Isabelle Levent

Instead, I’d like us to ask: in whose voice do our machines write? What voices do they obfuscate? Where do their words come from? In short, I’d like us to ask questions about power, and the ways in which it functions through and around language. 

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.”

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

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. 

For a computer to make a subtle combinational joke, never mind to assess its tastefulness, would require, first, a data-base with a richness comparable to ours, and, second, methods of link-making (and link-evaluating) comparable in subtlety with ours. 

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,

The fact that adding keywords like  Let’s Think Step By Step , adding “Greg Rutkowski”, prompt weights, and even negative prompting are still so enormously effective, is a sign that we are nowhere close to perfecting the “language” part of “large language models”.