other words and decide which ones matter.
That's the entire idea of self-attention. For every word, the model looks at every other word in the sequence and asks: how relevant is each of you to understanding me right now? It then builds each word's representation as a weighted blend of the others, weighted by relevance. "It" pulls strongly from "trophy" and "big," weakly from "suitcase." Meaning stops being about a word in isolation and becomes about a word in context.
