Transformer

Transformers solved a fundamental problem for AI: how to process entire sentences simultaneously while understanding relationships between all words. A neural network architecture that is particularly well-suited for processing sequential data like text, transformers are the foundation of many modern large language models (LLMs). 

Previous models processed text sequentially, like reading word by word. This shift from sequential to parallel processing marked a profound paradigm change in natural language processing.

Transformers use "attention mechanisms" to consider all words at once, so that models understand context more effectively.

The breakthrough came from the paper "Attention Is All You Need," which demonstrated that attention mechanisms alone could outperform more complex architectures. This parallel processing makes transformers faster to train and better at understanding context, especially in longer texts.

Virtually every major language model builds on transformer architecture. The ability to understand long-range dependencies and process text efficiently has enabled the AI language capabilities we see today. This includes chatbots and content generation tools. 

This architecture's efficiency allows for the training of massive models on enormous datasets. This has led to the sophisticated and human-like text generation we have come to expect and fundamentally transformed the field of AI and its practical applications.