Isabelle Levent

Many methods for creating these models don't (and to be honest can't) attach the name, website and other details of every image and piece of text used to a create a new image in the metadata to every step of the process.

The Lab’s mission is also to develop a critical literacy that can help cultural institutions approach AI technologies as advanced and multilayered media. While reliant on the highly specialised theoretical work needed to untangle issues such as ‘distributed authorship’ (Ascott 2005; Zeilinger 2021) involved in artistic research, the Lab does not

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

Our intuitive moral understanding of actors and transgressions may be at odds with the inherent complexity of AI systems.

Now none of this is meant to say that I think programmers, artists and engineers have no responsibilities when it comes to the outputs of machine learning models. In fact, I think we bear responsibility for everything these models do. (I never, for example, attribute authorship to a program or a model. If I publish the results of a text generator,

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.