OpenAI transcribed over a million hours of YouTube videos to train GPT-4

The Whisper audio transcription model is a tool developed by OpenAI to transcribe audio content. It was reportedly used to transcribe over a million hours of YouTube videos to train GPT-4, OpenAI's most advanced large language model. This transcription effort was part of OpenAI's strategy to gather high-quality training data for their AI models.

OpenAI transcribed YouTube videos for GPT-4 training because the company was facing a shortage of high-quality training data. They needed substantial amounts of diverse data to improve the performance of their AI models. Despite knowing that their method was legally questionable and might fall into a gray area of AI copyright law, they believed it to be fair use.

AI companies are facing challenges with training data due to the following reasons:

• Exhaustion of useful data: Companies like OpenAI have used up readily available high-quality data sources.
• Legal and ethical concerns: The use of copyrighted material without permission for training AI models is legally questionable and can lead to lawsuits.
• Privacy regulations: Changes in privacy policies, like those made by Meta in response to the Cambridge Analytica scandal, limit how consumer data can be used.
• Scarcity of new content: There is a concern that AI companies may outpace the creation of new content by 2028, making it difficult to find fresh data for training models.

AI companies are considering several strategies to overcome training data limitations, including:

• Creating synthetic data: Generating new data internally using their own models.
• Curriculum learning: Feeding models high-quality data in an ordered fashion to help them make smarter connections between concepts with less information.
• Licensing or acquisition: Considering paying for book licenses or buying a large publisher to access copyrighted works legally.
• Unauthorized use: Some companies are using data without permission, which has led to legal issues.

Curriculum learning is a method of training AI models by presenting them with high-quality data in a structured and sequential manner. The idea is to help the models establish smarter connections between concepts using less information. This approach is akin to how humans learn, starting with simple concepts and gradually progressing to more complex ones. It's one of the potential solutions being explored to address the scarcity of training data.