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AI Chatbots

Cornell University_011122C
[Cornell University]

- Overview

A chatbot is defined as a conversational application that aids in customer service, engagement, and support by replacing or augmenting human support agents with artificial intelligence (AI) and other automated technologies that can communicate with end users through chat.

Chatbots are computer programs that replicate and analyze human conversations (oral or written), enabling humans to communicate with electronic devices as if they were talking to a live agent. Chatbots range from simple programs that respond to a single instance to advanced virtual assistants that can learn and improve as data is collected and processed to provide a higher level of personalization.

The rise of chatbots in recent years is rooted in the accelerated pace of digital transformation. Businesses are increasingly migrating from traditional modes of communication to digital channels in order to interact and transact with customers. Businesses use artificial intelligence (AI) to drive new efficiencies in a variety of customer-facing functions, and chatbots are one of the top applications of AI in business.


- Applications of Chatbots 

Chatbots are used in dialog systems for various purposes, including customer service, request routing, or information gathering. While some chatbot applications use extensive word classification processes, natural language processors, and sophisticated AI, others simply scan for general keywords and generate responses using common phrases taken from relevant libraries or databases.

Most chatbots are accessed online via website pop-ups or virtual assistants. They can be grouped into usage categories including: business (e-commerce via chat), education, entertainment, finance, health, news, and productivity. Companies can use chatbots to extend, personalize experiences and proactively deliver services, a key differentiator in the digital age.

Businesses can benefit from chatbots as they can improve performance and save costs while providing convenience to customers and providing additional services to internal employees, customers and partners. They enable businesses to quickly answer various questions among stakeholders while reducing the need for human involvement.

Chatbots enable companies to interact with an almost endless number of customers in a personalized way, and scale up or down based on current needs. Even if a chatbot is deployed to millions of customers simultaneously, it can provide an almost “human touch” to everyone.


- How Does a Chatbot Work?

A chatbot is a software application used to conduct online chat conversations via text or text-to-speech, rather than providing a direct connection with a live human agent. Designed to convincingly mimic how humans behave as conversational partners, chatbot systems often require constant tweaking and testing, and many systems in production still cannot converse adequately, and none of them would pass the standard Turing test. 

To understand how chatbots work, we must first consider the three core mechanisms that drive the technology. Three mechanisms that require your attention are rules-based processes, AI-driven decision making, and field agent intervention. Depending on the mechanics of the chatbot, its functionality will vary slightly.


- Rule-Based Chatbots

Rule-based chatbots are also known as decision tree bots. As the name suggests, they use a set of defined rules. These rules are the basis for the types of questions that the chatbot is familiar with and can provide solutions to. Like a flowchart, rule-based chatbots map out conversations. They do this to predict what a customer might ask and how the chatbot should respond.

Rules-based chatbots can use very simple or complex rules. However, they cannot answer any questions beyond the defined rules. These chatbots do not learn through interaction. Also, they only execute and process the scenarios you trained them on. 

While the conversational flow of rules-based bots is less flexible, these guardrails are also an advantage. You can better guarantee the experience they will provide, whereas chatbots that rely on machine learning are less predictable. Some other advantages of rule-based chatbots are:

  • Usually faster (cheaper) to train
  • Integrate easily with legacy systems
  • Simplify handover to human agents
  • Highly responsible and safe
  • Can include interactive elements and media
  • Not limited to text interaction


- AI Chatbots

In contrast, an AI chatbot using machine learning understands the context and intent of a question before formulating a response. These chatbots use natural language responses to generate their own answers to more complex questions. The more you use and train these bots, the more they learn and the better they work with users.

AI chatbots are chatbots trained to hold human-like conversations using a process called natural language processing (NLP). With NLP, AI chatbots are able to interpret human language as they are written, which allows them to operate more or less on their own.

In other words, AI chatbot software can understand languages beyond preprogrammed commands and provide responses based on available data. This allows site visitors to lead the conversation, expressing their intent in their own words.

What's more, AI chatbots are constantly learning from their conversations - so, over time, they can adjust their responses to different patterns and new situations. This means they can be applied for a wide range of purposes, such as analyzing how customers feel or predicting what website visitors are looking for on your website.


- How an AI Chatbot Works

At a basic level, an AI chatbot receives input data, which it interprets and turns into relevant output. So, if a website visitor asks a question, an AI chatbot will analyze their intent and other factors like tone and emotion, and then try to provide the best answer. 

To do this, AI chatbots need access to large amounts of conversational data. That's why an AI chatbot must go through a training period in which programmers teach it how to understand the context of a person's words. It is this understanding that enables chatbots to answer complex queries in a natural, conversational manner.



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