Current thinking on Image Generators and Training Ethics
Training, replication, artistic practice and the commons: my argument for judging corporate AI by what it contributes back.
Lynn Cole / Writing
Essays, thoughts, and technical notes.
Training, replication, artistic practice and the commons: my argument for judging corporate AI by what it contributes back.
Priestess Lynn surveys the destruction of art. There is still plenty to cannibalize.
One of the biggest mistakes people are making right now is building closed loops into their agent systems.
Patterns, spatial memory and nonlinear connections shape the way I learn, code and make art. These are the creative rabbit holes that feel natural to me.
Enterprise software can learn from marketers: target the information, respect cognitive resources and build engagement into the workflow.
Tell a coding system what good means: your architecture, standards, metaphors, file boundaries and expectations for documentation.
Good management balances accountability, protects attention and builds a collaborative team. It is much more than giving orders.
An early position on AI art and music: separate corporate training practices from artists’ work, and investigate the data before deciding what it demonstrates.
An early pass through the Suno corpus turns up unusual musical patterns, noisy data and a demanding analysis pipeline. The method still has to earn its conclusions.
CADIE’s fictional story stayed with me as poetry and as a provocation: what kinds of emergent systems, and what kinds of world, could we choose to build?
AI changes the cost of scale and the economics of work. I see an inversion coming, where workers can become their own enterprise.
Recognizing my signature in Flux got me thinking about training, adversarial art and the influence artists leave inside models.