Google Search used to work by matching keywords. You type "jaguar speed." You get pages about the Jaguar car. Because "speed" and "jaguar" appear on car performance pages. The fact that you might mean the animal does not matter. Keywords do not carry context. In 2019, Google upgraded its search to use BERT. Now when you type "can you get medicine for someone pharmacy," the model understands that "for someone" means you are picking up a prescription for another person, not buying it for yourself. That context completely changes the relevant results. BERT is a transformer encoder pretrained on 3.3 billion words using a clever self-supervised objective: predict randomly masked words. No labels required. The pretraining forces the model to build deep contextual understanding of language. Then you fine-tune on your specific task with a small labeled dataset. The results shifted the entire field. In November 2018, BERT achieved state-of-the-art on 11 NLP benchmarks simultaneously.…