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Tag: Uber AI

Introducing Carbon Feed for Earners: The One-Stop Info Shop

After launching the Driver App in 2018 to over 2 million earners worldwide, we added content and functionality at a rapid pace. Although this really bolstered the platform, allowing for high-density and high-frequency content,...

DeepETA: How Uber Predicts Arrival Times Using Deep Learning

At Uber, magical customer experiences depend on accurate arrival time predictions (ETAs). We use ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Traditional routing engines compute ETAs...

Meta-Graph: Few-Shot Link Prediction Using Meta-Learning

Uber AI introduces Meta-Graph, a new few-shot link prediction framework that facilitates the more accurate training of ML models that quickly adapt to new graph data.

Announcing a New Framework for Designing Optimal Experiments with Pyro

Uber AI released a new framework on top of Pyro that lets experimenters seamlessly automate optimal experimental design (OED) for quicker model iteration.

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Building upon our existing open-ended learning research, Uber AI released Enhanced POET, a project that incorporates an improved algorithm and allows for more diverse training environments.
Uber AI in 2019: Advancing Mobility with Artificial Intelligence

Uber AI in 2019: Advancing Mobility with Artificial Intelligence

In 2019, Uber AI built tools and systems that leverage ML to improve location accuracy and enhance real-time forecasting, among other applications on our platform.

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

Developed by Uber AI Labs, Generative Teaching Networks (GTNs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.

Introducing LCA: Loss Change Allocation for Neural Network Training

Uber AI Labs proposes Loss Change Allocation (LCA), a new method that provides a rich window into the neural network training process.

Advancing AI: A Conversation with Jeff Clune, Senior Research Manager at Uber

We sat down with Jeff Clune, Senior Research Manager, to talk about his work in AI, journey to Uber, and Presidential Early Career Achievement in Science and Engineering (PECASE) award.

Science at Uber: Applying Artificial Intelligence at Uber

Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.

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

The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.

No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox

Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.

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.

Pyro Accepted by the LF Deep Learning Foundation as a Hosted Project

Created by Uber in 2017, Pyro was voted in by the Linux Foundation Deep Learning Technical Board as the latest incubation project to join its foundation.

Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro...

We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.

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

The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.

Introducing Ludwig, a Code-Free Deep Learning Toolbox

Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.