June 30, 2022

Assured and robust…or bust

Sheridan Hyland/LLNL

The consequences of a machine learning (ML) error that presents irrelevant advertisements to a group of social media users may seem relatively minor. However, this opacity, combined with the fact that ML systems are nascent and imperfect, makes trusting their accuracy difficult in mission-critical situations, such as recognizing life-or-death risks to military personnel or advancing materials science for the Lab’s stockpile stewardship mission, inertial confinement fusion experiments, radiation detectors, and advanced sensors. While opacity remains a challenge, LLNL's ML experts aim to provide assurances on performance and enable trust in ML technology through innovative validation and verification techniques. Read more in Science & Technology Review.