Definition
Plain language
The step-by-step process a neural network uses to trace each mistake backward through its layers and figure out how to adjust.
As stated in the literature
The algorithm that computes gradients of a loss with respect to every parameter by applying the chain rule backward through the network's layers; the foundation of training for essentially all modern neural networks, and the metaphor behind tracing blame backward through a multi-agent system.
Also called: backprop
Why it matters: It is the core mechanism that lets neural networks actually learn from their mistakes, and without it modern AI training would not work.
For example, if a network labels a photo of a cat as a dog, backpropagation traces that error back through every layer to nudge each internal setting slightly toward the correct answer.
Heard on the show
“Then they attach the error to the final node and trace backward, the way backprop walks blame back through a network.”Episode 181 — How to Backpropagate Blame Through a Team of Chatbots — And When It Backfires