Conversational AI is artificial intelligence that enables computers to understand, process, and produce human understandable language. In this article, we will learn about Conversational AI and its working. We will also understand the advantages and challenges associated with it.

What is Conversational AI?

Conversational AI is often used in the form of high-end chatbots. They differ from traditional chatbots based on simple software with limited functionality. Conversational chatbots provide different types of artificial intelligence for higher efficiency. The technology used in AI chatbots can also be used to develop voice assistants and virtual agents. The technology behind interactive AI platforms is still in its infancy but is evolving and expanding. Static chatbots typically appear on company websites and are limited to text interactions. In contrast, conversational AI can provide speech-based interactions.

Working of Conversational AI

Conversational AI combines natural language processing (NLP) and machine learning (ML) techniques with traditional interactive communication models such as chatbots. This combination is used to respond to the user, acting like a human-agent interaction. Static chatbots are policy-driven, and their conversations are based on predefined answers designed to direct users to specific information. While conversational AI models, use NLP to analyze and interpret user speech for meaning and ML to learn new information for future interactions.

Following are the steps for working of conversational AI model:

• User provides voice or text inputs to the conversational AI platform.
• If the input given by the user is text, conversational AI uses natural language understanding (NLU) to interpret the meanings of words given by the user.
• If the input generated is speech, speech-based AI uses automatic speech recognition (ASR) to identify the voice.
• After analysis, conversational AI uses natural language generation (sub-domain in NLP) to generate responses to customer input.
• Finally, Conversational AI analyzes user inputs and customer needs to provide better and more accurate answers in real-time.

Components of Conversational AI models

NLP technology is needed to analyze human speech or text, and ML algorithms are needed to synthesize and learn new data. Data and conversation design are two important elements of conversational AI. Major areas of NPL that are used in conversational AI are:

• NLU enables the machine or application to process speech data based on context, logic, syntax and semantics and ultimately determine the meaning of the user.
• Natural language generation (NLG) is the process of a machine generating text in a human language (also called a native language) based on any language. The purpose of NLG systems is to interpret information necessary for human intelligence.

Advantages of Conversational AI

1. Banking domain: Banks are using AI chatbots to handle complex requests that are difficult for traditional chatbots to do. When dealing with customers’ financial problems, it is especially important to eliminate human error and provide the correct answer or solution that will solve the problem.
2. IOT devices: Home appliances have the ability to interact with artificial intelligence through interactions like Amazon’s Alexa and Apple’s Siri. Conversational AI agents are also integrated into smart home devices.
3. Marketing and Retail: When customer service representatives are unavailable, AI-powered chatbots can meet customer needs, even on weekends. In the past, the only ways to interact with customers were call centers and personal visits. Today, customer support is no longer limited to business hours, as AI chatbots are available across many mediums and channels, including email and web.
4. Performance Monitoring: Conversational AI helps track performance by analyzing all customer interactions. This provides you with a lot of information as you improve your business process.
5. Increase Cost Efficiency: Hiring and managing a customer service center can be expensive. To provide better customer service, instead of hiring a large team of human agents, you can use conversational AI technology that works like a virtual agent or virtual assistant.

Challenges of Conversational AI

1. Difficulty in interpreting words and concepts: AI conversations can be influenced by slang, jargon, and regional language, which are examples of the evolution of human language.
2. Language translation: Many AI conversational models have been trained mostly in English and are unable to interact with non-English speakers in their native language. Multilingual proprietary chatbots are a service option for companies with international operations.
3. Data security: Companies that use AI chatbots to interact with customers must take necessary security measures to process and store the information sent.
4. Background noise: Background noise distorting the voice of the speaker can create challenge to conversational AI. It may create in difficulty in understanding the voice of the speaker thus AI will not be able to produce required responses.

Conclusion

Conversational AI uses NLP and machine learning to have human-like conversations with computers. Virtual assistants, chatbots, and more can understand context and intent and produce intelligent responses. This technology can be used in customer service to enhance communication, automate responses, and provide virtual agent in real-time.