Justina Petraityte

Justina has a background in Econometrics and Data Analytics. Her curiosity for Data Science and human behaviour analytics has taken her to many places and industries – over the past three years she has been doing Data Science work across video gaming, fintech, insurance industries. Her interest in chatbots, natural language processing and open source has led her to Rasa, a Berlin-based conversational AI startup where she works as a Head of Developer Relations focusing on improving developer experience in using open source software for conversational AI.

Building contextual AI assistants with OSS tools

ML, AI, & Data, Python & Libraries, Intermediate
8/18/2019 | 4:00 PM-4:45 PM | Robertson

Description

AI assistants are getting a great deal of attention from the industry as well as the research. However, the majority of assistants built to this day are still developed using a state machine and a set of rules. That doesn’t scale in production. In this talk, you will learn how to build AI assistants that go beyond FAQ interactions using machine learning and open source tools.

Abstract

When built well, AI assistants provide great strategic business value and are fun to interact with. However, the majority of assistants built to this day are developed using just a set of rules and don’t go beyond simple FAQ interactions. This doesn’t scale in production and provides a rather disappointing user experience.

In this interactive talk, we will challenge the usual approach of chatbot development by introducing machine learning-based methods for dialogue management. You will learn the fundamentals of conversational AI, as well as machine learning techniques behind natural language and dialogue management. Finally, you will learn the basics of using Rasa Stack – an open source ML-based framework which empowers developers to build contextual assistants in-house.