Transformer Architecture (2017): Introduced in the paper “Attention Is All You Need” by Vaswani et al. at Google, the Transformer architecture revolutionized natural language processing by replacing recurrent and convolutional layers with self-attention mechanisms. This allowed models to process words in parallel, capture long-range dependencies, and scale efficiently. Transformers enabled faster training and superior performance on translation, summarization, and question-answering tasks. They became the foundation for BERT, GPT, and other large language models that now power chatbots, coding assistants, and content generation. The key innovation—attention—lets models weigh the relevance of each word in context dynamically. By decoupling sequence processing from recurrence, Transformers unlocked the era of foundation models trained on massive datasets. Today, nearly all state-of-the-art AI language systems rely on this architecture, making it one of the most influential AI milestones of the 21st century.
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