It's the Alexa of the U.S. nuclear weapons world.
Weapons scientists and engineers at Los Alamos National Laboratory will soon have an automated research assistant to help make digitized records easier to find, the lab said.
The system is called Titan on the Red.
"@LosAlamosNatLab will soon have an automated research assistant to help search millions of records of #nuclear secrets with #artificialintelligence and #machinelearning on its 'red network,'" the National Nuclear Security Administration tweeted.
Artificial intelligence is a tool that can help digitize, catalogue and search collections, Mott Linn, National Security Research Center chief librarian, said in a statement.
“After completing a successful six-month test run withunclassified materials, we’re confident artificial intelligence/machine learning (AI/ML) will save the lab a lot of time and even more money when it comes to our research efforts,” he said. “Although AI/ML is a relatively new advancement, it’s proven. It will really be a game-changer for the way the NSRC operates, and more importantly, the way we help the lab meet its national security mission,” Linn said.
The goal of Titan on the Red is to extract metadata from the lab’s various digital data repositories, such as the Online Vault, PDMLink, shared drives and SharePoint sites, the lab said.
“One of the greatest assets at LANL is the information that we have generated in over 75 years of nuclear weapons work,” Charlie Nakhleh, the associate laboratory director for Weapons Physics, said in a statement. “This is what distinguishes us from any other weapons laboratory in existence. The Titan on the Red system will make this valuable information discoverable.”
The collections contain information on nuclear weapons modeling and simulation, weapons designs and pit production, the lab said.
“This advancement is really the only solution to making the lab’s vast collections searchable to our researchers,” Nanette Mayfield, leader of the NSRC’s Digital Collections team, said in a statement. “Investing in AI/ML saves countless manpower hours and money, while directly contributing to the lab’s mission success.”
