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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

By integrating graph learning techniques with our Uber Eats recommendation system, we created a more seamless and individualized user experience for eaters on our platform.

Uber Goes to NeurIPS 2019

Uber is presenting 11 papers at the NeurIPS 2019 conference in Vancouver, Canada, as well as sponsoring workshops including Women in Machine Learning (WiML) and Black in AI.

Announcing the 2020 Uber AI Residency

Uber's 2020 AI Residency will focus on initiatives related to our self-driving car project through Uber Advanced Technology Group (ATG).

Get to Know Uber ATG at ICCV, CoRL, and IROS 2019

Attending ICCV, CoRL, or IROS 2019? Learn about Uber ATG's recent research in artificial intelligence by checking out our workshops, posters, and keynotes.

Evolving Michelangelo Model Representation for Flexibility at Scale

To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
Pedestrian density map

Searchable Ground Truth: Querying Uncommon Scenarios in Self-Driving Car Development

When developing Uber's self driving car systems, engineers found a way to identify edge case scenarios amongst terabytes of sensor data representing real-world situations.
Zoubin Ghahramani

Science at Uber: Improving Transportation with Artificial Intelligence

Uber Chief Scientist Zoubin Ghahramani explains how artificial intelligence went from academia to real-world applications, and how Uber uses it to make transportation better.

Three Approaches to Scaling Machine Learning with Uber Seattle Engineering

At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
Logan Jeya

Science at Uber: Powering Machine Learning at Uber

Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.

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.

Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox

Ludwig version 0.2 integrates with Comet.ml, adds a new serving functionality, and incorporates the BERT text encoder, among other new features.

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.

Gaining Insights in a Simulated Marketplace with Machine Learning at Uber

Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.

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.

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