RNNs Had a Fatal Flaw — Why Transformers Replaced Sequential Processing

Añadido: 2026-05-20

Before 2017, AI models like RNNs processed text sequentially, where each word passed information to the next in a chain, causing information to fade over long distances (e.g., word 1 forgotten by word 50). The 2017 paper 'Attention Is All You Need' by Vaswani et al. introduced Transformers, which allow every word to attend to every other word simultaneously through direct connections, eliminating the sequential chain and preventing information loss. This parallel processing approach enabled AI to capture long-range dependencies and contextual relationships more effectively, revolutionizing natural language processing.