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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallArtificial intelligence (AI) has no single universally accepted definition. A useful modern description is a machine-based system that takes in information, infers how to produce an output—such as a prediction, generated content, recommendation, or decision—and may use that output to affect a physical or virtual environment. That definition describes what a system does; it does not require the system to think, understand, or feel as a person does.
What is artificial intelligence, really?
The OECD’s revised definition offers a practical starting point: “An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.” The OECD Council adopted this definition on 8 November 2023. Read the OECD Recommendation.
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In plain language, an AI system receives inputs and uses its design or learned patterns to work out an output in pursuit of an objective. That output can be a forecast, a piece of text or an image, a suggested next step, or a decision. The output can remain on a screen or affect something in the world; the definition allows for both.
Inputs, inference, outputs, and effects
- Inputs: Information the system receives, such as text, images, sensor readings, or other data.
- Inference: The process by which it uses those inputs to determine an output in relation to an objective.
- Outputs: Predictions, content, recommendations, or decisions, among other possible results.
- Effects: Outputs may influence a virtual environment, such as an application, or a physical one, such as a machine or vehicle.
How an AI system can work
A useful conceptual model has three parts: sensors collect data from an environment, operational logic processes that data in relation to objectives, and actuators can carry out actions that change the environment. The OECD uses this model to explain AI systems; it is not a checklist every AI product must satisfy. See the OECD’s account of AI in society.
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For example, a system may use a camera as an input, interpret what it sees, and send an instruction to a device. A text-generating system, by contrast, may take a written prompt and return text without any physical sensor or actuator. Both can fit the broader description of AI, even though their inputs, outputs, and effects differ.
AI is a broad field, not one human-like mind
AI covers systems built for different tasks, including perception, language, planning, learning, and communication. NIST’s glossary records definitions from multiple source documents: some emphasize performance in variable circumstances or learning from experience; others describe systems designed for tasks associated with human perception, cognition, planning, learning, communication, or physical action. These definitions reflect different contexts and purposes, rather than one mandatory list of properties. Consult the NIST glossary.
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Systems also differ in autonomy and in whether they adapt after deployment. An AI system might only produce a result when a person requests one; another may act with less direct intervention. Some systems can be updated or adapt in operation, while others do not change their behavior after deployment. Calling something AI alone does not tell you which case applies.
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Why a conversation or test score does not prove general intelligence
One historical way to think about machine intelligence is the Turing test. In the OECD’s 2019 primer, a human evaluator asks questions of both a human and a machine, then judges whether the machine’s typed answers can be distinguished from the human’s. It is a test of conversational behavior, not a general proof of consciousness or intelligence. The OECD primer discusses the Turing test.
The same primer attributes to computer scientist John McCarthy a 1956 definition of AI as “the science and engineering of making intelligent machines.” That is a useful historical description, but it does not resolve what intelligence means in every technical or philosophical context. The attribution appears in the OECD’s account, rather than in McCarthy’s original text.
Performance on a narrow benchmark also needs to be interpreted narrowly. The OECD’s 2021 capabilities report notes that a system could excel on a particular IQ-style test and “can do nothing else beyond the particular IQ tests.” A high score establishes performance on that test; it does not, by itself, demonstrate competence across other tasks. Read the OECD report on AI capabilities.
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A practical way to judge an AI claim
When someone calls a product or system “intelligent,” ask what the label means in that specific case. These questions make the claim more concrete:
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- What task is the system designed to perform?
- What information does it receive, and what does it produce?
- Where does it operate, and can its output affect anything beyond the software itself?
- How much autonomy does it have, and can it adapt after deployment?
- What evidence supports the claim—performance on one defined test, or results across a broader range of tasks and conditions?
The answers distinguish a specific capability from the much broader idea of intelligence. AI is a useful label for a varied class of machine-based systems, not a guarantee that any one system thinks like a person or can do everything a person can.
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