The Transformer is a deep learning architecture that relies entirely on the self-attention mechanism to process input sequences. Unlike Recurrent Neural Networks (RNNs) or LSTMs, which process data sequentially, Transformers process entire sequences in parallel. This allows for significantly faster training and the ability to capture long-range dependencies in data, meaning they can understand the relationship between words at the beginning and end of a long sentence or document.
Introduced by Google researchers (Vaswani et al.) in the seminal 2017 paper 'Attention Is All You Need'. It was originally designed for machine translation tasks.
The Transformer is the foundation of the modern Generative AI revolution. It powers almost all state-of-the-art language models (BERT, GPT-4, Llama, Claude) and has also revolutionized computer vision (Vision Transformers) and protein folding prediction (AlphaFold).