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 one interface to act directly upon the user’s intent without further human intervention. Deep learning capabilities allow AI chatbots to become more accurate over time, which in turns allows humans to interact with AI chatbots in a more natural, free-flowing way without being misunderstood. Chatbots can be found across any nearly any communication channel, from phone trees to social media to specific apps and websites.ĪI chatbots are chatbots that employ a variety of AI technologies, from machine learning that optimize responses over time to natural language processing (NLP) and natural language understanding (NLU) that accurately interprets user questions and matches them to specific intents. Any software simulating human conversation, whether powered by traditional, rigid decision tree-style menu navigation or cutting edge conversational AI, is a chatbot. While the technologies these terms refer to are closely related, subtle distinctions yield important differences in their respective capabilities.Ĭhatbot is the most inclusive, catch-all term.
The terms chatbot, AI chatbotand virtual agentare often used interchangeably, which can cause confusion. This sophistication, drawing upon recent advancements in large language models (LLMs), has led to increased customer satisfaction and more versatile chatbot applications. 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. 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. 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. 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. Over time, chatbot algorithms became capable of more complex rules-based programming and even natural language processing, allowing customer queries to be expressed in a conversational way. Such rudimentary traditional chatbots are unable to process complex questions, nor answer simple questions that haven’t predicted by developers. Unable to interpret natural language, they generally required users to select from simple keywords and phrases to move the conversation forward. The earliest chatbots were essentially interactive FAQ programs, programmed to reply to a limited set of common questions with pre-written answers.
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