The conversational interaction pattern is one of the seven core patterns for Artificial Intelligence projects. Common implementations of this pattern include chatbots, voice assistants, natural language understanding tools, natural language generation systems, sentiment and mood analysis based on conversational interaction, content summarization, content intelligence, and even gesture and handwriting analysis, which is a form of human communication.
The biggest challenge in conversational interaction is not only understanding how to convert audio waveforms that form parts of spoken speech into text, or to take individual text words and generate an understanding of a sentence, but how to take those spoken or written words and generate some machine understanding of what the communicator’s intent is. Even more so, the challenge is to take multiple conversational interactions and connect them together in a cohesive manner. While most chatbots are able to respond to individual phrases or sentences, the bigger challenge is properly understanding and processing longer form back-and-forth, “multi-turn” conversations.
Introduced at the Amazon Re:MARS 2019 event in Las Vegas this past week, Amazon is using a combination of recurrent neural networks (RNNs) and other aspects of machine learning and conversational technology to enable Alexa skills developers to build multi-turn, multi-skill interactions. Rather than requiring users to query multiple skills and engage in many, redundant interactions, Alexa Conversations enables a conversational thread across multiple intent and skill interactions to tie together those threads into a single coherent conversation.
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