Rasa: Build Your Own AI Chatbot with Joey Faulkner – Episode 134

Summary

With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.

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Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
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  • Your host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?
  • What are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?
  • How does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?
  • How is Rasa NLU implemented and how has the design evolved?
  • What are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?
  • What are some of the biggest challenges in gathering and curating useful training data?
  • What is involved in supporting multiple languages for an application using Rasa?
  • What are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?
  • What would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?
  • On the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?
  • What are some of the most interesting or unexpected uses of the Rasa tools that you have seen?
  • What do you have planned for the future of Rasa?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA