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Conventional Artificial Intelligence nowadays is hardly that much interactive which it is used to be. it is transactional at best. The reason behind this is that the two major conversational AI solutions: Text-based and voice-based are still yet to deliver is that the techniques for the efficient and effective human-machine conversations are still evolving as opposed to what nowadays the latest technology made us believe.
Human-machine conversations comprise of some of the natural language understanding which is an understanding what the user must say. Natural language generation is just all about formulating an on-topic response and reasonable to the user. Moreover, response generation is not just simply a product of analyzing and collecting various types of data.
Natural Language Processing, of which NLU is a subset, is now different process because the tasks which is much more regarding the linguistics need to include the lots of variation in the human psychology, linguistic diversities, and cultures. Moreover, some of the conversational experiences today are either the broad or shallow or sometimes very narrow but deep.
From the last couple of years, many of the organizations and conversational Artificial Intelligence solution developers, have been so much busy developing the chatbots of the primary two kinds. The first is the enterprise chatbots that are merely built to solve enterprise use cases such as the lead generation, customer support, etc. the second category which lies in the direct to consumer bots, chatbots that reach the consumer directly for some of the specific applications. Moreover, most enterprise bots nowadays available are automated versions and are also unable to hold some of the conversations behind the fee of the interactive dialogues.
For now, bots can continue to help us with the low-level tasks, automated, repetitive queries, as cogs in a large much more complex system.
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