The OCR model we developed can read text across various font sizes and colors, effectively identifying text in Street View images.

We partner with the largest satellite companies to source high-quality imagery and collect lidar data, which is crucial for improving our algorithms and expanding our model globally.

The Maps team utilized a similar model to assess solar energy potential on rooftops by identifying rooftops in images, demonstrating the versatility of our models.

Architecturally, there's a significant similarity among various AI models, including video diffusion models like Veo and text language models, which share a lot of core elements.

We've learned a lot through the process of training this model, discovering what scales well and what doesn't.

Deep learning applications at Google include Google Photos, which effectively utilizes computer vision to understand the content of images.

The video shown was entirely generated by a custom model, illustrating the capabilities of fine-tuning general-purpose video generation models on specific domain data.

The model developed for Google Translate utilized a deep LSTM stack with an attention module, allowing it to track multiple states and focus on relevant input data during output generation.

Claude can analyze images and respond with text, and now it has been trained to recognize specific actions on a computer screen, such as clicking buttons or typing text based on what it sees.