The AI-generated content is being used to train AI models, which can lead to model collapse and other problems. Researchers are concerned about the potential consequences of recursive training, which could magnify bias and error.
- Generative AI models are being fed with human-made content to learn and produce their own content.
- Recursive training of AI models on AI-generated content can lead to model collapse and other problems.
- Biases and errors in AI-generated content can migrate into future iterations of the programs, amplifying errors over time.
- Curating training datasets is essential to alleviate bias and other problems in AI-generated content.
What is generative AI?
Generative AI models are trained on large amounts of data to learn and produce their own content, such as text, images, or music.
What is recursive training?
Recursive training involves training an AI model on the output of a previous AI model, leading to a feedback loop that can magnify bias and error.
What is model collapse?
Model collapse is a degenerative process whereby an AI model forgets what it has learned over time, resulting in nonsensical outputs.
How can we alleviate bias and other problems in AI-generated content?
Curating training datasets is essential to ensure that AI-generated content represents the underlying distribution well. Using AI to generate text or images that counterbalance prejudice in existing datasets and computer programs could also help to debias systems.
The use of AI-generated content to train AI models can lead to model collapse and other problems. Researchers are calling for curating training datasets to alleviate bias and other issues.