Using GPS and sensor data from Android phones, Uber engineers develop a state model for trips taken by Uber Eats delivery-partners, helping to optimize trip timing for delivery-partners and eaters alike.
By Ryan Waliany, Lei Kang, Ernel Murati, Mohammad Shafkat Amin and Nikolaus Volk /
On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.
Applying hardware acceleration to deep neuroevolution in what is now an open source project, Uber AI Labs was able to train a neural network to play Atari in just a few hours on a single personal computer, making this type of research accessible to a far greater number of people.
By Felipe Petroski Such, Kenneth O. Stanley and Jeff Clune /
Differentiable Plasticity is a new machine learning method for training neural networks to change their connection weights adaptively even after training is completed, allowing a form of learning inspired by the lifelong plasticity of biological brains.
By Thomas Miconi, Jeff Clune and Kenneth O. Stanley /
Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.
By Jason Yosinski, Zoubin Ghahramani and Raquel Urtasun /
In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.
A daylong event at Uber’s Palo Alto office, sponsored by our LadyEng group, showcased the technical work across Uber Engineering as well as the people who are leading and building these projects. Here are some of the resulting presentations.