Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.
At the Uber Open Summit Sofia 2019, we showcased how open source technologies are driving the future of artificial intelligence, site reliability, and other domains.
With zero downtime, Uber's Payments Engineering team embarked on a migration that would allow authorization hold logic to be written once and used across existing and future payments products.
Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.
Uber Poet, an open source mock application generator, helped us determine if refactoring the application part of our code into a few large modules would make our overall Swift build times faster.
We redesigned Uber's web-based booking flow for riders who prefer a browser over the app, simplifying pickup options and speeding up interactivity.
During our 2019 Uber European Technology Showcase, technical teams across the company discussed how we build products that drive safe and reliable transportation.
Ludwig version 0.2 integrates with Comet.ml, adds a new serving functionality, and incorporates the BERT text encoder, among other new features.
Uber Director of Data Science Franziska Bell discusses how we created data science platforms at Uber, letting employees of all technical skills perform forecasts and analyze data.
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.
The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.
Uber's MoneyCon brought together industry leaders to discuss the latest technologies and key learnings in the payments and finance engineering space.
In a selection of presentations delivered at a June 2019 Uber meetup, we discuss how to use H3, our open source hexagonal indexing system, to facilitate the granular mining of large geospatial data sets.
As head of Uber's Advanced Technologies Center in Paris, Francois Sillion and his team are responsible for supporting the R&D behind Uber Air, our effort to add a third dimension to our platform using flying vehicles.
Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.
Uber Labs leverages causal inference, a statistical method for better understanding the cause of experiment results, to improve our products and operations analysis.
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.
Jennifer Anderson, a veteran of Silicon Valley technology companies, leads Uber's Product Platform organization, which hosts our core services. In this interview, she describes her organization and the lessons she has learned.