What is AI? Artificial intelligence (AI) is a broad field of computer science focused on building systems that perform tasks such as recognizing images, working with language, making predictions, recommending choices, or taking action. AI is not one product or a synonym for chatbots: different systems use different techniques to handle particular tasks.
What does artificial intelligence mean?
There is no single, universally accepted technical definition of AI. Definitions vary with their purpose: some emphasize capabilities such as learning or reasoning, while others describe systems by the tasks they perform or the techniques they use. The National Institute of Standards and Technology (NIST) records multiple definitions in its glossary.
A useful working definition is that AI is the field and family of techniques used to build artificial systems that perform selected tasks involving capabilities such as perception, learning, language, reasoning, planning, prediction, decision-making, communication, or action. John McCarthy, one of the field’s pioneers, described it as “the science and engineering of making intelligent machines,” as quoted by Stanford Human-Centered Artificial Intelligence.
That description does not mean every AI system can do all of those things. An AI system is generally designed for an objective or a set of tasks. Its label alone does not establish how capable or reliable it is beyond those tasks.
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How does AI work?
At a high level, an AI system takes in information, processes it according to operational logic aimed at an objective, and produces a prediction, recommendation, decision, or action. OECD.AI describes this in terms of input collected by sensors, logic that interprets information and generates outputs, and actuators that can change the system’s environment.
- Input: The system receives information, such as text, an image, or data from a sensor. Not all AI uses physical sensors; software may receive information directly as digital input.
- Processing: The system applies its operational logic to interpret or transform that information in relation to its task.
- Output or action: It may return a result, such as a prediction or recommendation, or trigger an action. Some systems can affect the physical or digital environment through actuators or connected tools; many simply produce an output for a person or another program.
This input-processing-output pattern is a way to understand how AI systems operate, not a claim that they all work alike internally. AI also does not have to copy the human brain or use methods observable in biology. McCarthy wrote in a Stanford-hosted Q&A that AI is related to using computers to understand human intelligence, “but AI does not have to confine itself to methods that are biologically observable.”
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How is AI different from machine learning?
AI is the broader field; machine learning (ML) is a set of techniques used within it. The terms are related, but they are not interchangeable. Some AI systems use machine learning, while AI as a field also encompasses approaches that do not fit the everyday idea of a model learning from examples. NIST’s glossary reflects this range by including more than one definition and emphasis.
Generative AI is another related but distinct term: it refers to AI systems designed to generate outputs, such as text or images. It is not a synonym for all AI or for machine learning as a whole.
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What kinds of tasks can AI perform?
AI is best understood by the task a system is built to handle, rather than as a single level of general intelligence. Examples include:
- Language: processing or generating text and other language outputs.
- Image recognition: identifying or classifying content in images.
- Prediction: estimating an outcome from available information.
- Recommendations: suggesting options based on a task or objective.
- Decision-making or action: selecting an option or, when connected to tools or actuators, carrying it out.
Performance on one task does not prove broad competence on another. A system that produces fluent language, for example, should not be assumed to be generally reliable, conscious, or capable of human-like understanding just because its output appears convincing.
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When did AI begin?
The name and the field have different historical milestones. Stanford HAI says John McCarthy coined the term “artificial intelligence” in 1955. The Stanford Encyclopedia of Philosophy identifies the 1956 Dartmouth Summer Research Project on Artificial Intelligence as the field’s official start. These dates refer to different events: naming the subject and convening the foundational workshop.
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