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Tag: Uber ATG

Solving for Urban Air Travel: A Q&A with François Sillion, Director of Uber ATCP

As head of Uber's Advanced Technologies Center in Paris, Francois Sillion and his team are responsible for supporting the R&D behind Uber Air, our effort to add a third dimension to our platform using flying vehicles.

Power On: Accelerating Uber’s Self-Driving Vehicle Development with Data

A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In our development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe.
Rendered image of street and vehicles

Introducing AVS, an Open Standard for Autonomous Vehicle Visualization from Uber

Uber announces the release of the Autonomous Visualization System (AVS) as an open source project. AVS is a standard for creating a visual environment based on sensor data from autonomous vehicles, with playback available in multiple formats, including the web and video.

From Self-Driving Cars to Optimizing Claims Efficiency: My Unconventional Journey to Insurance Engineering

In this article, engineering manager Lili Kan reflects on her decision to lead Uber's Insurance Engineering team and discusses the challenges—and opportunities—of building insurance products for our platform.

Four Ways Uber Visualization Made an Impact in 2018

Uber's Head of Urban Computing & Visualization reflects on his team's work visualizing data to better understand urban mobility in 2018—and beyond.

Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead

We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.

Announcing the 2019 Uber AI Residency

The Uber AI Residency is a 12-month training program for academics and professionals interested in becoming an AI researcher with Uber AI Labs or Uber ATG.

Scaling Machine Learning at Uber with Michelangelo

Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.

Going Global: Highlights from the Second Annual Uber Technology Day

On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.

Introducing the Uber AI Residency

Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

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.

Engineering More Reliable Transportation with Machine Learning and AI at Uber

In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.

Engineering a Million-Mile Journey with Uber ATG

Uber ATG's Poornima Kaniarasu shares how she found her "place" developing the machine learning technologies behind our self-driving vehicles.

Engineering Uber’s Self-Driving Car Visualization Platform for the Web

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.

Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.

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