Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, enabling you to quickly and easily build sophisticated, natural language, conversational bots (“chatbots”). Speech recognition and natural language understanding are some of the most challenging problems to solve in computer science, requiring sophisticated deep learning algorithms to be trained on massive amounts of data and infrastructure. Amazon Lex democratizes these deep learning technologies by putting the power of Amazon Alexa within reach of all developers. Harnessing these technologies, Amazon Lex enables you to define entirely new categories of products made possible through conversational interfaces. As a fully managed service, Amazon Lex scales automatically, so you don’t need to worry about managing infrastructure. With Amazon Lex, you pay only for what you use. There are no upfront commitments or minimum fees.
By collecting over 20 years of messaging transcript data and feeding it to their AI-powered chatbot, LivePerson can automate almost every industry’s messaging and integrate with most messaging channels like your website, mobile app, Apple Business Chat, text messaging, Google Rich Business messaging, Line, Facebook Messenger, WhatsApp, and Google AdLingo.
Rasa NLU & Core allows for more human-like dialogue, trained using interactive, and supervised machine learning. Rasa Core picks up patterns from real conversations and also takes the history and external context of a conversation into account. The Rasa NLU engine is an open source tool for intent classification and entity extraction, and offers natural language understanding for bots and assistants. It features the capability to turn natural language into structured data. It is modeled as a set of high level APIs for building your own language parser using existing NLP and ML libraries. Many developers world-wide are using it…

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