Photo: Zulfugar Karimov on Unsplash
What is generative AI?
In short: Generative AI is a type of AI that creates new content, such as text, images, audio or video, after learning patterns from huge collections of examples. It is fast and impressive, but it can make confident mistakes, reflect biases in its training data and raise questions about copyright and fakes.
AI that makes things
For years, most AI was used to sort or predict: spotting spam, recommending films or recognising faces. Generative AI is different. It creates something new when you ask: a paragraph, a picture, a song, a voice or a short video clip. Chatbots that write answers and apps that turn a sentence into an image are both generative AI.
Our article What is AI? covers the basics if you are new to the topic.
How it learns
A generative AI system, often called a model, is trained on an enormous number of examples: text from books and websites, or images paired with descriptions. During training it does not memorise each example like a library. Instead, it learns patterns: which words tend to follow which, or what shapes and colours usually go with the word "sunset".
When you type a request, called a prompt, the model uses those patterns to build something that fits. A text model predicts one small piece of text at a time. An image model starts from random noise and gradually shapes it into a picture that matches your description, a bit like a sculptor revealing a figure from a block of stone.
Our article How chatbots work shows the text side step by step.
What it is good at
- Producing first drafts of emails, letters, summaries and plans.
- Explaining a topic in simpler words, or in another language.
- Brainstorming ideas, names and options.
- Creating illustrations, mock-ups and simple graphics.
- Helping with routine computer code, which a person should still check.
The limits to remember
- It can be confidently wrong. It produces what sounds right, not what is verified. Made-up facts are sometimes called hallucinations.
- It reflects its training data. If the examples contained stereotypes or gaps, the output may too.
- It may be out of date. A model only knows what was in its training data unless it is connected to search.
- Ownership questions are unsettled. There are ongoing legal arguments in several countries about training on copyrighted work and who owns the results.
- It can be misused. The same tools that make art can make fake photos, voices and videos. See Spotting deepfakes.
Tip: Treat generative AI like a quick, well-read assistant who sometimes makes things up. Use it to get started, then check anything important against a reliable source.
Labels and transparency
More services now label AI-generated images and video, and laws such as the EU's AI Act are bringing in transparency rules for AI-made content. Labels help, but a missing label does not prove something is real. Our explainer AI image labels covers how they work.
Try this
- Ask a chatbot to explain something you already know well.
- Look for anything it got slightly wrong or left out.
- Ask it for its sources, then check whether they exist and say what it claimed.
It is a quick way to see both its strengths and its weak spots.
Key takeaways
- Generative AI creates new text, images, audio and video from a prompt.
- It learns patterns from huge numbers of examples rather than looking answers up.
- It is great for first drafts and ideas but can be confidently wrong.
- Check important facts, and remember that AI-made fakes exist.
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