Transformers are often mentioned as the backbone of modern AI. Can someone explain how they work in a way that’s understandable without deep math?
Transformers revolutionized AI by introducing the concept of self‑attention. Instead of processing words one by one like RNNs, transformers look at all words in a sentence simultaneously and decide which words are most relevant to each other.
For example, in the sentence “The cat sat on the mat because it was tired”, the model needs to know that “it” refers to “the cat.”
Self‑attention allows the model to weigh that relationship correctly.
Transformers consist of encoder and decoder blocks, each with layers of attention and feed‑forward networks.
This architecture scales well, enabling training on massive datasets. That’s why models like GPT‑4 can handle complex reasoning and context across long passages.