Chatbots has a great relevance in the business world. As brands focus on promoting personalized user experiences, more intelligent chatbots are being built to engage users and improve their brand image.
We have heard a lot about chatbots. But, it is rare to find a live intelligent chat bot. This kind of chat bot is also called Artificial Intelligence (AI) chat bot. These chatbots bring a sense of human touch and are not humans at all. The thing that matters in AI chatbots is the intelligence quotient (IQ) of the chat bot. The IQ aspect gives power to the AI chat bot to learn from the user conversations and handle any scenario of a conversation with ease.
So, does the chat bot really know what the user actually wants?
A chat bot is considered to be smart when it is well aware of the user needs. For example; let us consider the case of a live chat bot helping a user book a room in a hotel. The user is prompted to give out the date the user has in mind to book the room. The query 'Are premium rooms available' comes from the user and here, the role of chat bot comes into play. Now the AI chat bot must understand this specific user need to provide a relevant answer in this situation.
An intelligent chat bot will understand and learn the language variations to give a convincing answer. In the future, there may come a time where the bots will have AI which will know what we want before we even ask it.
An AI chat bot is based on the human ability of self-learning and gaining information efficiently. Thus, it is important to make the chat bot sense natural language expressions. For this, there are tools like IBM Watson that incorporate natural language capability into a chat bot.
And, is the smart chat bot a good learner?
If a chat bot is smart, its basic character would be learning. But, an intelligent chat bot is one that learns user conversations at every kind of situation to improve its performance progressively.
Machine learning (ML) algorithms and human supervisors enable the learning of the chat bot. ML techniques like reinforcement learning supervised, and unsupervised techniques can be leveraged to ensure that the AI chat bot becomes a good learner.
The ability to learn is a key factor in creation of an intelligent chat bot. With neural networks and deep learning, chatbots can become good learners. Learning is important to ensure that the chat bot recognizes data patterns it receives and responds to user requests in the most appropriate way.
A chat bot is built to serve the users' requests. It is crucial for a chat bot to plan how to perform the task requested by a user. Chatbots respond to each request by learning from the past conversations with the users.
The progress of a chat bot from one user request to another require better planning. In case of complex requests, chat bots must identify the action sequence to fulfill the primary goal of the user. Planning is a sequence of actions which form conversations. It includes acknowledgment, questions, and information. The chatbots improve more after every conversation with users.
How do we determine if the chat bot is intelligent?
The AI chat bot comes with the ability to fix a goal and work independently to achieve that goal. This sounds easier. But, this is not the case because identifying the goal for a specific situation is quite a hurdle.
The chatbot adheres to a three-step process for realizing the goal. It is the sense-think-act cycle that can define the intelligence of a chatbot. An AI chatbot goes through this cycle to make progress towards pre-defined goal independently.
Ability to sense
For an AI chat bot, sensing the surroundings is a prerequisite to obtain the information required to perform a task. The bot finds it easier to listen to what user says than listening to what is conveyed by the user. It is a challenge to infuse sensing power into the robot as there is a great need to integrate the robots with most modern sensors.
Sharp to think
The chatbot must convert information received from a user into an understandable format and store it into their knowledge base. An AI chatbot makes decision by leveraging pre-existing knowledge and one that it acquires continuously. Based on this decision, the chatbot takes action to achieve pre-defined goals. Using neural networks in ML makes the chatbot think and take actions depending on the request placed by the user.
The knowledge base influences the learning capability of the chat bot from its past conversations with users. The knowledge base helps in learning faster, identifying relevant information and providing a response that is relevant.
The information gathered guides the chatbot to decide on the relevant action to perform. Taking decision is more about what the chatbot has to reply to a user's request. Predictive analytics using ML can make the AI chatbot plan ahead about queries that would come from the user. This makes the chatbot more intelligent.
Quick to act
The chat bot knows the action it has to take to respond to a user. The chat bot must type out the reply to a specific query raised by the user. Typing out a sentence is relatively easy for a chat bot when compared to responding through its audio or video skills. For audio or a video chat bot, responding to the user through a suitable action becomes difficult in the way that it has to sound like a human.
It is not the time to wander in the business world. The businesses progress with the advances in the technology. Thus, the business platforms heavily depend on chatbots and an intelligent chatbot is a must-have for any businesses today.