Imagine an AI that not only understands language but also has a sense of where things are in a city. That's the idea behind CityGPT, a project from Tsinghua University. Researchers found that typical large language models (LLMs) struggle with real-world tasks involving location. LLMs might be great at generating stories or code, but ask them about navigating a city, and they're often lost. This is because they're trained primarily on text from the internet, lacking the 'grounding' of physical experience. CityGPT aims to change this by creating what's like a mental map for the AI. Researchers built a special dataset called CityInstruction. It mimics how we experience a city – walking down streets, seeing landmarks, understanding where different neighborhoods are. They fed this data, along with general knowledge, to various LLMs like ChatGLM3-6B, Qwen1.5, and the LLama3 series. The team also designed CityEval, a benchmark to test the AI's urban understanding. This benchmark includes tasks like identifying city landmarks, understanding neighborhood functions, spatial reasoning (figuring out directions and distances), and even predicting human movement. The results? Smaller LLMs trained with CityInstruction performed surprisingly well, often competing with much larger, more general AI models. This shows the power of focused training. CityGPT opens exciting doors for AI applications. Think of improved navigation apps that understand your needs better, smarter urban planning tools, or even AI assistants that can truly understand and respond to requests in a real-world context. The journey to create genuinely intelligent AI requires not just book smarts but also street smarts, and CityGPT is a significant step in that direction.