The first step in training a neural network to solve a problem is usually the selection of an architecture: a specification of the number of computational nodes in the network and the connections between them. Architectural decisions are generally based on historical precedent, intuition, and plenty of trial and error.Read More
Amazon releases data set of annotated conversations to aid development of socialbots
Today I am happy to announce the public release of the Topical Chat Dataset, a text-based collection of more than 235,000 utterances (over 4,700,000 words) that will help support high-quality, repeatable research in the field of dialogue systems.Read More
DeepMind’s health team joins Google Health
Heres what the future looks like for the team.Read More
Science at Uber: Improving Transportation with Artificial Intelligence
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.
Turning Dialogue Tracking into a Reading Comprehension Problem
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
The Podcast: Episode 8: Demis Hassabis – The interview
In this special extended episode, Hannah Fry meets Demis Hassabis, the CEO and co-founder of DeepMind.Read More
Episode 8: Demis Hassabis – The interview
In this special extended episode, Hannah meets Demis Hassabis, the CEO and co-founder of DeepMind.Read More
Three Approaches to Scaling Machine Learning with Uber Seattle Engineering
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 …
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The 16 Alexa-related papers at this year’s Interspeech
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
Science at Uber: Powering Machine Learning at Uber
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.