Transformer is a revolutionary neural network architecture that fundamentally changed artificial intelligence and natural language processing. Unlike traditional neural networks, it uses an attention mechanism that allows it to process sequences in parallel rather than sequentially. This makes it incredibly efficient at handling long-range dependencies in data, which is crucial for understanding language and context.
The core innovation of Transformer is the self-attention mechanism. This allows each word in a sequence to look at all other words and determine how much attention to pay to each one. The attention formula uses three matrices: Query, Key, and Value. Each word can attend to every other word simultaneously, creating rich contextual understanding without needing to process words sequentially.
The key difference between Transformers and traditional RNNs is how they process sequences. RNNs must process words one at a time in order, creating a bottleneck that prevents parallel processing. Transformers, however, can process all words simultaneously through self-attention. This parallel processing capability makes Transformers much faster to train and better at capturing long-range dependencies in sequences.
Transformers have become the foundation of modern artificial intelligence. Since their introduction in 2017, they have powered breakthrough models like BERT for language understanding, GPT for text generation, T5 for text-to-text tasks, Vision Transformer for image processing, and DALL-E for generating images from text. These applications now power the chatbots, search engines, translation services, and creative AI tools we use every day.
To summarize what we have learned about Transformers: They are revolutionary neural network architectures that use self-attention mechanisms to process sequences in parallel. This makes them much faster and more efficient than traditional RNNs. Transformers have become the foundation for modern AI breakthroughs like GPT, BERT, and DALL-E, fundamentally changing how we approach natural language processing and artificial intelligence.