For us, educating developers on conversational AI is as important as building great tools and software. Today, we are extremely excited to announce our biggest investment in education so far — the Rasa Masterclass.
The Rasa Masterclass is a weekly series of videos which will air on our Youtube channel every Thursday. We will cover the entire process of developing a contextual AI assistant — from just an idea, all the way to deploying an application in production. Each episode will contain some theoretical background to better understand the specific topic and will also include a practical implementation. …
TL;DR
Platforms like Google Assistant makes it easy to build custom voice assistants. But what if you wanted to build and assistant that runs locally and ensures the privacy of your data? You can do it using open source Rasa, Mozilla DeepSpeech and Mozilla TTS tools. Check out this tutorial to find out how.
With platforms like Google Assistant and Alexa becoming more and more popular, voice-first assistants are destined to be the next big thing for customer interactions across various industries. However, unless you use hosted of-the-shelf solutions, development of voice assistants come with a whole new set of…
At Rasa, we are constantly looking for ways to make it easier for developers to build great conversational AI. Developing a conversational AI assistant is not an easy task, and improving it over time using real conversations that don’t always go right is a whole new challenge. That’s why we developed a brand new product — Rasa X! Read more why we built it here.
Rasa X is a tool designed to make it easier to deploy and improve Rasa-powered assistants by learning from real conversations.
Rasa X is packed with new UI features which allow you to:
- View and…
The Rasa open source community is growing faster than ever. Today we are excited to announce a new community initiative — we’re introducing the Rasa Contributor Program!
What is the Rasa Contributor Program about?
Community is at the core of Rasa. Therefore, it’s important for us to create a space where people with different backgrounds and skills can find ways to get involved. The Rasa Contributor Program is designed to inspire our community members to contribute to open source and to make sure that their efforts and achievements are recognized within the community.
Rasa Contributor Program stages
The Rasa Contributor Program consists of three stages, each based on different levels of…
Training data is an essential part of every machine learning model. It is no different when it comes to conversational AI. We designed Rasa so that instead of writing rules, your assistant could observe what real conversations look like, learn from them and use that knowledge to manage the dialogue.
Modelling dialogue is not an easy task due to the natural complexity of human language. This calls for the mindful design of conversational data to optimize what patterns your assistant learns. In Rasa terms, this conversational data is called Rasa stories.
We believe that customizing ML models is crucial for building successful AI assistants. The open source Rasa provides you with a strong foundation for building good NLU models for intent classification and entity extraction, but if you have ever wanted to enhance existing Rasa NLU models with your own custom components (sentiment analyzer, spell checker, character-level tokenizer, byte-pair encoder, etc), we put a lot of work into making Rasa NLU modular so that you can do this. Want to learn how to implement it? You came to the right place! …
Desde el lanzamiento en 2016, Google Assistant se ha convertido rápidamente en una voz familiar en los hogares de las personas. Desde la reproducción de tu canción favorita hasta el control de las luces, el Asistente de Google se encarga de todo a través de interacciones de voz simples. Pero ¿qué hay de manejar conversaciones más complejas? Si has usado el Asistente de Google, probablemente hayas notado que cada solicitud debe comenzar con la frase mágica “Hola, Google” y que la mayoría de las conversaciones con el asistente no van más allá de una sola solicitud.
Un asistente contextual que va más allá de las simples interacciones de estilo de preguntas frecuentes requiere algo más que un algoritmo y una oración. Un asistente de conversación debe haber recopilado los detalles importantes necesarios para responder las preguntas de los usuarios en el contexto adecuado. De lo contrario, no se conseguirá un resultado positivo. Bastante simple, y conocido como slot filling. Pero, ¿cómo reúnes y defines los detalles que son importantes antes de tomar acción o proporcionar una respuesta?
Este proceso es fácil con nuestra última novedad de FormPolicy. Esta es una característica nueva que implementa el slot…
A contextual assistant that goes beyond simple FAQ-style interactions requires more than just an algorithm and a prayer. A conversational assistant needs to have collected important details needed to answer user questions in the right context. Otherwise, no happy path. Simple enough, and known as slot filling. But how do you gather and define the details that matter before taking action, or providing a response?
Slot filling is made easy with our new addition of FormPolicy. This is a fresh feature that implements slot filling in an easy and effective way. How? FormPolicy allows you to cover all the happy…
Justina Petraityte
Data Scientist, Developer Advocate @RasaHQ
