In 1829, an event took place that unleashed a technological revolution. At the Rainhill Trials a group of steam locomotives squared off to determine which one could win a series of tests of speed, strength and reliability. The winning machine, Rocket, not only blew away its competition at the trials, it also set the direction for steam locomotive development for the following century. What does all this have to do with GPT-3, the transformer language model that OpenAI made available in a limited beta starting in June? Some reviewers have heralded GPT-3 as the first glimpse of artificial general intelligence, while others are calling it a massive lookup table.

Imagine you could build a system that can automatically reach out to users to collect feedback? This is possible today and this blog post will outline how to do it. In this post I will take you through how you build a chatbot using Rasa Open Source. The post was inspired by a series of great blog posts on this topic, but adds in a little of my own experience in doing the deployment. Rasa provides a framework that is understandable and intuitive, avoiding blackbox approaches and making it possible to get something up and running very quickly.

In 1829, an event took place that unleashed a technological revolution. At the Rainhill Trials a group of steam locomotives squared off to determine which one could win a series of tests of speed, strength and reliability. The winning machine, Rocket, not only blew away its competition at the trials, it also set the direction for steam locomotive development for the following century. What does all this have to do with GPT-3, the transformer language model that OpenAI made available in a limited beta starting in June? Some reviewers have heralded GPT-3 as the first glimpse of artificial general intelligence, while others are calling it a massive lookup table.