Communication is an integral part of human life. It would be fair enough to say that human beings cannot coexist without communicating with another. But Artificial intelligence and machine learning have got so intelligent. You can talk to your mobile phones, computers or even home speakers.
The AI software collects a huge amount of data that is being used to learn and be smarter in the speech. Siri, Google Assistant, Alexa are examples of cleverly designed interactive artificial consciousness. Towards the beginning, this artificial intelligence was only able to assign menial tasks like setting a reminder, making a call, etc. But now they are smart enough to tell you a joke or sustain a conversation based on previously asked questions.
Chatbots are AI-based simulations that act as a virtual being in a conversation, just like a normal human being but exist with life from coding and data analytics. Though human beings and computers use the same language, let’s say English. The nature of speech has colloquial terms in sentences so it is very hard for an AI to understand and comprehend what the user is asking of it.
Unlike human beings, AI cannot read emotions so thereby fails to understand what the user may infer due to the dynamic nature of speech. This is called Natural Language Understanding (NLU). BERT Framework, ALBERT, Ro BERT, DistilBERT have made huge leaps in tackling this NLU Model. Taking these approaches, they are getting substantially closer to simplifying the complex constructs in pinpointing the primary essential requirement.
Advanced tasks related to communication can be given to AI so that frequently asked questions can be answered with ease 24/7. The biggest problem addressed in the past few decades is when a spelling error or grammatical error occurs it was difficult to point out the error. However, it is possible to program and application which would display the mistakes.
The English vocabulary has words that represent multiple meanings, a single word may have numerous definitions according to the situation so it is very hard to interpret or understand for chatbots. There is something called metadata, metadata is a series of data that systematically describes given information within the main data.
For a Chatbot, it will be hard to perform at its maximum capabilities in a diverse working environment where new expressions may merge frequently. So, the systems should be so advanced in terms of machine learning to keep up with this data and retained information in real-time. Given the huge heaps of data, artificial intelligence is getting smarter day by day. Still, chatbots are non-intuitive and less lifelike.
The machine learning should incorporate intuitive semantic intelligence and algorithms to better understand the user and keep learning the intricacies of human language. In the future, we will be able to have a human level of interaction with chatbots. This is desired as it makes conversations and task assigning close to effortless and convenient.