Isabelle Levent

We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively “good” prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on

However, we often found that it was the unexpected differences between the prompt and the generated image’s interpretation of it that yielded new insight for and excitement from participants.

They will shade our constant submissions to the vast digital commons, intentional or consensual or mandatory, with the knowledge that every selfie or fragment of text is destined to become a piece of general-purpose training data for the attempted automation of everything. They will be used on people in extremely creative ways, with and without

Whatever the size of the space, someone who comes up with a new idea within that thinking-style is being creative in the second, exploratory, sense. If the new idea is surprising not just in itself but as an example of an unexpected general  type , so much the better.

Imagine an art-lover at an exhibition entitled ‘Dots 2008’. He speaks to two artists, each displaying a painting. In both cases, the art-lover cannot see past the seemingly random arrangement of dots of paint. He mentions this to the first artist, who says: “Oh, no, they’re not randomly placed. Each dot represents a friend of mine. The colour of

There’s another edge case as well; in theory, with the same prompts and the random seed that’s used for generating the images, you could end up with someone else generating the same, or a very similar, image as what you created.