Artificial intelligence (AI) is a broad category of computer systems designed to perform tasks such as recognizing images, working with language, finding patterns, making predictions, or generating content. It is not one machine or one technique: a photo-sorting feature and a text chatbot may both use AI in very different ways.
What is artificial intelligence?
There is no single definition of AI that covers every context. In everyday terms, AI refers to artificial systems designed to carry out tasks associated with capabilities such as perception, language, learning, planning, prediction, or decision-making. NIST’s glossary collects several definitions, while Stanford HAI describes familiar capabilities including understanding language, recognizing images, learning from data, reasoning, and making decisions.
John McCarthy, whom Stanford HAI identifies as Stanford’s first faculty member in AI, called it “the science and engineering of making intelligent machines.” That is a memorable short characterization, not a complete modern definition.
How are AI, machine learning, and deep learning related?
Think of the terms as a set of nested categories: AI is the broadest term; machine learning (ML) is one approach within AI; and deep learning is a kind of machine learning. NASA describes ML as using data and algorithms to train computers to classify, predict, or find similarities and trends in large datasets. Deep learning uses neural networks with many layers.
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| Term | In simple terms | Relationship |
|---|---|---|
| Artificial intelligence (AI) | A broad category for systems designed to perform tasks involving capabilities such as perception, language, learning, or decision-making. | Umbrella term; definitions vary by context. |
| Machine learning (ML) | A way for a computer system to use data to learn patterns that support tasks such as classification or prediction. | One approach within AI. |
| Deep learning | Machine learning based on neural networks with many layers. | A subset of ML. |
| Neural network | A layered computational structure made of interconnected units. | Used in deep learning and other machine-learning systems. |
| Natural language processing (NLP) | Techniques for computers to process or work with human language. | NASA describes it as a subset of ML. |
| Generative AI | AI that produces new content, such as text, images, or audio, in response to inputs. | A family of systems; a text chatbot is one example. |
Neural networks are sometimes described as inspired by the brain. That refers to a broad structural analogy—interconnected units arranged in layers—not evidence that a computer network thinks or experiences the world as a human brain does.
How does AI work in simple terms?
There is no single process that every AI system follows. In many machine-learning systems, developers use data and algorithms to identify patterns that can help with a task. Depending on the system, its output might be a category, a prediction, a recommendation, or support for a human decision.
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- Classification analogy: Imagine sorting incoming mail into bins. A classifier assigns an input, such as an image, to a category. Unlike a person sorting mail, a computer relies on the way its system was built and the patterns available to it.
- Prediction analogy: Imagine estimating which of several outcomes is more likely using past patterns. A prediction system can produce an estimate, but that is not a guarantee of what will happen.
- Text-generation analogy: Imagine suggesting a likely continuation for a sentence. A chatbot can generate a plausible sequence of words from a prompt and patterns learned from data. That analogy helps explain the output, but it does not describe every model’s architecture or training process.
What can AI help with?
AI capabilities are best understood as tasks, not promises that every product can perform them well. Examples include:
- Perception: recognizing or classifying images and other data.
- Language: processing text or other human-language input.
- Finding patterns: identifying similarities or trends in data.
- Prediction and classification: estimating an outcome or assigning an input to a category.
- Decision support: helping people consider possible outcomes or recommendations.
- Content generation: producing text, images, audio, or other content from an input.
These are illustrative uses. The result depends on the particular system, its data and context, and the task; the word “AI” alone does not establish how accurate or suitable a tool will be.
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What does an AI chatbot do—and can you trust its answer?
A chatbot powered by a large language model generates text in response to a prompt. Stanford Teaching Commons explains this at a high level as analyzing large amounts of web data and producing likely word sequences associated with the prompt. Language patterns in training data can also carry dominant perspectives and biases. This is a simplified explanation, not a full description of every model or training pipeline.
A fluent, confident answer is not automatically a verified answer. Treat chatbot output as a draft or suggestion: check factual claims against reliable sources, especially before acting on advice involving health, money, safety, legal matters, or personal data. A system may be useful for a first explanation or outline, but its tone does not prove its claims are correct.
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How can a beginner try AI responsibly?
- Start with a low-stakes task. For example, ask for a summary of text you provide or request a few ideas for organizing a personal project.
- Give relevant context. State what you are trying to do and any useful constraints, without including private or sensitive information.
- Ask for a usable format. You might request a short list, a plain-language explanation, or a comparison table. Clear instructions are practical guidance, not a guarantee of a good result.
- Review the output. Look for missing context, unsupported claims, bias, or mistakes; verify important facts independently.
- Keep human judgment in the loop. Decide whether the result fits your situation rather than treating a generated response as an automatic decision.
AI literacy is not limited to learning how to code. It also includes understanding how systems work, learning to use them, and considering practical and ethical implications.
What should you know about AI risks and oversight?
AI systems are varied, and no general label guarantees accuracy, fairness, safety, autonomy, or human-like reasoning. Results can reflect limitations in a system’s data or design. Avoid sharing sensitive information with a tool unless you understand how it handles that information, and use extra care when an error could have serious consequences.
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Organizations can use risk-management guidance to consider trustworthiness during AI design, development, use, and evaluation. NIST’s AI Risk Management Framework (AI RMF) is voluntary, not a universal requirement, and following a framework does not guarantee that a particular tool is accurate or safe. NIST says AI RMF 1.0 was released on January 26, 2023; a generative AI profile followed on July 26, 2024. NIST also says AI RMF 1.0 is being revised as part of the White House AI Action Plan.
Quick Recap
What to remember
- AI is a broad category, not a single product or fixed method.
- Machine learning is one approach within AI; deep learning is a subset of machine learning.
- AI can support tasks such as recognizing images, processing language, finding patterns, predicting, and generating content.
- Generated language may sound convincing without being verified, so check consequential claims.
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