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Thursday, February 25, 2021

Example of Question Answering Application: Jarvis

Question Answering Applications 

Developing Question a Question Answer Application with NVIDIA Jarvis   By James Sohn | February 25, 2021  Tags: AI/Deep Learning, BERT, cloud computing, featured,

There is a high chance that you have asked your smart speaker a question like, “How tall is Mount Everest?” If you did, it probably said, “Mount Everest is 29,032 feet above sea level.” Have you ever wondered how it found an answer for you?

Question answering (QA) is loosely defined as a system consisting of information retrieval (IR) and natural language processing (NLP), which is concerned with answering questions posed by humans in a natural language. If you are not familiar with information retrieval, it is a technique to obtain relevant information to a query, from a pool of resources, webpages, or documents in the database, for example. The easiest way to understand the concept is the search engine that you use daily. 

You then need an NLP system to find an answer within the IR system that is relevant to the query. Although I just listed what you need for building a QA system, it is not a trivial task to build IR and NLP from scratch. Here’s how NVIDIA Jarvis makes it easy to develop a QA system.

Jarvis overview

NVIDIA Jarvis is a fully accelerated application framework for building multimodal conversational AI services that use an end-to-end deep learning pipeline. The Jarvis framework includes optimized services for speech, vision, and natural language understanding (NLU) tasks. In addition to providing several pretrained models for the entire pipeline of your conversational AI service, Javis is also architected for deployment at scale. In this post, I look closely into the QA function of Jarvis and how you can create your own QA application with it.  ... " 

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