Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
Uber ATG's Poornima Kaniarasu shares how she found her "place" developing the machine learning technologies behind our self-driving vehicles.
Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.
Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.