LATIDIA · Investigación

Cuando el arte de la IA no tiene autor: el estudio encuentra que las imágenes generadas a menudo no se pueden rastrear hasta los datos de entrenamiento

Un nuevo método para eliminar quirúrgicamente ejemplos de entrenamiento de un modelo revela que a medida que crecen los conjuntos de datos, el vínculo entre lo que un modelo aprende y lo que produce se disuelve.

La noticia

When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate. The connection itself has disappeared. The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual