This blog post details findings from the Semantic Telemetry Project, analyzing user engagement with Microsoft Copilot. Key takeaways include the correlation between complex task engagement and continued tool use, the increasing complexity of tasks performed by novice users, and the importance of AI expertise matching user expertise for satisfaction.
This blog post introduces the Semantic Telemetry project at Microsoft Research, which uses a data science approach to analyze how people interact with AI systems, specifically focusing on Copilot in Bing usage. It discusses the complexity of human-AI interactions and how they differ from traditional search.
- Topics: Copilot in Bing chats were analyzed for topic categorization. Technology (21%) was the most common topic, followed by Entertainment (12.8%), Health (11%), and others. Within technology, programming and scripting were prominent subtopics.
- Platform Differences: Mobile users tend to use Copilot for personal tasks, while desktop users engage in more professional activities.
- Topics: Copilot in Bing chats were analyzed for topic categorization. Technology (21%) was the most common topic, followed by Entertainment (12.8%), Health (11%), and others. Within technology, programming and scripting were prominent subtopics.
- Platform Differences: Mobile users tend to use Copilot for personal tasks, while desktop users engage in more professional activities.
