Tag: artificial intelligence
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.
Building a Backtesting Service to Measure Model Performance at Uber-scale
We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.
Year in Review: 2019 Highlights from the Uber Engineering Blog
To cap off 2019, the Uber Engineering Blog editors present a selection of our most popular articles covering a range of technical topics, from AI to mobile development.
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.
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.
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.
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.
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 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.
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.
Data Science at Scale: A Conversation with Uber’s Fran Bell
We spoke to Data Science Director Fran Bell about machine learning at Uber and what she finds most challenging—and rewarding—about her work.
Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training
Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.
