The “Smart Communities, Smart Responders (SCSR) AI for IoT Prize Competition” asks participants to deliver an AI system to help first responders leverage the data coming from IoT devices, smart buildings, and other public data streams. The Texas A&M University (TAMU) and US Ignite will run this new program with $1.2 million in funding provided by NIST’s Public Safety Innovation Accelerator Program – Artificial Intelligence for IoT Information (PSAIP – AI3) cooperative agreement.
Like NIST’s CHARIoT Challenge, also led by US Ignite, the AI for IoT Prize Competition aims to use AI to aggregate and present environmental data in real-time in ways that make the information more useful and actionable for first responders. The CHARIoT Challenge offered teams more than $1M in prizes to develop cutting-edge AR and IoT data tools. Now, the AI for IoT Prize Competition seeks to spark innovative development of real-time data visualizations to help first responders solve complex challenges quickly in the field.
Real-Time Sensors @ Disaster City
A functional AI learning system requires a lot of IoT data. TAMU and US Ignite plan to place real-time sensors in key locations in Disaster City to collect real-world data. Disaster City is a world-renowned urban search and rescue training campus in Texas. It hosts more than 20,000 first responders annually.
Developers and innovators participating in the AI for IoT Prize Competition will need to demonstrate the accuracy and scalability of an AI system. To advance in the competition (and subsequently win) the participant teams need to deliver a working system with end-to-end functionality and integrated operations to be useful to public safety professionals. Solutions will be put to the test in a live exercise.
While AI and IoT data collection are cool all on their own, the AI for IoT Prize Competition will have an even broader impact. Via this competition, TAMU and US Ignite envision establishing:
- Technical standards and best practices of real‐time data integration and visualization,
- Optimal AI/ML models to predict first responder actions across a wide range of scenarios, and
- User experience scenarios that enable first responders to act on simulation models, sensors, and mission‐driven predictions.
Successful attainment of these outcomes can revolutionize the way that first responders communicate and perform. Increased network connectivity makes the development of AI and ML technologies viable, and these technologies have the potential to radically improve threat response and ultimately save lives.