# Machine Learning **Domain:** Artificial Intelligence / Statistics / Adaptive Systems **Doc Type:** Canonical Discipline Node **Maturity:** Foundational ## Definition **Machine learning** develops systems whose behavior or predictions improve through exposure to data, feedback or search rather than through exhaustive specification of every decision rule. ## Evolutionary Nexus [[articles/The Evolutionary Roots of Silicon Valley|The Evolutionary Roots of Silicon Valley]] places machine learning inside a longer history of adaptive inference: paleontology reconstructs systems from fragments, genomics reads descent from sequence, expert systems encode biological expertise and evolutionary computation searches by variation and selection. Not every machine-learning method is literally Darwinian. Gradient descent changes parameters along a calculated local direction; supervised learning minimizes error against labeled examples; reinforcement learning updates behavior under reward; evolutionary algorithms operate on populations with explicit variation and selection. Their commonality is differential retention under an evaluative structure, not one identical mechanism. ## Continuity Context Learning systems become part of human cognitive development when they mediate memory, attention and decision. This makes model updates a form of environmental change within [[wiki/Human-Machine Symbiosis|Human-Machine Symbiosis]], with duties of disclosure, contestability and preservation of user agency. ## Key Insight **Machine learning does not eliminate design; it relocates design into data, objectives, representations, feedback and the environment in which adaptation occurs.** ## Sources / Provenance - Tom M. Mitchell, _Machine Learning_, 1997. - Christopher M. Bishop, _Pattern Recognition and Machine Learning_, 2006. - Historical synthesis: [[articles/The Evolutionary Roots of Silicon Valley|The Evolutionary Roots of Silicon Valley]]. ## See Also [[wiki/Supervised Learning|Supervised Learning]], [[wiki/Reinforcement Learning|Reinforcement Learning]], [[wiki/Gradient Descent|Gradient Descent]], [[wiki/Evolutionary Algorithms|Evolutionary Algorithms]], [[wiki/Objective Function|Objective Function]]