Image, Audio and Video Generation
You have already met AI that writes text. But the same family of technology can now paint pictures, speak in a human voice, compose music and even film short video clips — all from a few words you type. This lesson is your guided tour of generative media: how it works under the hood, how to control it, and how to stay safe and honest while using it.
The big idea in one sentence: a modern image, audio or video model learns what "normal" looks or sounds like, and then rebuilds a fresh example of it from scratch, steering the process with your words. Once you understand that single idea, everything else — prompts, editing, deepfakes, detection — falls into place.
What you will be able to do after this lesson
- Explain diffusion step by step — how a model turns pure random noise into a clear picture.
- Write strong image prompts using subject, style, lighting, composition and negative prompts.
- Name and use the four core editing powers: inpainting, outpainting, style transfer and upscaling.
- Describe how text-to-speech, voice cloning and music generation work, and the fraud and consent risks of voice cloning.
- Judge what AI video can and cannot yet do.
- Spot likely AI media by checking hands, text, physics and lighting — and understand why detection keeps getting harder.
- Discuss the ethics: consent, copyright and labeling AI content.
1. Diffusion: turning noise into a picture
Most modern image generators (tools like Stable Diffusion, Midjourney and DALL·E work this way) are built on an idea called diffusion. The word "diffusion" comes from physics: it means the way something spreads out and mixes until it is evenly scattered, like a drop of ink slowly clouding a glass of water until the whole glass is a uniform grey. AI diffusion borrows that picture and then runs it backwards.