Welcome to the world of intelligent chatbots, your companion and conversation agents which would make your life smarter. A leading research paper even said by 2020, the average person will have more conversations with bots than with their spouse. So be ready to embrace this new life in a year from now.<\/p>\n
Ok hold on, have you ever tried telling Siri or Google to \u201cFind restaurants which doesn\u2019t serve pizza\u201d. At least they are both consistent in some way, they gave the same answer – suggesting restaurants which serve pizza.<\/p>\n
Ok how about Sofia, the first citizen humanoid robot, which is making its way to every media event and giving interviews and boost of human like conversations. Well the truth is far from reality, it is providing an illusion of understanding conversation, but as you start asking intelligent questions you would realize it can answer fixed set of questions.<\/p>\n
Well by now, you should be able to clear out the noise from reality. So, should I invest in chatbots with all these limitations? Yes, any technology would have its limitations, but you need to be aware of what you can build now, what to avoid and how to work around the limitations. I have seen many companies trying to build sophisticated chatbots using products from leading chatbot vendors and cloud offerings, spending million on dollars and hitting a roadblock.<\/p>\n
If you go by what is being projected and start building it out, you would soon realize these limitations one way or the other. The problem is that most of the vendors claim it\u2019s very easy to build a chatbot, but in reality, all of these techniques fall short when it comes to building a true conversational agent.<\/p>\n
With current implementations of chatbot, we are probably at the first generation of AI chatbots which are more or less scripted and giving answers to pointed questions. What I mean by scripted is that it is trained to understand general vocabulary, entities, the metaphor, synonyms etc. The chatbot uses fixed set of flows to understand the context. For domain specific use cases, additional training is required, and you need to train on specific domain terminology and relationship between the words. While, there are research going on using deep neural nets, we are still quite far away from building a true conversational chatbot which understands the nitty-gritty of language and domain.<\/p>\n
For instance, if you are building a shopping advisor chatbot, the term \u201cblack and white dress\u201d implies \u201cblack and white\u201d as color and dress as category. You might expect the color \u201cblack and white\u201d is fairly generic and should be easily identified by the AI system, but that\u2019s not really the case.<\/p>\n
Based on my experience on building a sophisticated shopping personalized advisor, none of the AI NLP implementation fitted the requirements. A simple scenario is these 3 sets of sentences – \u201cblack and white dress\u201d, \u201cand black dress\u201d and \u201cblue jeans and white shirt\u201d. In all these 3 examples, the use of word \u201cand\u201d means different meaning. In the first case, its represents a combined color \u201cblack and white\u201d, in second instance \u201cand\u201d represent a brand and in third instance two queries joined by a conjunction (i.e. and). Even with required training, a generalizing model was not possible with any of the available solutions. These are just one of the many examples I am highlighting.Imagine the complexity when dealing with medical literature.<\/p>\n