Bidirectional Encoder Representations (BERT) is a deep learning model that revolutionizes natural language processing by understanding the context of a word based on its surrounding words in a sentence, using a transformer-based architecture. It achieves state-of-the-art performance by pre-training on a large corpus of text and fine-tuning on specific tasks such as question answering and sentiment analysis.