Every day, Uber users around the world initiate customer support tickets through our Customer Obsession Platform. To ensure a seamless user experience, each of those tickets must be matched with an agent who speaks the user’s language and who has been trained to handle issues of that type and in that country, among other qualifications. Routing tickets to an agent with the right skillset has become more complex as    

Zoubin Ghahramani is Uber’s Chief Scientist and the Head of AI. The ease and simplicity of Uber’s platform is built on fundamental advances in science and technology. Teams across Uber are committed to developing the most advanced scientific techniques in a wide array of domains, from artificial intelligence and its many sub-fields, including natural language processing and self-driving vehicles, to   To finish reading, please visit source site

Intelligent conversational agents have evolved significantly over the past few decades, from keyword-spotting interactive voice response (IVR) systems to the cross-platform intelligent personal assistants that are becoming an integral part of daily life.  Along with this growth comes the need for intuitive, flexible, and comprehensive research and development platforms that can act as open testbeds to help evaluate new algorithms, quickly prototype, and reliably deploy conversational agents. At Uber AI, we developed the    

This article is based on the paper “Plug and Play Language Models: A Simple Approach To Controlled Text Generation” by Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. The transformer neural network architecture, developed by Vaswani et al. (2017), has enabled larger models and momentous progress in natural language processing (NLP) over the last