Introduction to Conversational AI
Conversational Artificial Intelligence (or Conversational AI) is a set of technologies underpinning automated messaging and speech-enabled systems that enable human-like interactions between computers and humans.
Conversational AI refers to any computer that can be spoken to and is most commonly encountered today via chatbots and voice assistants.
What is the definition of Conversational AI?
Conversational AI (artificial intelligence) refers to systems that can “speak” to people, such as virtual assistants or chatbots (e.g., answer questions).
Conversational AI apps are frequently utilised in customer support. They are available on websites, online retailers, and social media platforms. AI technology may significantly improve the speed and efficiency with which consumer questions are answered and routed.
How does conversational AI work?
Conversational AI is essentially powered by two functionalities. The first of these is machine learning. Simply said, machine learning means that the technology “learns” and improves as it is utilised. It gathers data from its exchanges. It then utilises that knowledge to develop itself over time.
As a consequence, your system will perform better six months after you add it to your website, and even better a year later.
The second is known as natural language processingv or NLP. This is the method through which artificial intelligence comprehends language. It can progress to natural language generation after learning to identify words and phrases. This is how it communicates with your consumers.
For instance, if a consumer approaches you on social media inquiring when an item will be sent, the conversational AI chatbot will know how to answer. This is because it has experience addressing similar queries and recognises which words perform best in response to shipping questions.
Although the theory may appear difficult, conversational AI chatbots provide a highly easy client experience.
What are the Components of Conversational AI
Conversational AI refers to any machine that may be spoken to. A chatbot on a website or social messaging app, a voice assistant or speech-enabled device, or any other interactive messaging interface might be used. Through discussion, people can ask questions, acquire views or suggestions, complete transactions, get help, or achieve other context-dependent goals.
Conversational AI brings together five technology components
- Automatic Speech Recognition (ASR)
- Natural Language Understanding (NLU)
- Dialogue Management
- Natural Language Generation (NLG)
- Text to Speech (TTS)
Automatic Speech Recognition (ASR) :
Speech Recognition is the computer-based processing and recognition of human voice (Automatic Speech Recognition). It is the process of translating a voice signal to a series of words using computer software and an algorithm. It converts voice to text in conversational AI.
Natural Language Understanding (NLU) :
It is a subset of Natural Language Processing (NLP) that entails converting human language into a machine-readable format. NLU is concerned with a machine’s capacity to comprehend human language. NLU is the process of rearranging unstructured data so that machines can “understand” and evaluate it.
Dialogue Management (DM):
The key component of Conversational AI is Dialogue Management, which receives input from the ASR and NLU systems, interacts with external knowledge sources, and generates messages to be sent to the user. The dialogue management method consists of two major tasks:
Modelling dialogue: Keeping track of where the conversation is at.
Making decisions regarding the next system action using dialogue control.
In general, it directs the flow of communication between the agent and the user.
Natural Language Generation (NLG):
It is a subset of the Natural Language Process (NLP), which is defined as the “process of creating meaningful phrases and sentences in natural language form.” It develops narratives that describe, summarise, or explain structured data supplied in a human-like manner.
Text-to-Speech (TTS) :
Conversational AI has reached its conclusion. The text answer generated by the NLU and NLG stages is converted to natural-sounding speech using a text-to-speech (TTS) system. It operates in the inverse of the ASR system. Below diagram depicts TTS architecture :
Conversational AI may be the future of numerous day-to-day living activities as technologies and processing capacity advance. Because of the speedy responses it gives, conversational AI will improve consumer happiness.
Conversational AI use cases
When people think of conversational artificial intelligence, they often think of online chatbots and voice assistants for their customer support services and omnichannel deployment. Most conversational AI apps include comprehensive analytics in the backend software, which aids in providing human-like conversational interactions.
Experts believe existing conversational AI applications to be poor AI since they are focused on executing a relatively restricted set of activities. Strong AI, which is still a theoretical idea, focuses on a human-like awareness that can tackle a wide range of activities and issues.
Despite its restricted emphasis, conversation AI is a very valuable technology for organisations, assisting them in becoming more profitable. While an AI chatbot is the most common kind of conversational AI, there are several more applications throughout the company. Here are a few examples:
- Online customer support: Throughout the customer journey, chatbots are replacing human representatives. They respond to commonly asked questions (FAQs) regarding issues such as shipping or give individualised advice, such as cross-selling items or recommending sizes for users, altering the way we think about client involvement across websites and social media platforms. Messaging bots on e-commerce sites with virtual agents, messaging applications like Slack and Facebook Messenger, and jobs often performed by virtual assistants and voice assistants are examples.
- Companies may become more accessible by lowering entrance barriers, especially for people who use assistive technology. Text-to-speech dictation and language translation are common Conversations AI functions for these groups.
- HR procedures: Conversational AI may be used to optimise several human resources operations, such as employee training, onboarding processes, and updating employee information.
- Health care: Conversational AI has the potential to make health care services more accessible and cheap for patients while also enhancing operational efficiency and streamlining administrative processes such as claim processing.
- Internet of things (IoT) devices: Nearly every household now has at least one IoT gadget, ranging from Alexa speakers to smartwatches to cell phones. To engage with end users, these gadgets employ automatic voice recognition. Amazon Alexa, Apple Siri, and Google Home are all popular apps.
- Computer software: Conversational AI simplifies many office chores, such as search autocomplete and spell check when you search anything on Google.
While most AI chatbots and applications still have minimal problem-solving abilities, they can save time and money on recurring customer support engagements, freeing up staff resources for more engaged client interactions. Overall, conversational AI apps have been successful in simulating human conversational interactions, resulting in increased levels of consumer happiness.
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Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.
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