Uber AI is at the heart of AI-powered innovation and technologies at Uber. AI research and its applications solve challenges across the whole of Uber.
Uber AI in 2019: Advancing Mobility with Artificial Intelligence
Artificial intelligence powers many of the technologies and services underpinning Uber’s platform, allowing engineering and data science teams to make informed decisions that help improve user experiences for products across our lines of business.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
At Uber, many of the hard problems we work on can benefit from machine learning, such as improving safety, improving ETAs, recommending food items, and finding the best match between riders and drivers.
Controlling Text Generation with Plug and Play Language Models
Plug and Play Language Model, introduced by Uber AI Labs, gives NLP practitioners the flexibility to plug in one or more simple attribute models into a large, unconditional language model.
Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations
Uber AI's graph neural netowrk based method is used used for improving the quality of dish and restaurant recommendations in Uber Eats. The articledetails the algorithm, the experiments and the pipeline, and also shows examples of how they all work together in production to improve the user experience.Marketplace.
COTA: Improving Uber Customer Care with NLP & Machine Learning
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.
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Uber uses convolutional neural networks in many domains that could potentially involve coordinate transforms, from designing self-driving vehicles to automating street sign detection to build maps and maximizing the efficiency of spatial movements in the Uber Marketplace.
Improving Pickups with Better Location Accuracy
Uber launches Beacon V2, a piece of hardware for Uber drivers, that implements on device sensor fusion using signals from GNSS, IMU, and barometer to improve location accuracy.
Introducing Hypothesis GU Funcs, an Open Source Python Package for Unit Testing
Hypothesis-gufunc is an extension to hypothesis to aid in unit testing broadcasting and matrix input numpy/torch functions.
Introducing LCA: Loss Change Allocation for Neural Network Training
We develop a new measure called Loss Change Allocation (LCA) to better understand the training process. LCA measures how much each parameter helps decrease (or increase) the loss at every iteration. It is able to reveal broader patterns such as how training is very noisy, some layers hurt, and micro-learning is synchronized across layers.
Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox
Ludwig 0.2 is released with a host of new functionalities and features. The major ones are the integration with Comet.ml, the addition of BERT among text encoders, the implementation of audio/ speech, H3 (geospatial) and date (temporal) features, substantial improvements on the visualization API, and the inclusion of a serving functionality.
Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution
Uber AI Labs releases EvoGrad, a library for catalyzing gradient-based evolution research, and Evolvability ES, a new meta-learning algorithm enabled by this library.
Teams at Uber AI
We leverage sensors as a source of truth and develop algorithms to solve our users' top pain points, ranging from crash detection to improved GPS, menu OCR to automatic driver license approval, and many more use cases, requiring expertise in signal processing, computer vision and NLP. Click to see our publications.
Open Source Projects