Attention Mechanism

An Attention Mechanism lets a neural network focus on the most relevant parts of its input when producing each output, rather than treating all inputs equally. The concept was introduced for machine translation in 2014-2015 (Bahdanau et al.) and proved transformational — instead of compressing the entire source sentence into a single fixed vector, the model could "look back" at any source word as needed. Attention now powers translation in Google Translate, summarization tools, image captioning, and the entire transformer family of large language models. The general formula uses queries, keys, and values to compute weighted combinations of input representations. While technical, this AI term matters for AI governance because interpretability tools often inspect attention weights to support AI compliance and responsible AI explainability — though attention weights are an imperfect proxy for model reasoning and should not be treated as definitive explanations in regulated settings.

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