A White-Box Testing Model For Deep Learning Systems

How do you find errors in a system that exists in a black box whose contents are a mystery even to experts? That is one of the challenges of perfecting self-driving cars and other deep learning systems that are based on artificial neural networks--known as deep neural networks--modeled after the human brain. Inside these systems, a web of neurons enables a machine to process data with a nonlinear approach and, essentially, to teach itself to analyze information through what is known as training data. When an input is presented to a "trained" system--like an image of a typical two-lane highway shown to a self-driving car platform--the system recognizes it by running an analysis through its complex logic system. This process largely occurs inside a black box and is not fully understood by anyone, including a system's creators. Any errors also occur inside the black box and are thus difficult to identify and fix.