To answer this question, let’s understand what functionality chatbots currently provide.
Current chatbot implementation do well for handling fixed set of dialogs with the user, repetitive tasks and certain initial aspects of customer service tasks. Wherever there is a fixed set of processes and flows to automate, chatbot can be used to provide 24 * 7 support for any queries. If human expertise is used for answering basic set of questions where answers are readily available, it would be eventually be replaced.
But in real-life scenarios, most of the conversation usually doesn’t follow a fixed flow paradigm. But if the conversation moves from basic questions to questions which need further analysis, or the topic of conversation gets changed, you need a sophisticated chatbot implementation to take care of various conversation flows, identify the context switch, identify intents which your chatbot may not be aware of and create queries to find that information from your knowledge source. You are now moving from fixed set of flows to more dynamic flows which needs to be interpreted by your chatbot. Building such complex chatbot implementations requires sophisticated domain specific adoption using machine learning techniques and custom solutions. Current out of the box chatbot services fall short of building such chatbot implementations.
And even if you have all the data in the world at your disposal, infinite processing and computation power, using the current generation technology and research, you can never build a system that can compete with an expert human in the field. Taking even a 5 year horizon from now, I don’t think we can develop such a level of intelligent chatbots.
For instance, can chatbots or an assistant, help doctor to recommend cancer treatments accurately and consistently. The answer is No.
The information provided from chatbot can aid doctors to take a clue from the answer provided, it may be right or wrong. You can never certify this. The chatbot would always act as assistance to an expert person to get some job done. Ultimately, these systems are throwing a bunch of answers based on some probabilities. The answers are limited to what you have fed into the system, you can’t infer a new knowledge on the fly or can correlate information like a human expert to come to any conclusion.
While, there are research going on to use deep neural nets for conversation flows, we are still quite far away of building truly conversational interfaces which understands the nitty-gritty of language and domain. Also, the answers provided needed to be explainable and unless you have a way to backtrack on why a particular answer was provided, such deep neural systems can’t be used for use cases which requires auditability and explainability.
In short, enjoy the smart chatbots that gives a perception on being intelligent, but intelligence is a long way away.