Transformers power 90% of modern AI models
Transformer architectures now underpin over 90% of state-of-the-art AI models reports, driving breakthroughs across NLP and image processing.
The dominance of transformers stems from their ability to handle sequential data in parallel, unlike recurrent neural networks. This allows for faster training and the capture of long-range dependencies in data. Google's initial research on transformers, particularly the development of the Transformer architecture, paved the way for models like BERT and subsequent advancements. These models have become foundational in NLP, enabling breakthroughs in machine translation, text summarization, and question answering. Their impact extends beyond NLP; transformers are increasingly used in computer vision for tasks like image recognition and object detection. This is due to their ability to model relationships between different parts of an image, similar to how they model relationships between words in a sentence.
