Attention Mechanism
The attention mechanism enhances AI models by helping them focus on the most relevant information in a dataset. It plays a vital role in state-of-the-art language models and machine translation.
The attention mechanism is a technique in AI that allows models to focus on specific parts of the input data, assigning different weights to different parts based on relevance. This is widely used in natural language processing (NLP) tasks, particularly in models like Transformers, which power technologies such as GPT and BERT. Attention improves the performance of models in tasks like language translation and summarization.