Tag: Deep Learning
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.
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.
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.
Pyro Accepted by the LF Deep Learning Foundation as a Hosted Project
Created by Uber in 2017, Pyro was voted in by the Linux Foundation Deep Learning Technical Board as the latest incubation project to join its foundation.
Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro...
We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.
First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL
The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.
Introducing Ludwig, a Code-Free Deep Learning Toolbox
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the...
Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.
Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead
We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.
Experience in AI: Uber Hires Jan Pedersen
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.
NVIDIA: Accelerating Deep Learning with Uber’s Horovod
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.
Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning
Uber's Advanced Technologies Group introduces Petastorm, an open source data access library enabling training and evaluation of deep learning models directly from multi-terabyte datasets in Apache Parquet format.
Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning
Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.
Transforming Financial Forecasting with Data Science and Machine Learning at Uber
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.
Measuring the Intrinsic Dimension of Objective Landscapes
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.
Accelerating Deep Neuroevolution: Train Atari in Hours on a Single Personal Computer
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.
Differentiable Plasticity: A New Method for Learning to Learn
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.
Introducing the Uber AI Residency
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.
