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Uber AI

Uber AI is at the heart of AI-powered innovation and technologies at Uber. The research and advancements in artificial intelligence made by the team solve challenges across the whole of Uber.

Uber AI

Uber AI is at the heart of AI-powered innovation and technologies at Uber. The research and advancements in artificial intelligence made by the team solve challenges across the whole of Uber.

Uber AI Labs

Our world-class team at Uber AI Labs pursues fundamental research in machine learning and connects cutting-edge advances to the broader business. Machine learning is essential to our business, and we are therefore fully committed to the pursuit of fundamental advances and vigorous engagement with the broader machine learning community.

Computer vision

Cameras provide a unique insight into the complex world Uber operates in. Our mission is to drive product differentiation and business efficiencies at Uber using visual data. We work together with product teams to develop state of the art computer vision and machine learning solutions.

Conversational AI

Our mission is to make all Uber interactions as natural as talking to a friend! We deliver cutting-edge Conversational AI solutions to product teams across Uber, working with other AI teams and business units.

Sensing & Perception

We believe sensors tell a story. Our goal is to leverage sensors as a source of truth and develop algorithms to solve our users' top pain points. The team has improved features like crash detection, enhanced location accuracy innovations, and even sent phone mounts to drivers for safer driving.

Uber AI News

  • 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.

  • Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform

    Plato Research Dialogue System is a platform for building, training, and deploying conversational AI agents that allows us to conduct state of the art research in conversational AI and quickly create prototypes and demonstration systems, as well as facilitate conversational data collection.

  • Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

    Uber builds upon the Lottery Ticket Hypothesis by proposing explanations behind these mechanisms and deriving a surprising by-product: the Supermask.

  • Introducing the Uber Research Publications Site

    Uber's Chief Scientist announces the launch of the Uber Research Publications Site, a portal for showcasing our contributions to the research community.

  • Modeling Censored Time-to-Event Data Using Pyro, an Open Source Probabilistic Programming Language

    Uber scientists demonstrate how to train time-to-event models from censored data using the Pyro probabilistic programming language.

  • First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL

    Uber hosted the first-ever Uber Science Symposium, a full-day event of presentations and conversations with stellar speakers and attendees, focusing on reinforcement learning, natural language processing and conversational AI, deep learning and deep learning infrastructure.

  • Introducing Ludwig, a Code-Free Deep Learning Toolbox

    Uber AI introduces a toolbox to train deep learning models and use them for prediction that does not require any coding.

  • Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

    To make research into understanding deep RL easier to conduct, by creating and open sourcing a repository of trained Atari Learning Environment agents along with tools to understand and analyze their behavior.