When AI art has no author: Study finds generated images often can’t be traced to training data
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Artificial intelligence image generators often cannot be traced back to their training data, according to a study by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). This phenomenon, dubbed "attribution decay," occurs as the amount of data used to train the models increases. The study's findings have significant implications for artists, companies, and policymakers dealing with concerns over ownership, licensing, and regulations surrounding AI-generated content.
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