What makes a Chatbot Intelligent?

It turns out — They aren’t as difficult to make as You think

Fifteen years ago, when nano-technology came into limelight, many thought that it can potentially change the world. But fast forward now, we can hardly notice any difference except for hardware products.

Likewise, when chatbots came into limelight, many thought that it’s actually game-changer in customer interactions. But only a few companies thrived in this bot evolution. So what makes them tick? How they made their chatbots intelligent?

“Only the paranoid survive.” — Andy Grove

How to make a chatbot intelligent?

The hardest part is not building the bot, but maintaining it and making sure it stays stable, useful and usable. For that, you need a tool that is capable of the following: —

Collecting analytics about each session, so that you can notice user problems early.

Providing an interface for improving your training dataset using new data from actual user interactions.

Evaluating the model after each update.(If using NLP/ML/AI)

Interfacing with a program using written language seems like an odd step. It’s never been the most efficient way of doing something, and it requires very advanced technology to accurately understand what people are trying to say, in whatever slang, shorthand, or bad spelling/grammar they use. So how do we make it understand what we say?

Use of NLP/ML/AI

In a typical support interaction, the customer describes a symptom. Then, via conversation with the customer, the support agent will eventually confirm the underlying problem that caused this symptom. After identifying the problem, the support agent can hopefully prevent a solution to that problem. In many cases, the language used to describe the symptom, problem, and solution do not overlap.

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To define a context or understanding the meaning of the conversation, the use of NLP is a necessity. when we mention NLP we must think of a state machine, goals to be achieved, dialogue and discourse theory, world models and pragmatics. It is not just syntax and lexical semantics. Especially when a simple information retrieval bot (aka query/response) is not enough for the task at hand.

The chatbot should be a learning ninja. In order to build a bot which is smart, then ‘learning’ is one of the most important aspects in improving its performance all the time through conversations. ML techniques like reinforcement learning help in creating a good learning model if we have huge data.

In creating a character, the majority of the chatbots follow the retrieval based model which works on the concept of predefined responses. The bot picks the queries from the user and pushes the appropriate response from the storage repository based on the context of the query.

Challenges in building an intelligent Bot

Requirement of knowledge representation

Siri, Cortana, Viv and the like, have access to rich knowledge graphs and believed that this is an important aspect of their strength. It is not easy to accumulate such data and to put them to work for your application. The hard part of the intelligent platform

Context Integration

Chatbots were seen as a way to assist users with self-help, sales, and support issues. Everyone hyping them was trying to sell them as solutions to perform those operations. To do this, they had to guess what in the world you’re talking about given the user’s input and the knowledge base available to the bot. This was and is still a huge problem given how free-form language works.

The problem with chatbots and NLP is that, if the bot is too simple, it isn’t that useful. But the more powerful the bot gets, the more complex the NLP can become, and quickly.

Conclusion

To be frank, a large proportion of messages will not be understood at all to the machine and there is little we can do about it. What we can do is anticipate user frustration and alleviate the situation by offering a little help and managing expectations. Also, you don’t really have to learn NLP/ML for your need. There are several tools like wit to help you build what your users need.

Nowadays, the chatbot must be user-centric and witty to make it ‘sound intelligent’. For that, it needs to be smart and knowledgeable.

What are your challenges in building an intelligent bot? I would love to know your thoughts in comments.