Isabelle Levent

The Lab’s primary focus is on the ways in which artists and designers are adopting, adapting and remaking AI processes, building their own datasets and reaching into the ‘grey box’ of AI technologies. 

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. 

OpenAI, which has been accused by its peers of releasing tools to the public with reckless speed, is particularly good at designing interfaces for its models that feel like magic. “It’s a conscious design imperative to produce these moments of shock and awe,” Crawford says. “We’re going to keep having those moments of enchantment.”

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

The issue here does not concern how many jobs will be created, how much income generated, how many pollutants added... Rather, the issue has to do with the ways in which choices about technology have important consequences for the form and quality of human associations.

As part of this model where the ‘back-end’ gets more attention than the artefact, a systematic dismantling of the myth of ‘the artist’ as a stand-alone genius, standing above, or aside from the world, needs to be enacted.