Rumelhart, Hinton & Williams (paper)

The 1986 paper by Rumelhart, Hinton, and Williams introduced the backpropagation algorithm, a method for teaching neural networks how to learn from data. It enables networks to adjust their internal settings (weights) by calculating mistakes at the output and working backward through the network. This process allows the network to improve its performance over time, making it capable of tasks like recognizing images or understanding language. The paper was crucial in advancing artificial intelligence by providing a practical way for neural networks to learn complex patterns efficiently.