# Gradient Descent **Domain:** Optimization / Machine Learning **Doc Type:** Canonical Concept Node **Maturity:** Foundational ## Definition **Gradient descent** is a family of iterative optimization methods that adjust parameters in a direction expected to reduce an objective function, using local derivative information or an estimate of it. ## Evolutionary Relation [[articles/The Evolutionary Roots of Silicon Valley|The Evolutionary Roots of Silicon Valley]] calls gradient descent hill-climbing on a fitness landscape. The structural analogy is useful: both move through a space under differential evaluation. The mechanisms differ. Gradient descent follows local slope in a differentiable parameter space; natural selection acts on inherited population variation without calculating a gradient. This distinction matters because gradient descent can be highly efficient while remaining sensitive to representation, initialization, local geometry and the chosen loss. It does not discover purpose; it follows the evaluative surface designers and data construct. ## Governance Context Optimization hides normative choices inside differentiable proxies. If a harmed population is absent from the objective, the gradient has no reason to protect it. ## Key Insight **Gradient descent supplies direction, not justification: it can tell a system how to reduce a loss without establishing that the loss deserves to govern.** ## Sources / Provenance - Léon Bottou, Frank Curtis and Jorge Nocedal, “Optimization Methods for Large-Scale Machine Learning,” _SIAM Review_ 60 (2018). - Nexus synthesis: [[articles/The Evolutionary Roots of Silicon Valley|The Evolutionary Roots of Silicon Valley]]. ## See Also [[wiki/Objective Function|Objective Function]], [[wiki/Selection Gradient|Selection Gradient]], [[wiki/Machine Learning|Machine Learning]], [[wiki/Natural Selection|Natural Selection]]