In Kate Compton’s reading, the “10,000 Bowls of Oatmeal Problem” describes an issue in generative work when an algorithm can produce a ginormous amount of artifacts that are each unique but are not perceived as uniquely different from the audience perspective. Compton provides an example that if she created 10,000 bowls of oatmeal and each grain of oat was different, according to the algorithm, it is unique, but perceived from an outside view, it all ends up looking the same.
When you need to generate thousands of artifacts that will have to vary slightly, you can choose between perceptual uniqueness or perceptual differentiation. Perceptual differentiation is an easier level to succeed at if the environment doesn’t need the artifacts to be highly memorable (i.e. trees in a landscape or a very large crowd.) Perceptual differentiation is when the user can tell from a glance that there is a difference between the artifacts, but it isn’t that significant. On the other hand, perceptual uniqueness is the contrasting view between remembering a main character versus remembering a face in the crowd. The artifact must have a distinct character personality, making it more memorable than the other artifacts.
To overcome this problem, it is important to understand your audience and know the key characteristics of the artifact you are generating. Humans like readable meanings and identifiable personalities, and is a great strategy to start with overcoming this problem.