A smart city generates massive amounts of data through CCTV cameras and various sensors installed throughout the urban landscape. Understanding and utilizing this city-scale data, along with managing various urban devices like traffic lights and drawbridges, is nearly impossible for humans. ATHENA empowers smart city operators to track and effectively respond to events happening in the city through a Large Multi-modal Model augmented with a variety of tools.
Leveraging deep learning-based tools such as video and audio understanding modules, ATHENA can comprehend and react to complex situations that are beyond the capabilities of humans and conventional LLMs, enabling faster and more accurate smart city operations than ever before.
Moreover, ATHENA integrates a powerful LLM chatbot that references the registered OpenAISpec on the server to understand the API structure. This capability allows the chatbot to extract which API is required and what parameters are needed from the user's natural language query. Consequently, ATHENA can flatten complex service workflows into a simple natural language interface, making it easier than ever to interact with and control smart city operations.
Up to 70% Time Reduction in Data Analysis City-scale Data-driven Decision Making End-to-end Emergency Handling System
