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.