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AI model that detects the users language and translates between languages to make the bot multilingual, allowing it to chat with users in different languages.
Three different AI models that evaluate the sentiment for each response to understand the mood of the users for managing the conversation accordingly or empathizing with them.
Retrieval Based: Helper bot that collects information on behalf of the Master Bot via the Information Extractor or Integration Services.
It can also predict the next best action, product, service, promotion etc… Retrieval based bots might be domain specific and/or task specific and you could have several of them chained together.
Yet another model performs facial image or video analysis to read facial expressions and determine user emotions.
We will then study the proposed reference architecture which goes a step further towards identifying the required components, their integration and functionalities to implement this framework.
While legacy speech recognition systems understood what people say, today’s sophisticated natural language processing systems understand what people mean and want to do.
Both speech recognition and natural language understanding rely on large volume of training data, domain-specific knowledge and user information.
One model performs text analytics to identify sentiment in chat responses or voice responses by converting voice to text.
Other model performs tone analysis of user’s voice to identify voice patterns which determine emotions in the user’s voice as they speak with the bot.