#11 Attention is All You Need (2017)

In 2017, eight researchers at Google published a seminal paper with this almost throwaway title. The paper proposed a new deep learning model that relies entirely on self-attention mechanisms, completely replacing the recurrent and convolutional neural networks that were standard at the time. It described a new way for machines to understand language, not by reading word by word, but by looking at everything at once and deciding what mattered.  It was called Transformer architecture, and like many foundational inventions, it was quiet, technical, and easy to miss at the time. It was used by every major AI system that followed, making modern AI possible.

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