In recent months, Uber Engineering has shared how we use machine learning (ML), artificial intelligence (AI), and advanced technologies to create more seamless and reliable experiences for our users. From introducing a Bayesian neural network architecture that more accurately estimates trip growth, to our real-time features prediction system, and even our own internal ML-as-a-service platform, Michelangelo, these two fields are critical to supporting Uber’s mission of developing reliable transportation solutions for everyone,    

To facilitate the best end-to-end experience possible for users, Uber is committed to making customer support easier and more accessible. Working toward this goal, Uber’s Customer Obsession team leverages five different customer-agent communication channels powered by an in-house platform that integrates customer support ticket context for easy issue resolution. With hundreds of thousands of tickets surfacing daily on the platform across 400+ cities worldwide, this team must ensure that agents are empowered to    

Uber AI Labs and Uber ATG Toronto are excited to announce the Uber AI Residency, an intensive one-year research training program slated to begin this summer.   Uber has invested substantially in machine learning and artificial intelligence, with groups around the company working on a variety of techniques—deep learning, reinforcement learning, neuroevolution, probabilistic modeling,  natural language processing,    

Choice is fundamental to the Uber Eats experience. At any given location, there could be thousands of restaurants and even more individual menu items for an eater to choose from. Many factors can influence their choice. For example, the time of day, their cuisine preference, and current mood can all play a role. At Uber Eats, we strive to help eaters find the exact food they want as effortlessly as possible. We approach    

Earlier this year, we introduced Uber’s Customer Obsession Ticket Assistant (COTA) system, a tool that leverages machine learning and natural language processing (NLP) techniques to recommend support ticket responses (Contact Type and Reply) to customer support agents, with Contact Type being the issue category that the ticket is assigned to and Reply the template agents use to respond. After integrating it into our Customer Support Platform, COTA v1 reduced English-language ticket resolution times by    

Imagine standing curbside, waiting for your Uber ride to arrive. On your app, you see that the car is barely moving. You send them a message to find out what’s going on. Unbeknownst to you, your driver-partner is stuck in traffic en route to your pick-up location. They receive your message and want to reply back. This scenario is something Uber driver-partners tell us is a pain point. So we began thinking, what if    

High quality map data powers many aspects of the Uber trip experience. Services such as Search, Routing, and Estimated Time of Arrival (ETA) prediction rely on accurate map data to provide a safe, convenient, and efficient experience for riders, drivers, eaters, and delivery-partners. However, map data can become stale over time, reducing its quality. As a customer-obsessed company, Uber reviews and