The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.
Ludwig version 0.2 integrates with Comet.ml, adds a new serving functionality, and incorporates the BERT text encoder, among other new features.
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.
Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.
Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.
Curious about what it is like to traverse the high-dimensional loss landscapes of modern neural networks? Check out Uber AI Labs’ latest research on measuring intrinsic dimension to find out.
Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.
Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.
Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.
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.
Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.
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.
Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool to help neuroevolution researchers better understand this family of algorithms.
Uber AI released a new framework on top of Pyro that lets experimenters seamlessly automate optimal experimental design (OED) for quicker model iteration.
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.
Attending ICCV, CoRL, or IROS 2019? Learn about Uber ATG's recent research in artificial intelligence by checking out our workshops, posters, and keynotes.
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.
Uber welcomes Jan Pedersen as a Distinguished Scientist to our Uber AI group, where he will bring his extensive experience to our efforts in improving artificial intelligence and machine learning.
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 hosted its first Open Summit on November 15, inviting the open source community to learn about our open source projects from the engineers who use them every day. Check out highlights from the day, including keynotes from the Linux Foundation's Jim Zemlin and Uber AI's Zoubin Ghahramani.