In materials discovery, experimental data can be noisy or uncertain because furnace outputs may be mislabeled, labeling may be stochastic, machines may disagree, and telemetry may not reveal the actual temperature.

“issues of say decision making under uncertainty so when you're doing optimization against math there's a high precision to it you're not really dealing with you know kind of variance or uncertainties or you know aberrant measurements whereas that's very key to to our process like for example when we are doing materials discovery loop things don't come off the out of the furnace labeled right even the labeling process can be stochastic and noisy”

“and sometimes you'll have aberrations between different machines,”

“maybe we'll have insufficient telemetry so we thought we ran at this temperature but really the temperature was some delta away and an intelligence that can take in this noisy data and make intelligent decisions like a scientist would is a sort of a different set of reasoning strategies.”