Computer conversation refers to all verbal or textual exchanges between a human and a computer system, such as a chatbot, virtual assistant, or conversational agent. Unlike simple human-machine interactions based on commands, it relies on artificial intelligence (AI) techniques to simulate natural discussion, understand context and intent, and dynamically adjust responses. This involves natural language understanding (NLU), dialogue management, and automatic response generation (NLG). Computer conversation is distinct from classic question-answer systems thanks to its ability to handle multi-turn dialogues, adapt to conversation history, and personalize exchanges.
Use cases and example applications
Computer conversations are used for customer support (website chatbots), voice assistants (Alexa, Siri, Google Assistant), automated booking or transaction management, education (virtual tutors), or healthcare (patient assistance, symptom triage). They automate repetitive tasks, provide 24/7 responses, and personalize user interactions.
Main software tools, libraries, frameworks
Key tools include Rasa (open source, Python), Dialogflow (Google), Microsoft Bot Framework, IBM Watson Assistant, Botpress, and open-source models from OpenAI (GPT), Meta (LLaMA), or Cohere. These platforms offer language understanding modules, dialogue management, and multi-channel integration.
Recent developments, evolutions, and trends
The increasing sophistication of large language models (LLMs) enables more natural, contextual, and personalized conversations. Voice modality integration, emotion management, and cultural adaptation enrich user experience. Trends include AI/human hybridization, exchange security, bias reduction, and use in professional and sensitive contexts (healthcare, banking, administration).