Institution
Google's research organization, with foundational work across machine learning, systems, language, and vision.
Language Models · Google Research
BERT made deep bidirectional Transformer pretraining practical, letting one pretrained encoder be fine-tuned into strong task-specific NLP systems with minimal architecture changes.
Text-to-Image · Google Research
Imagen showed that stronger language encoders can materially improve text-to-image diffusion models, especially for prompt alignment and photorealism.
Language Models · Google Research
PaLM used the Pathways system to train a 540B dense Transformer and showed how scale improves few-shot language, reasoning, and code performance.
Language Models · Google Research
T5 unified NLP transfer learning by casting every task as text input to text output, then systematically studying objectives, data, scale, and fine-tuning choices.
Vision Foundation Models · Google Research
ViT showed that a standard Transformer can compete in image recognition when images are split into patches and trained at sufficient scale.
Transformers · Google Research
The Transformer removed recurrence and convolution from sequence transduction, replacing them with attention and parallel training; almost every modern LLM stands on that move.