What is Conversational AI?

Conversational AI is the set of technologies behind automated messaging and speech-enabled applications that offer human-like interactions between computers and humans. By recognizing a user's speech or text patterns, predicting their intent, and responding with an adaptive, automated script, conversational AI platforms can create truly natural, human-like interactions.

While chatbots have gained popularity in recent years, conversational AI solutions can be offered over both text and voice modalities through various channels and devices. The best conversational AI offers an end result that is indistinguishable from what could have been delivered by a human.

The working principle of conversational AI is quite simple. Just like humans, conversational AI follows a basic process to communicate. It starts with taking input, understanding it, and then responding accordingly.

To achieve this, conversational AI relies on natural language processing (NLP) and machine learning (ML). NLP is responsible for analyzing and understanding the user input. This is done by breaking down the input into small pieces and understanding the intent behind it.

Once the input is analyzed, ML algorithms are used to generate a response. The response is then fine-tuned using reinforcement learning algorithms to make it more accurate over time.

ASR converts human speech into text that can be read and understood by a computer.

Natural language processing uses machine learning to analyze language and is the method currently used in conversational AI.

Before machine learning, linguistic analysis evolved from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will further advance conversational AI's natural language processing capabilities.

Machine learning is a sub-field of artificial intelligence, made up of a set of algorithms that get better with experience. As more data is inputted, the machine gets better at recognizing patterns and uses this to make predictions.

NLU is the ability of a computer to understand human language. It involves understanding the intent of a user's utterance and extracting information from it.

NLG is the ability of a computer to generate human-like language. It involves taking information from a computer and turning it into human-like sentences.

Dialogue management is the ability of a conversational AI to manage a conversation. It involves keeping track of the conversation, understanding the context, and responding in a way that is relevant to the conversation.

Reinforcement learning is a type of machine learning that helps a conversational AI to improve its responses over time. It involves giving the AI positive reinforcement when it responds correctly and negative reinforcement when it responds incorrectly. This helps the AI to learn from its mistakes and improve its responses.

Conversational AI is a more advanced form of chatbot that uses natural language processing (NLP) and dialog management to create a more human-like interaction.

Chatbots are basic answer and response machines, also called bots, where you must type the exact keyword required to receive the appropriate response. There are even not considered Conversational AI, because they are not using NLP, dialog management, and ML.

Conversational AI systems range from simple AI chatbots to more complex virtual assistants and can be used for a variety of purposes, including customer service, conversational commerce, and business process automation.

At the most basic level, conversational AI applications can be programmed with varying levels of complexity resulting in dramatically different end products.

Customer service chatbots are conversational AI applications that are used to automate customer service conversations. These AI chatbots can be used to answer common customer questions, such as product information and pricing, returns and exchanges policy, shipping information, assist with purchase decisions, and more.

Business process automation conversational AI applications are used to automate repetitive tasks within a business. These chatbots can be used to schedule appointments, place orders, track inventory, and more.

Virtual assistants are conversational AI applications that are used to provide human-like interaction. Virtual assistants can be used for a variety of purposes, including customer service, conversational commerce, and business process automation.

Conversational AI can help improve traditional voice assistants and virtual agents.

Virtual customer assistants are conversational AI applications that are used to provide customer service at scale. These chatbots can be used to answer common customer questions, such as product information and pricing, returns and exchanges policy, shipping information, and more.

The main challenges in conversational AI are related to the technology itself. As conversational AI applications become more advanced, the challenges will be related to more human factors, such as constantly changing communication (languages, dialects, accents, emoji, slang, and many more). Security and privacy concerns, when conversational AI is dealing with sensitive personal information and discovery and adoption by the general population.

Conversational AI can help in the industries below:

Yes, Siri is an example of conversational AI. Siri is a virtual assistant that uses natural language processing to answer questions and perform tasks. The more Siri answers questions, the more it understands through machine learning. Instead of giving scripted responses, Siri answers as a real person would based on what it has learned.