We are glad to announce that MuyVentive, LLC has joined the NVIDIA Inception program, which is designed to nurture startups revolutionizing industries with advancements in AI and data sciences.
The NVIDIA Inception Program will provide MuyVentive with the tools and resources to help accelerate the development of our cutting-edge conversational AI product as well as our other AI-powered solutions.
NVIDIA’s Inception program is a virtual accelerator program that helps startups during critical stages of product development, prototyping and deployment. Every Inception member gets a custom set of ongoing benefits, from hardware grants and marketing support to training with deep learning experts.
In our first blog post we are going to explore a topic in the intersection of natural language understanding (NLU), machine learning explainability and visualization. In particular, we are going to revisit an article we published on MSDN magazine earlier this year. The article discusses how to use explainable machine learning techniques to improve intent classification for natural language understanding, an important building block for chatbots and voice interfaces. The article can be accessed here.
In a nutshell, the MSDN article gives some background about NLU and explains how to improve LUIS (Microsoft Natural Language Understanding Service) intent classification. Here we will show how to apply the same techniques to Rasa NLU, which is an open source natural language understanding tool developed by Rasa (www.rasa.com), with functionality similar to LUIS.