Common Assumptions on Machine Learning Malfunctions Could be Wrong

Deep neural networks are one of the most fundamental aspects of artificial intelligence (AI), as they are used to process images and data through mathematical modeling. They are responsible for some of the greatest advancements in the field, but they also malfunction in various ways. These malfunctions can have either a small to non-existent impact, such as a simple misidentification, to a more dramatic and deadly one, such as a self-driving malfunction. New research coming out of the University of Houston suggests that our common assumptions on these malfunctions may be wrong, which could help evaluate the reliability of the networks in the future. The paper was published in Nature Machine Intelligence in November.