The digital engineering team at the Department of Energy's (DOE) Idaho National Laboratory (INL) aims to expand the capability of general artificial intelligence by using algorithms and auditable data.
Chris Ritter, leader of the digital and software engineering group at INL, said on a Federal News Network segment that his office works to curate data and transform it into a format that could present scale-up advantages.
With data being required to train algorithms, he discussed a type of algorithm called “explainable or transparent artificial intelligence.”
"So it’s completely auditable. And we can apply some penalize regression techniques to those areas, and you can make that a more novel technique,” added Ritter.
He also talked about research efforts to address complex energy problems by applying machine learning.
"Machine learning is about having pre-programmed devices, which can conduct analysis on their own," the INL official said.