According to Jim Rutt, gradient descent is a mathematical optimization algorithm used to minimize the error function in various machine learning models. Essentially, it's a method that iteratively adjusts the parameters of a model in the direction where the error decreases most steeply. In each iteration, the algorithm calculates the gradient of the error function with respect to the model's parameters and then updates these parameters by moving them a tiny step opposite to the gradient's direction. This process continues until the model converges to a local or global minimum of the error function. Gradient descent is essential for training neural networks and other complex models, as it provides a systematic approach to finding optimal solutions in high-dimensional spaces.

See also: neural network, self-organization, free will, cognitive science