Chatbots became widely popular around 2016 with the growth of chat platforms like Slack and voice interfaces such as Amazon Alexa. As chatbots came into use, so did the infrastructure that enabled chatbots. NLP APIs and complete chatbot frameworks came out to make it easier for people to build chatbots.

The first suite of chatbot frameworks were largely built around rule-based state machine systems. These systems work well for a narrow set of use cases, but fall over when it comes to chatbot models that are more complex. Rasa was started in 2015, amidst the chatbot fever. 

Since then, Rasa has developed a system that allows a chatbot developer to train their bot through a system called interactive learning. With interactive learning, I can deploy my bot, spend some time talking to it, and give that bot labeled feedback on its interactions with me. Rasa has open source tools for natural language understanding, dialogue management, and other components needed by a chatbot developer.

Tom Bocklisch works at Rasa, and he joins the show to give some background on the field of chatbots and how Rasa has evolved over time.

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