Building contextual AI assistants with machine learning and open source tools
AI assistants are getting a great deal of attention from the industry and research. However, the majority of assistants built to this day are still developed using a state machine and a set of rules. That doesnt scale in production. Tyler Dunn explores how to build AI assistants that go beyond FAQ interactions using machine learning and open source tools.
| Talk Title | Building contextual AI assistants with machine learning and open source tools |
| Speakers | Tyler Dunn (Rasa) |
| Conference | O’Reilly Artificial Intelligence Conference |
| Conf Tag | Put AI to Work |
| Location | London, United Kingdom |
| Date | October 15-17, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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. Tyler Dunn challenges the usual approach of chatbot development by introducing machine learning-based methods for dialogue management. You’ll learn the fundamentals of conversational AI, machine learning techniques behind natural language and dialogue management, and the basics of using Rasa Stack—an open source ML-based framework that empowers developers to build contextual assistants in-house.
