Attention mechanism

An attention mechanism is a machine learning technique that helps models focus on the most relevant parts of input data when making predictions. Instead of treating all inputs equally, the model assigns different weights to each element based on its importance for the task at hand.

Attention is a key feature of modern AI architectures like Transformers and BERT, powering applications in natural language processing (NLP) and computer vision. It allows models to

• Handle long sequences and capture relationships between distant elements in the data;
• Prioritize the most informative parts of the input, improving prediction accuracy; and
• Provide interpretability, showing which inputs influenced the output.

Common applications of attention mechanisms include machine translation, text summarization, image captioning, speech recognition, and building large language models like GPT.