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A team of researchers at Duke University is addressing issues of transparency when it comes to the deep learning methods of neural computer vision systems. A technique the team has introduced aims to help to understand potential errors and biases in the “thinking” of deep learning algorithms. The issue, known as the “black box” problem, describes the hidden reasoning within neural networks that is largely unknown, even, in some cases, to designers. Previous attempts to shed light on the thought processes behind such decisions have considered the actions following the learning stage itself, highlighting what the computer was “looking” at rather than its reasoning.
“The problem with deep…READ MORE