What is RASA? — the open-source AI for building conversational chatbots.
As machine learning and artificial intelligence continue to develop amazing products which seemed impossible, machines gain consciousness too and learn just like humans do.
One such big implication is the open-source RASA.
What is RASA?
Rasa is an open-source machine learning framework used to create conversational chatbots. It is used to automate the text and voice-based assistants.
Rasa is capable of enhancing interactions with customers and hence helps businesses. Rasa can be used to target some very crucial aspects of businesses like recruiting where the AI can be used to build chatbots to deliver a great interview process by imparting a great candidate experience. For businesses, the customers which come to their website , it is a great experience to conversate with an AI. The list could go on about implications.
So, now you understand what Rasa is, let’s dive deeper into detail.
Advantages of Rasa:
- Integration- being open-source and with the ease of building chatbots, Rasa can be integrated into systems easily and automate stuff.
- Customization — who said we cannot add our touch? Rasa is flexible and can be modified to fit needs.
- Interactive learning- Rasa is trained to learn on its own. It doesn’t matter if businesses don’t’ know how to train the AI, it learns on its own as you talk to it. Talk about AI being so cool!
- Not your regular static machine- Rasa is not a machine but a conversational AI bot which is the replica of the human interaction mechanism. You could expect the bot to be extremely interactive learning from the chats.
Elements of Rasa
Rasa works on three main elements-
- Natural Language Understanding(NLU)
- Natural Language Generation (NLG)
- Dialogue Management
NLU
NLU converts text to vectors to identify the intention of the sentence and converts the incoming text to tokenizers with the extraction of entities. How? A part-of-speech tagger or POS-tagger is used to tag each work with a part of speech like noun, verb, etc. then the Chunker chunks into groups the nouns with words related to them.
For example- you ask rasa “what will be the weather tomorrow?”. The first process vectorizes it and finds out intent which is “request weather”.
The next step uses tokenizer, POS tagger, and Chunker to finally extract the entity “tomorrow”
So we get — “ request weather” and “tomorrow” which is pretty much what our question’s motive was.
Natural language generation(NLG)
NLG is a subset of AI and is powerful owing to its feature to take input of non-linguistic format and convert it into a human-understandable form!.
Dialogue management
As the name suggests it is used to manage so that when the extracted data from intent and entity is received, it doesn’t mess up.
Conclusion
With chatbots becoming extremely important, Rasa is the new convention in the world of conversational AI. It can be used easily and for small volumes is free. Machine learning and artificial intelligence are the new technologies helping the world become developed and they will continue to create amazing products!
One such product is AskLua (you probably saw that coming). AskLua is a service used to conduct automated interviews with the help of AI!. AskLua makes screening easier and much more faster owing to its friendly AI bot and ensures a fair and efficient interview process.
Take a free trial to see the implication of AI come to life.!
