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Uber AI Labs

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20 FEB

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.

3 JAN

COTA: Improving Uber Customer Care with NLP & Machine Learning

In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.

21 DEC

Year in Review: 2017 Highlights from the Uber Engineering Blog

To ring in the New Year, the Uber Engineering Blog shares some of our editor's picks for 2017.

18 DEC

Welcoming the Era of Deep Neuroevolution

By leveraging neuroevolution to train deep neural networks, Uber AI Labs is developing solutions to solve reinforcement learning problems.

7 DEC

Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning

Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.

15 NOV

Welcoming Peter Dayan to Uber AI Labs

Arriving now: Uber's Chief Scientist Zoubin Ghahramani introduces Uber AI Labs' newest team member, award-winning neuroscientist Peter Dayan.

10 NOV

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.

3 NOV

Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language

Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.

9 JUN

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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