Isabelle Levent

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 th

Much of the discussion this year is about text-to-image, but I believe this is a temporary stage; these things are going to continue evolving very quickly.

New art-making technologies change art in consistent ways, and studying the past helps us understand how things will change in the future.

Academically, this is a collision of everything from computer science and art history to media studies to disruptive innovation to labor economics, and no one of these disciplines seems sufficient to cover the topic. 

With so much focus on creation, few systems consider revision. Revision—this is where the average writer gets the most outside help.

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