Two kinds of AI: sorters and makers
Most AI you've met is a sorter: it looks at a photo and says "cat or dog?", or reads a review and says "happy or angry?". A generative model is a maker: give it the idea "a cat" and it produces a brand-new cat picture that never existed before. Same raw ingredient — data — but one labels it and the other creates from it.
Learning a style, not memorising
Think of an art student who studies thousands of Van Gogh paintings. They don't memorise one painting to copy it — they absorb the style: the swirls, the colours, the brushwork. Afterwards they can paint a new scene Van Gogh never painted, in his style. A generative model does the same with its training data: it learns the pattern, then makes new work that fits it.
The big idea
- Generative models learn the underlying pattern of their data, then sample new examples from it.
- They work for many kinds of data: images (Midjourney, DALL·E), text (ChatGPT), audio, and video.
- This course covers the main families: GANs, VAEs, and — the one behind today's image tools — diffusion.
Where we're headed
Over the next lessons we'll meet three ways to build a generative model — GANs, VAEs, and diffusion — then see how they team up with language to turn your words into pictures. Let's start with the most competitive one: GANs.