Microsoft Partners to Develop AI Disaster Prediction Tools
Big Partnership: Microsoft & US Dept. of Energy
|Source: DARPA AI|
Targets Include Extreme Weather, Natural Disaster Predictions
Microsoft and the US Department of Energy (DOE) are spearheading a new partnership to create artificial intelligence (AI) tools capable of predicting natural disasters like the global extreme weather events and historic California wildfires that we are experiencing from Climate Change. The AI partnership includes the Pentagon's Joint AI Center, the Pacific Northwest National Lab along with Microsoft and the US Department of Energy. The AI partnership was just announced.
Climate Change and Extreme Weather
One of the most exciting potential applications of AI is to forecast and predict Climate Change and extreme weather conditions, like the historic flooding in China, the record breaking back to back hurricanes hitting Louisiana and the blistering heat much of the world endured in the summer of 2020. Global weather services have satellites, computer modelling and tons of data on weather conditions, patterns and developing storms. Plugging all of that data into deep learning machine models (AI) will likely result in earlier and more accurate predictions of extreme weather, identify important climate change patterns and significant developments.
First Five Consortium
The DOE calls the new AI initiative the First Five Consortium. The name refers to the critical first, five minutes after a disaster strikes when first responders make critical decisions. The AI tools developed will help provide breakthrough information for that decision making. The DOE-Microsoft partnership will develop and deploy AI in four critical areas:
- Wildfire prediction and fire line containment - of great and growing importance in California, the Amazon and elsewhere
- Natural disasters like predicting Earthquakes and Volcanos
- Search and rescue such as spotting survivors in burning, collapsed buildings
- Damage assessment - knowing Arctic ice melt rates much sooner and predicting the consequences more quickly would be a critically important application.