What Is Bidirectional Encoder Representations from Transformers (BERT)?

BERT is a revolutionary new deep learning strategy helping artificial intelligence programs understand the context of ambiguous words in the text. BERT stands for Bidirectional Encoder Representations from Transformers. It's like a transformer, but with less robot and more data. And it's bidirectional! That means you can send information both ways, which helps when trying to understand context and meaning. BERT takes advantage of how our brains process language by relying on visual cues. It's like when you look at an image of a cat and suddenly start thinking about cats everywhere, even if there aren't any around—you know what we mean? That's basically what BERT does: it takes in information about words and then tries to find similar words based on their visual properties (like shape). So far, BERT has been used to improve AI systems' understanding of images (by making them better at recognizing cats), as well as their ability to parse speech patterns and identify topics being discussed in conversation—and now we're excited to bring this technology into the world of NLP! BERT, the brain behind Google's new NLP model, is a bidirectional Encoder Representation with Gated Recurrent Units. Bert uses gated recurrent units to simulate how our brains process information: it builds a model of meaning based on the context of words and their prior occurrences. This differs from previous methods that treated each word as an independent unit; instead, Bert can learn from patterns across multiple words. Because of this ability to learn from context, Bert can create a more nuanced understanding of language than other models—both in terms of meaning and grammar. It can understand questions that require context to answer (e. g., "What did he say? "), as well as more complex sentences that require context for understanding (e. g., "The man who was walking saw a dog"). This makes it more accurate at answering questions than previous models—and faster!