Transformer
A neural network architecture that processes sequences using self-attention, enabling models to weigh the importance of different parts of the input.
The Architecture Behind Modern AI
Transformers changed everything. Introduced in 2017, this architecture powers virtually every state-of-the-art language model, including GPT, Claude, and Gemini. It also revolutionized image generation, speech recognition, and protein structure prediction. If there's one technical innovation that defines the current AI era, it's the transformer.
What makes transformers special is how they handle sequences. Earlier models processed data step by step - read word one, then word two, then word three. Transformers see everything at once and use attention mechanisms to figure out which parts matter for understanding each element.
Why Transformers Won
The magic is parallelization. Because transformers process sequences all at once rather than step by step, they train dramatically faster on modern GPUs. This made it practical to scale models to billions of parameters trained on internet-scale data.
Transformers also handle long-range dependencies beautifully. In a sentence like "The trophy doesn't fit in the suitcase because it's too big," understanding what "it" refers to requires connecting words far apart. Older architectures struggled with this. Transformers excel at it because every element can directly attend to every other element. This architectural advantage compounds as you scale up - bigger transformers continue to get smarter in ways that surprised even their creators.