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Attention is All You Need

The 2017 paper Attention Is All You Need reshaped modern AI by proving that a model built entirely on self-attention—without recurrence or convolution—could learn long-range dependencies efficiently and in parallel, achieving state-of-the-art translation at a fraction of prior computational cost. This Transformer architecture became the foundation for today’s language, vision, and multimodal generative systems, igniting the current AI boom.


But while machines thrive on attention, humans struggle to hold it. As AI scales effortlessly, perhaps the more urgent question is how we protect—and better direct—our own.


 
 
 

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