In Depth
BERT (Bidirectional Encoder Representations from Transformers) was released by Google in 2018 and represented a major breakthrough in how machines understand language. Unlike earlier models that read text left-to-right or right-to-left, BERT reads entire sentences at once, allowing it to grasp the full context of each word based on its surroundings.
BERT is primarily used for understanding tasks rather than generating text. It powers search engine improvements, sentiment analysis, question answering, and named entity recognition. Google integrated BERT into its search algorithm in 2019, marking one of the largest improvements to search relevance in years.
The model introduced the concept of masked language modeling, where random words in a sentence are hidden during training, and the model learns to predict them. This approach, combined with its bidirectional nature, made BERT the foundation for hundreds of subsequent models and established the pre-train-then-fine-tune paradigm that dominates modern AI.