MIT researchers can now track AI’s decisions back to single neurons

AI researchers had a breakthrough when they became able to practically replicate in their machines what we believe to be one of the most basic functions of the human brain: thought is generated by the combined activity of clusters of connected neurons. They’re now left in the same position as the neuroscientists who first proposed that idea: On the outside looking in, wondering just how millions of tiny components contribute to a greater whole. But computer scientists might understand their machines before we understand ourselves. New research from MIT offers clues to how artificial neural networks process information—and points to a possible method for interpreting why they might make one decision over another. That could help us more easily figure out, for example, why a self-driving car swerved off the road after perceiving a certain object, or investigate exactly how biased an image classification algorithm was trained to be. Buckle in, because this is about to get fairly nerdy. First, a quick overview of how a trained, deep-neural network functions: The goal is for you to be able to give it a picture, and have it tell you what’s in that picture. The network takes that image and processes it…


Link to Full Article: MIT researchers can now track AI’s decisions back to single neurons

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