Defense Advanced Research Projects Agency (DARPA) officials recently announced the winners of its AI for Critical Mineral Assessment Competition, noting the effort increases and better secures critical mineral supply.
In conjunction with the U.S. Geological Survey (USGS), the competition’s goal was to crowdsource ideas to reduce the time required to complete parts of assessments using AI and machine learning to automate key processes.
Canada-based company Uncharted garnered the top honors for their solution, while American company Jataware received second place, and Team Ptolemy, which included team members from the Massachusetts Institute of Technology, University of Arizona, and Pennsylvania State University, received third place.
“Critical minerals are essential to the national security supply chain, and as such, the agency is approaching the need from multiple angles,” DARPA Director Stefanie Tompkins said. “The USGS collaboration puts an emphasis on identifying existing domestic resources. Other DARPA programs are evaluating the feasibility of recovering rare earth elements from e-waste and bioengineering methods to purify rare earth elements.”
USGS Director David Applegate said the competition has served as a valuable opportunity for the USGS to collaborate with leading minds in AI to improve the approach to critical mineral assessments.
“It has already led to incredible time savings in how we prepare data in a machine-readable format,” Applegate said. “Furthermore, these machine-learning models have implications beyond mineral resources into other fields that use map data, including geologic mapping, ecological mapping of species diversity, and many other application areas.”