Building AI assistants that scale using machine learning and open source tools

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.

Justina Petraityte offers a hands-on walk-through of developing intelligent AI assistants without any predefined rules, based entirely on machine learning and using only the open source tools Rasa NLU and Rasa Core. You’ll learn the fundamentals of conversational AI and best practices for developing AI assistants that scale and learn from real conversational data.

Outline:

Stage 1: Natural language understanding
You’ll start with language understanding, bootstrapping from very little annotated training data. In this stage, you’ll also tackle the challenges of going beyond pretrained word vectors for NLU and enabling the assistant to capture more than one intention per user input.

Stage 2: Dialogue management
You’ll use machine learning to build the AI assistant’s ability to handle increasingly complex multiturn dialogues based on the actual conversational data. You’ll also enable the assistant to actually complete user-requested tasks by connecting the assistant to external APIs and using the knowledge of the outside world to steer the conversation.

Stage 3: Closing the feedback loop
In the last section, you’ll close the feedback loop by improving the performance of the assistant by using the interactive learning and a real-time user feedback.