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Artificial intelligence (AI) is the field of study and practice that develops systems capable of perception, learning, reasoning and autonomous decision-making. It spans symbolic methods, statistical machine learning and deep neural networks. AI enables automation, predictive analytics and new product capabilities across domains.
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Artificial intelligence refers to computer systems performing tasks that typically involve human perception, learning, reasoning, or language processing.
The field took shape with the 1956 Dartmouth Summer Research Project, where John McCarthy coined the term “artificial intelligence”. It then developed through many methods and schools.
An AI system receives data and a task, processes them with a model, and produces a classification, prediction, decision, or other output. Quality depends on data, objective, model, and environment and must be assessed in context.
Examples and measurements support training, adaptation, or evaluation.
A model represents patterns and produces outputs for new inputs.
The goal and constraints determine what performance the system should deliver.
Tests examine quality, limits, and possible harms in the actual use case.
The basic concept helps frame AI initiatives realistically and formulate requirements for data, evaluation, and accountability. Claims about performance must be measured against the task, context, and consequences.
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