State emergency management officials and Arizona State University are investing in the future of disaster response.
A $1.9 million research grant given by the Arizona Board of Regents to ASU hopefully will develop Artificial Intelligence (AI) software that can help the state prepare for an emergency.
Gabe Lavine is the Arizona director of the Division of Emergency Management. He says during a crisis, data and information come in at a rapid speed.
"Information sharing and information management is one of the most important things we do as emergency managers," Lavine said.
This is where he says artificial intelligence can help his team. AI can dissect data and run calculations much quicker than humans, helping them get resources, and determine what chain reaction issues may arise from an emergency.
"AI is a tool, just like someone who wears glasses, it enhances our natural human abilities," Lavine said.
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On the ASU side, Professor Paulo Shakarian is already working on building the software with his colleagues and some select students.
"This is operational software to help people in the state make mission-critical decisions," Shakarian said.
A wildfire could lead to a flood, which could then cause a power outage. Shakarian says one of the hardest parts of emergency management is knowing where exactly to position resources.
He says this AI project could use incoming data to identify a chain reaction event before it occurs, getting resources to areas and people who need them as quickly as possible.
"The effect of this is saving lives, and saving property," Shakarian said. "That's what we think success will look like long term."
Lavine adds this project won't replace any people or lead to job losses. He says it will help complete his team, as emergency management is also facing staff shortages felt across the public safety sector.
"It's replacing the people I can't hire, or don't have the overhead for," Lavine said. "What I'm hoping to achieve is we are making more informed decisions that serve the community in the most effective and efficient way possible."
Shakarian says the project is supposed to take three years, and the software will be implemented in phases every six months.
