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How do machine-learning systems identify patterns and make decisions from data?

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Artificial intelligence — artificial intelligence Image generated by the Stable Diffusion model from the prompt “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings,” which is the definition of artificial intelligence in the *Encyclopædia Britannica* article on the subject. Stable Diffusion is trained on a large set of images paired with textual descriptions and uses natural language processing to generate an image.

Machine-learning systems identify patterns and make decisions by enabling computers to learn autonomously, which is particularly effective when explicit programming for every scenario is impractical. These systems often utilize artificial neural networks and genetic algorithms to improve their algorithms over time.13

Neural networks, inspired by the human brain, process information through layers of artificial neurons. Knowledge is distributed across the network via adaptive weights on the connections between these neurons. By adjusting these weights—often through supervised learning with labeled examples or unsupervised learning to discover hidden patterns—the network learns to recognize associations and make predictions. For instance, a back-propagation algorithm can refine these weights based on performance.21

Alternatively, genetic algorithms simulate natural selection, where symbols are "bred" and culled to produce increasingly effective programs. Together, these methods allow systems to perform complex tasks, such as image classification, data mining, and navigating autonomous vehicles, by adapting to the data they encounter.312