Editing Google BERT (section)

==Topic Overview==
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a method developed by Google for natural language processing (NLP) pre-training. Launched in 2018, it has been widely implemented in various applications like search engine optimization, content creation, and understanding user intent. BERT is a state-of-the-art machine learning model for NLP tasks, developed by researchers at Google AI Language. It's based on the Transformer architecture and utilizes a bidirectional training of the Transformer, a popular attention model, to understand the context of a word in a sentence. Unlike older models which only examined words in one direction, BERT is bidirectional, allowing it to understand the full context of a word by looking at the words that come before and after it.