4 Challenges You Should Know About Before Building an AI Chatbot
What is an AI Chatbot?
“Chatbots will fundamentally revolutionize how computing is experienced by everybody”- Satya Nadella
Humans are intelligent. They can influence anything in this world to act on their command. A machine is no exception.
The Chatbot, also called as a conversational bot, interactive agent, conversational AI, talkbot or Chatterbot (That’s a good nickname!) is an advanced innovation of the human, by the human and for the human to get things done by interacting with the computers. The conversation could be either textual or auditory. If the chatbot is not able to answer the human queries, it would direct the conversation to its human counterpart.
It would involve a set of computer-simulated conversations where users can record their requirements to get the answers. Siri, Alexa, Sophia… they all belong to this vast community of Chatbots.
Do you know? There are over 100,000 Chatbots in Facebook. 80% of businesses are going to use Chatbots by the year 2020. Let’s dig deeper on how it works and what are the current challenges faced by the industry.
How does a Chatbot work?
When humans provide any inputs, the machine should be in a position to understand it. There are different parameters involved in understanding the humans- Text classifiers where we separate the words and sentences to form a meaningful intent, Algorithms to determine the way the Chatbot operates, Natural Language Understanding (NLU) to analyse text from scratch to extract more metadata including concepts, keywords, categories, entities and Artificial Neural Networks (ANN). These are some sophisticated technologies behind AI Chatbots.
It is similar to any other application, containing an app layer, a database layer and different APIs to make a call to other external administrations. A Bot would be incapable of comprehending what the user demands on its own. With the help of Machine learning, we can train the bots based on the past information made available to them. The bots would not be able to differentiate when the user asks “Where is my train” and “The train did not arrive”. The context is the same. The user has to know when the train would arrive at the station.
To ensure that the bot answers to this question smartly, API layers can come into play. The developers would utilize the discussion logs based on past conversations and what the users have asked.
By making different ML algorithms, models and tools, developers coordinate with the client inquires to reply with the right answer.
Training a Chatbot
It happens a lot faster than human education.
Customer service representatives = Manual instruction
Chatbots = Conversation logs
Chatbots learn diligently through various conversation logs that the developers feed to them. The bots make use of Pattern matches to group the text. The basis of it is the Artificial Intelligence Markup Language (AIML), a standard structured model involving the patterns.
4 Challenges faced by Chatbots
Context integration
Responding sensibly and smartly are the golden millets of an efficient Chatbot. Users face the challenge where they have to integrate the context into the Chatbot. There are also so many other stuff like location, contextual data, date, time or other details that you must integrate in the Chatbot. Sometimes, it becomes a tough nut to crack.
Coherent responses
Coherence, or in other words, as the Oxford dictionary defines, the quality of being logical and consistent, is important for Chatbots. The Chatbot must understand that the logic behind questions like “Did you have your breakfast?” and “Did you eat?” are the same. Only smart Chatbots can handle this well.
Performance assessment
If the chatbot can perform the task properly, we can deem that it is performing well. Human judgement is subjective. It becomes quite difficult to make judgements on whether the chatbot is functioning well or not. We can bring some performance metrics into a picture. But that would not solve the problem statement since there are unwavering human emotions involved in analysing the performance of your Chatbot.
Read intention training
Relevancy is the key to impress the Chatbot user. Sometimes, understanding the intention of the user becomes harder. The Chatbot has to understand why the user is asking the specific question. It has to get to know the intention behind it.
If you are willing to utilise the best potentials of the Chatbot, come to us. We can help you entirely in the AI Chatbot process from conceptualization to maintenance. We are nominated as the most promising product outsourcing company in India. Soon, we would be in top metropolitans in Bay area, european countries and Singapore.
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