Electrifying global vehicle fleets will require vast new troves of metals like cobalt and copper that may be tough to find without help from big data.
Antoine Caté shares his view on how machine learning will influence geologists during exploration stages in this interview by National Geographic.
He believes machine learning models have the potential to “dramatically improve” success rates in exploration—in part due to their ability to detect patterns among datasets with more variables than the human brain can process. Still, he cautions such tools are only as good as the information that’s fed into them: If an algorithm is built with substandard data, it will be ineffective at best and at worst lead prospectors down false paths.
AI also doesn’t eliminate the need for human ingenuity. “These tools are amazing for diagnostics,” Caté says. “But at the end of the day you still need a skilled person to take the information and make something out of it.”
Reach out to Antoine for further insights on this topic.
Subscribe to National Geographic to read the full article by Jonathan W. Rosen.
