The Transformer: Core Ideas from 'Attention Is All You Need'

Transformers revolutionized AI by replacing sequential RNN processing with parallel self-attention mechanisms that process all words simultaneously. This architecture solves long-range dependency problems by allowing any word to directly reference others, while enabling faster training through parallelization—ultimately powering today's large language models like ChatGPT through its ability to efficiently process massive datasets.