What do people mean by AI?

What do a photo editor, a music service, and a chatbot have in common? All can use artificial intelligence, but they do different jobs. One changes a picture, another suggests a song, and the third can help write an invitation. The shared label is only a starting point.

Artificial intelligence, or AI, covers computer systems that use information to make predictions, recommendations, decisions, or new content. The OECD's explanation of AI systems gives the more precise definition. For now, start with the job a system does.

A spam filter can estimate which messages are unwanted. A music service can recommend a song. A speech-recognition system can turn spoken words into text. An assistant can draft an invitation. One product may combine several of these capabilities.

Some automation follows rules written directly by a person. A reminder that appears at a time you set does not need AI.

Many modern AI systems use machine learning: developers train a model on examples so it can learn patterns useful for a task. A trained model is the part of the system that applies those patterns. Training adjusts numerical settings inside the model to improve its performance on the training task. It learns patterns without gaining a person's experiences or judgment.

Imagine sorting unwanted messages. A fixed rule might block every message containing a particular phrase. A learned model can use patterns across many features of a message. Both can make mistakes. Check whether the approach works for the messages it actually encounters.

These are simplified examples, not descriptions of the implementation inside a particular email service.

Generative AI produces content, such as text, images, audio, video, or computer code. An assistant drafting an invitation uses this capability. A system that sorts existing messages into folders does a different job.

Creating content and finding information are also different operations. A convincing paragraph can contain an invented detail. A product may use search to gather information before generating its answer, but generation alone does not establish that the answer is correct.

The NIST guide to generative-AI risks examines unreliable information and harmful bias alongside other risks. You can continue without reading the technical report. Keep it for a deeper question.

Consider these fictional features. You can do this exercise without opening an AI tool.

For an exact reminder, ordinary automation may be all you need. For an invitation, writing it yourself, adapting a template, and asking an assistant are all reasonable options. AI is useful when its result and the effort to check it fit your task.