Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform

Summary

Uber open sources yet another internal project, this time a framework for designing, training and deploying conversational AI to further the research state in this field.  In comparison to other frameworks, their framework Plato claims to have the most flexible architecture for plugging in different deep learning libraries, building different flows, and supporting multi-agent conversational dialogsenabling agents how to learn to exchange information between themselves.  The framework is fully downloadable from their github page.

Commentary

This matters because as we deploy more machine learning applications into the public, they can start to have more user-interaction enabled by understanding humans in the way we communicate best, through natural conversation. It also enhances the information an AI agent can convey to a user.  It makes sense that Uber would work on a project like this; for the case of a self driving car, the system may need to get more information from a rider about destination or changes to the plan (eg: “I’m feeling car sick” -> Car should stop or open the windows, or someone could alternatively ask for information on an area).
 

Library of SOTA NLP Transformers Written in PyTorch

https://github.com/huggingface/pytorch-transformers

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