Artificial neural networks are modeled from the biological neural networks that make up our brains; they are used to enable computers to learn similarly to how our brains learn. For example, we learn to differentiate concepts over time by repetition, after seeing so many varieties of trees and of flowers, we learn what is the template of a tree, and can recognize trees in the future even if it is a new variety. Certain features of trees—branch-leaves-trunk—are known to be connected, and when activated together we recognize, this is a tree. An artificial neural network acts in similar fashion; connections between artificial neurons become strengthened over time if they are frequently activated together, in what’s termed “Hebbian” learning...