Episode 2185 - June 29 - Tiếng Anh - Phần 2 của 2 - Chatbot là gì - Vina Technology at AI time - Lê Quang Văn | Podcast - Nhac.vn

How chatbots work - Part 2 of 2

The earliest chatbots were essentially interactive FAQ programs, which relied on a limited set of common questions with pre-written answers. Unable to interpret natural language, these FAQs generally required users to select from simple keywords and phrases to move the conversation forward. Such rudimentary, traditional chatbots are unable to process complex questions, nor answer simple questions that haven’t been predicted by developers.

Over time, chatbot algorithms became capable of more complex rules-based programming and even natural language processing, enabling customer queries to be expressed in a conversational way. This gave rise to a new type of chatbot, contextually aware and armed with machine learning to continuously optimize its ability to correctly process and predict queries through exposure to more and more human language.

Modern AI chatbots now use natural language understanding (NLU) to discern the meaning of open-ended user input, overcoming anything from typos to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon and use conversational AI to formulate an appropriate response. These AI technologies leverage both machine learning and deep learning—different elements of AI, with some nuanced differences—to develop an increasingly granular knowledge base of questions and responses informed by user interactions. This sophistication, drawing upon recent advancements in large language models (LLMs), has led to increased customer satisfaction and more versatile chatbot applications.

The time it takes to build an AI chatbot can vary based the technology stack and development tools being used, the complexity of the chatbot, the desired features, data availability—and whether it needs to be integrated with other systems, databases or platforms. With a user-friendly, no-code/low-code platform AI chatbots can be built even faster.

Chatbots vs. AI chatbots vs. virtual agents

The terms chatbot, AI chatbot and virtual agent are often used interchangeably, which can cause confusion. While the technologies these terms refer to are closely related, subtle distinctions yield important differences in their respective capabilities.

Chatbot is the most inclusive, catch-all term. Any software simulating human conversation, whether powered by traditional, rigid decision tree-style menu navigation or cutting-edge conversational AI, is a chatbot. Chatbots can be found across nearly any communication channel, from phone trees to social media to specific apps and websites.

AI chatbots are chatbots that employ a variety of AI technologies, from machine learning—comprised of algorithms, features, and data sets—that optimize responses over time, to natural language processing (NLP) and natural language understanding (NLU) that accurately interpret user questions and match them to specific intents. Deep learning capabilities enable AI chatbots to become more accurate over time, which in turn enables humans to interact with AI chatbots in a more natural, free-flowing way without being misunderstood.

Virtual agents are a further evolution of AI chatbot software that not only use conversational AI to conduct dialogue and deep learning to self-improve over time, but often pair those AI technologies with robotic process automation (RPA) in a single interface to act directly upon the user’s intent without further human intervention

To help illustrate the distinctions, imagine that a user is curious about tomorrow’s weather. With a traditional chatbot, the user can use the specific phrase “tell me the weather forecast.” The chatbot says it will rain. With an AI chatbot, the user can ask, “What’s tomorrow’s weather lookin’ like?” The chatbot, correctly interpreting the question, says it will rain. With a virtual agent, the user can ask, “What’s tomorrow’s weather lookin’ like?”—and the virtual agent not only predicts tomorrow’s rain, but also