Transformer

The transformer is a type of neural network architecture introduced by Google researchers in a landmark 2017 paper titled "Attention Is All You Need." Before transformers, AI language systems processed text one word at a time from left to right, which was slow and made it hard to understand how words far apart in a sentence relate to each other.

The transformer's breakthrough innovation is called "attention." Instead of reading text sequentially, a transformer looks at all the words in a passage at once and figures out which words are most relevant to each other. For example, in the sentence "The cat sat on the mat because it was tired," the attention mechanism helps the model figure out that "it" refers to "the cat" and not "the mat." This ability to see the big picture all at once makes transformers much better at understanding context and meaning.

This architecture turned out to be incredibly powerful and scalable. When you make a transformer bigger -- more layers, more attention heads, more parameters -- it gets better and better at language tasks in a way that previous architectures did not. This scalability is what enabled the creation of large language models. GPT, Claude, Gemini, and LLaMA are all built on the transformer architecture.

Transformers are not limited to text either. Researchers have adapted them for images (Vision Transformers), audio, video, and even protein structure prediction. The transformer has become the Swiss Army knife of modern AI, which is why that original paper's title turned out to be prophetic -- attention really was all you needed.