At Uber, we take advanced research work and use it to solve real world problems. In our Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies …
The post Science at Uber: Improving Transportation with Artificial Intelligence appeared first on Uber Engineering Blog.
During a conversation between a customer and a dialogue system like Alexa’s, the system must not only understand what the customer is saying currently but also remember the conversation history. Only by combining the history with the current utterance can the system truly understand the customer’s requirements.Read More
In this special extended episode, Hannah meets Demis Hassabis, the CEO and co-founder of DeepMind.Read More
Uber’s services require real-world coordination between a wide range of customers, including driver-partners, riders, restaurants, and eaters. Accurately forecasting things like rider demand and ETAs enables this coordination, which makes our services work as seamlessly as possible.
In an effort …
The post Three Approaches to Scaling Machine Learning with Uber Seattle Engineering appeared first on Uber Engineering Blog.
At next week’s Interspeech, the largest conference on the science and technology of spoken-language processing, Alexa researchers have 16 papers, which span the five core areas of Alexa functionality.Read More
At Uber, we take advanced research work and use it to solve real world problems. In our Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies …
The post Science at Uber: Powering Machine Learning at Uber appeared first on Uber Engineering Blog.
Neural networks (NNs) have become prolific over the last decade and now power machine learning across the industry. At Uber, we use NNs for a variety of purposes, including detecting and predicting object motion for self-driving vehicles, responding more …
The post Introducing LCA: Loss Change Allocation for Neural Network Training appeared first on Uber Engineering Blog.
AI researchers around the world are trying to create a general purpose learning system that can learn to solve a broad range of problems without being taught how. Hannah explores the journey to get there.Read More
Our waking and sleeping lives are punctuated by fragments of recalled memories: a sudden connection in the shower between seemingly disparate thoughts, or an ill-fated choice decades ago that haunts us as we struggle to fall asleep.Read More
Earlier this year, we reported a speech recognition system trained on a million hours of data, a feat possible through semi-supervised learning, in which training data is annotated by machines rather than by people. These sorts of massive machine learning projects are becoming more common, and they require distributing the training process across multiple processors. Otherwise, training becomes too time consuming.Read More
