What is Conversational AI and How Does it Work?

Introduction

The world, as we know today, is evolving at such a rapid pace that ten years from now, we would most certainly be living in a different society. Everything from computers to advanced quantum technology would see phenomenal growth in computing power and experience—one such area where most changes are expected in the field of Artificial Intelligence.

With the growing number of businesses and scientists working day and night to bring out the most of this domain to be used for future consumers, the faster we are racing towards that goal of universal AI. It includes a plethora of fields such as machine translation, computer vision, Natural Language Understanding, Natural Language Generation.

The global NLP market is stated to hit the market value of about $29 Billion by the year 2026. Also, there is an extremely innovative field in the field of AI itself, and that is conversational AI. 

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What is Conversational AI? 

Scientists and researchers have been working tirelessly on what seems to be an extremely innovative but difficult area to crack. This includes work on Natural Language Understanding (NLU) and Natural Language Processing (NLP).

NLP is a subfield of data science and describes how computers and human languages interact with each other. In contrast, NLU deals with the structuring of raw unfinished data into understandable computer format. 

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Chatbots and AI

Although Chatbots nearly correspond to NLP and hence conversational AI, they are quite different from each other. Chatbots follow a decade-old rule-based approach which includes training a bot on the pre-planned tactics of using common phrases to develop scripted conversations.

Read: How to make chatbot in Python?

Development in these fields

New developments in these fields are relatively complex and extremely promising. These include parts such as NLU that includes intent and named entity recognition. These modules that focus on linguistics such as spell-checking, management of modules keeping in mind local and global scenarios, and also API’s integration.

All chatbots require a lot of patience and hard work to work as intended. 

But the recent advancement in the technologies associated with Conversation AI has really opened the market for these types of resources and nearly all companies are now working on upgrading or developing their NLP bots for the future. Their uses are exceptional, which includes.

Smart Devices

A lot of devices that have the inbuilt AI and Internet of Things integration have popped up all around the globe with many players sharing the pie, such as Google launched its assistant and Amazon Alexa which are also the frontrunners in this competition. With every three out of four products launched from a single company Amazon, it is the market leader at least in the United States of America. In China, the competition is more fierce with tens of hundreds of companies trying to tap that massive audience. 

Now there are other smart devices in the market other than smart speakers that leverage the NLP technology such as toys, auto devices, smart home appliances like Philips hue, smart door, smart heating solutions, and smartwatches.

Virtual Assistants

These work on the same concept we discussed before but can tap a larger audience. There are many virtual assistants in the market, including Amazon Alexa, Google Assistant, Apple Siri, Microsoft Cortana, which are based on the user’s intents and run commands accordingly.

They come pre-installed in many devices we use every day, including Smart Phones, Smartwatches, and Smart TVs. In the year 2018, the number of smart device shipments increased to an all-time high of 78 million units around the globe. These devices have the power to become a bridge between consumer and resource providers.

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How Does it Work?

The entire process starts with interaction, and here the interaction is between a person and a smart device such as a smartphone, speakers, phones, etc. In these scenarios, the questions raised by the person may include information about services, products, or commands to start or play music. And hence the first part of any NLP translation is treatment processing. 

After that, dialogue processing platforms take the front line. It includes an Automatic Speech Recognizer, a text to a speech synthesiser, and other systems to integrate all the processing. Assistants such as Google Assistant and Amazon Alexa use a person’s voice to authenticate him/her. Hence, additional functionalities have to be added to cater to these use cases. 

The case is now transferred to Dialog Manager which searches the Dialog State Database and finds similar databases with the same intent. Using that context, the user’s query along with this captured context is passed into the Natural Language Processing module where the actual intent of the user is verified.

Then again this flow is passed to Dialog Manager which in turn defines how to respond to the client and hence chooses the way to respond for example use a box, screen, or dialogue box that is in line with the requirements of the user and solves the problem effectively. 

To generate the text of the audio reply, the business or technical logic is examined and directed to the system interface. Word matchings and macro replacement coordinating functions are then used to generate text replies. The newly generated reply is saved in the database to be assessed for the next interaction, and the response is sent to the client. 

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Conclusion 

The concept of NLP and Conversational AI has been in talks in the market for about a decade but, the real need for NLP scientists have been increasing exponentially, as all the new and old companies are trying to get the best talent available. These NLP scientists are offered exponential packages and the best services available. 

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