BERT, a leading model in Natural Language Processing (NLP), showcases the success of the Transformer architecture, particularly through its unique "masking" learning objective. This masking approach, which involves predicting randomly masked words in a text, distinguishes BERT from other models by enabling a two-phase learning process: context encoding and token reconstruction. Unlike traditional models like Word2Vec, which provide static word meanings, BERT and similar Transformer models utilize bidirectional attention, allowing them to understand context by considering both preceding and succeeding words. Despite its practical success, the exact mechanisms of how masking improves linguistic understanding remain partly unexplained, prompting ongoing research into the intricacies of language learning in humans and machines. This complexity highlights that while BERT's approach is effective for general linguistic tasks, it may not always be suitable for specific applications like text generation, where looking ahead in the text might contradict task objectives.