Editing Artificial intelligence (section)

=== Learning ===
[[Machine learning]] is the study of programs that can improve their performance on a given task automatically.<ref>[[machine learning|Learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 19–22}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=397–438}}, {{Harvtxt|Luger|Stubblefield|2004|pp=385–542}}, {{Harvtxt|Nilsson|1998|loc=chpt. 3.3, 10.3, 17.5, 20}}</ref> It has been a part of AI from the beginning.{{Efn
|[[Alan Turing]] discussed the centrality of learning as early as 1950, in his classic paper "[[Computing Machinery and Intelligence]]".{{Sfnp|Turing|1950}} In 1956, at the original Dartmouth AI summer conference, [[Ray Solomonoff]] wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".{{Sfnp|Solomonoff|1956}}
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[[File:Supervised and unsupervised learning.png|right|upright=1.4|frameless]]
There are several kinds of machine learning. [[Unsupervised learning]] analyzes a stream of data and finds patterns and makes predictions without any other guidance.<ref>[[Unsupervised learning]]: {{Harvtxt|Russell|Norvig|2021|pp=653}} (definition), {{Harvtxt|Russell|Norvig|2021|pp=738–740}} ([[cluster analysis]]), {{Harvtxt|Russell|Norvig|2021|pp=846–860}} ([[word embedding]])</ref> [[Supervised learning]] requires labeling the training data with the expected answers, and comes in two main varieties: [[statistical classification|classification]] (where the program must learn to predict what category the input belongs in) and [[Regression analysis|regression]] (where the program must deduce a numeric function based on numeric input).<ref name="Supervised learning">[[Supervised learning]]: {{Harvtxt|Russell|Norvig|2021|loc=§19.2}} (Definition), {{Harvtxt|Russell|Norvig|2021|loc=Chpt. 19–20}} (Techniques)</ref>

In [[reinforcement learning]], the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".<ref>[[Reinforcement learning]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 22}}, {{Harvtxt|Luger|Stubblefield|2004|pp=442–449}}</ref> [[Transfer learning]] is when the knowledge gained from one problem is applied to a new problem.<ref>[[Transfer learning]]: {{Harvtxt|Russell|Norvig|2021|pp=281}}, {{Harvtxt|The Economist|2016}}</ref> [[Deep learning]] is a type of machine learning that runs inputs through biologically inspired [[artificial neural networks]] for all of these types of learning.<ref>{{Cite web |title=Artificial Intelligence (AI): What Is AI and How Does It Work? {{!}} Built In |url=https://builtin.com/artificial-intelligence |access-date=2023-10-30 |website=builtin.com}}</ref>

[[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization]].<ref>[[Computational learning theory]]: {{Harvtxt|Russell|Norvig|2021|pp=672–674}}, {{Harvtxt|Jordan|Mitchell|2015}}</ref>

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