Researchers at the universities of Delaware (UD) and Massachusetts-Amherst have developed a high-confidence approach to artificial intelligence-based models that incorporates uncertainty, error, physical laws, expert knowledge, and missing data into its calculations.
The model itself identifies data required to reduce errors, enabling a higher level of theory for generating more accurate data, further shrinking error boundaries on predictions and the area to explore.
UD's Joshua Lansford said, "Uncertainty is accounted for in the design of our model. Now it is no longer a deterministic model. It is a probabilistic one."
From University of Delaware
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