Intelligent chatbot?
As brands tend towards promoting personalized experiences, more and more intelligent chatbots are being built to engage users and improve brand image and capital.
That said it is a rarity to find a live intelligent chatbot, aka AI chatbot. What we know is that chatbot brings a human touch, that is it needs to be really intelligent. The crux is not the chatbot rather it is the intelligence quotient of the chatbot that can bring the human essence.
It is the intelligence that gives power to the AI chatbot to learn from previous conversations and handle any situation. As chatbots move into complex territories, maintaining the intelligence quotient becomes increasingly difficult.
DOES THE CHATBOT KNOW WHAT THE USER WANTS?
A chatbot is intelligent enough when it becomes aware of user needs. For instance, let us consider the case of a live chatbot helping a user book a room in a hotel. The user is prompted to give out the date to book the room. It’s good until the query ‘Are premium rooms available?’ comes from the user. Now the AI chatbot must understand this user need and provide a relevant answer. An intelligent chatbot will understand and learn the language nuances to give a convincing answer.
To reduce design complexity, it is important to ignore proactive user queries by keeping it local. But an AI chat bot is based on human capability of self-learning and gaining information efficiently. Thus it’s imperative to make the chatbot sense natural language utterances. There are tools like Watson IBM, Api.ai, and Wit.ai to incorporate natural language capability into a chat bot.
IS THE CHATBOT A LEARNING CHAMPION?
An intelligent chatbot is one that learns conversations all the time to improve its performance. The modules in a chatbot including user modeling modules and the natural language module can only perform better by learning continuously. Machine learning(ML) algorithms and human supervisors enable the learning of the chatbot. ML techniques like reinforcement learning supervised, and unsupervised techniques can be leveraged to ensure the AI chatbot becomes a good learner. With neural networks and deep learning, chatbots can become good learners. Learning is paramount to ensure that the chatbot recognizes patterns in data it receives and responds to user requests in the most apt manner.
DOES THE CHATBOT KNOW HOW TO MEET USER REQUESTS?
It is crucial for the chatbot to plan how to perform the task requested by a user. Chatbot responds to each user request by learning from the previous conversation so as to what the request is. When it comes to complex tasks, chatbots must identify the action sequence to do the primary goal of the user. Planning is a sequence of actions which form conversations and include acknowledgment, questions, and information. As it learns from conversations with the users it will continue growing smarter and smarter with every conversation.
HOW DO WE KNOW IF A CHATBOT IS INTELLIGENT?
The AI chatbot comes with the ability to fix a goal and work to achieve it. This is easier said than done where identifying the goal for a specific situation is a hurdle in itself to cross. The chatbot adheres to a three-step process for realizing the goal. It is the sense-think-act cycle that can define the IQ of a chatbot. An AI chatbot goes through this cycle to make progress towards pre-defined goals autonomously.
Take the case of Siri and Google Now. Their intelligence is due to the pre defined knowledge stored internally. This knowledge base helps in learning faster, identifying relevant information and providing a relevant response. Taking decision is more about what the chatbot has to reply to a user’s query. Predictive analytics using machine learning can make the AI chatbot plan ahead about request that would come from the user.
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WHAT DO YOU WANT THE CHATBOT TO DO?
Infusing the IQ into your chatbot also depends on what you want your chatbot to do. You can either make the chatbot help the user or collect information from the user. A helper chatbot is considered to be smarter than the chatbot that serves as a collector because it interprets what the user is saying and performs the task for the user.
What are the characteristics that define the helper chatbot?
The helper chatbot is recognized by its natural language processing(NLP) and understanding power. Collector chatbots, in turn, leads the conversation with the user. They adhere to pre-defined question models and are not smart enough to respond when a user raises a query. The drive to increase the intelligent quotient of the collector chatbots depends on the intelligent platform where they are built to reside.
WHAT IS THE MODEL FOR THE INTELLIGENT CHATBOT?
A chatbot on the retrieval-based model works on the concept of predefined responses. The chatbot picks appropriate responses from the repository stacked which is based on the context and query raised by the user. Generative models built using machine translation techniques can generate new responses instantly. It enable longer conversations where the chatbot deals with several user queries. Though deep learning techniques are leveraged for building both these models, generative models seem to have an upper hand over its counterpart.
DO WE FORESEE CHALLENGES IN BUILDING INTELLIGENT CHATBOT?
From making the chatbot context-aware to building the personality of the chatbot, there are challenges involved in making the chatbot intelligent.
Context integration
Sensible responses are the holy grail of the chatbots. Integrating context into the chatbot is the first challenge to conquer. In integrating sensible responses, both the physical context as well as linguistic context must be integrated. For incorporating linguistic context, conversations are embedded into a vector, which becomes a challenging objective to achieve. While integrating contextual data, location, time, date or details about users and other such data must be integrated with the chatbot.
Coherent responses
The chatbot must be powered to answer consistently to inputs that are semantically similar. For instance, an intelligent chatbot must provide the same answer to queries like ‘Where are you from’ and ‘where do you reside’. Though it looks straightforward, incorporating coherence into the model is more of a challenge. The secret is to train the chatbot to produce semantically consistent answers.
Model assessment
How is the chatbot performing?
The answer to this query lies in measuring whether the chatbot performs the task that it has been built for. Since the chatbot is built on an open domain model, it becomes very difficult to judge whether the chatbot is performing its task. Moreover, researchers have found that some of the metrics used in this case cannot be compared to human judgment.
Read intention
In some cases, reading intention becomes a challenge. Take generative systems for instance. They provide generic responses for many of user inputs. The ability to produce relevant responses depends on how the chatbot is trained. Without being trained to meet specific intentions, generative systems fail to provide the diversity required to handle specific inputs.
PLAN TO USE NLP AND MACHINE LEARNING?
Another factor that deserves attention is the plan to leverage NLP or machine learning for building the chatbot. In the case of natural language processing, it is about finding answers by parsing language into intent, entities, agents, actions, and contexts. With NLP reckoned as the driving force, NLP platforms like WIT, API, and LUIS can be leveraged to build an intelligent chatbot.
While you plan to leverage machine learning to create your own NLP, you must decide upon the model prior to building the intelligent chatbot. It is important to weigh generative and retrieval-based model, open and closed domains to create the intelligent chatbot that you have in mind.
For now, the chatbot imperative is to meet user-centric tasks. For that to happen, the chatbot must be smart and well versed. The chat to build a smart chatbot gets chattier when significant elements surrounding the building process make an entry. As we look into the future, intelligent chatbots will be built to rule the world of connections.
