From Sequential to Simultaneous: The Transformer Revolution

Transformers fundamentally changed AI by replacing sequential token processing with parallel self-attention computation, enabling the large language models that drive today's AI revolution. Unlike RNNs, which process text step-by-step, Transformers compare all tokens simultaneously—a shift that makes them dramatically more efficient and capable of capturing long-range dependencies essential for language understanding.