Labeled: A New Wave Of AI Content Labeling Efforts

The growing need for high-quality text data is having a significant impact on AI development. Some executives and researchers are concerned that the industry's demand for this type of data could outstrip supply within two years, which has the potential to slow down the progress of AI. This is because high-quality text data is crucial for developing the next generation of large language models (LLMs), which require vast amounts of diverse and accurate data to improve and function effectively.

Model developers are paying for natural content and reviving their efforts to label synthetic data because they are hungry for more high-quality content to develop the next generation of large language models (LLMs). Labeling AI content helps in separating natural from synthetic content, which is essential for training models effectively and avoiding issues such as model collapse. Moreover, labeling can serve as a shield against fake news by making it easier to identify AI-generated content.

Generative AI data providers like Appen or Scale AI are incentivizing writers by offering payment for creating content that can be used for LLM model training. Writers, who are often required to have a Ph.D., master's degree, or current college enrollment, are paid between $25 to $50 per hour to perform tasks such as ranking AI responses, writing short stories, and fact-checking. Although writers aren't getting rich from this work, the early signs suggest that writing for AI could become a monetizable skill.

Google and YouTube are employing several strategies to combat AI spam and fake news. Google is using a double-pronged approach that includes prominently showing forums like Reddit in search results, where content is more likely to be created by humans, and implementing penalties for AI spam. YouTube has introduced guidelines requiring video creators to disclose when they've created altered or synthetic content that's realistic, including using AI tools. Additionally, YouTube will label realistic altered or synthetic election content that doesn't violate their policies to clearly indicate to viewers the nature of the content.

Labeling AI content has several potential benefits. It can help model developers by making it easier to distinguish between natural and synthetic data for training purposes. For society, labeling acts as a protection against fake news by clearly indicating when content is AI-generated. Furthermore, labels can increase the value of human-generated content by highlighting its authenticity. In the commercial realm, labels can help consumers make informed decisions and may even incentivize content creators to produce high-quality content, as it could lead to new monetization models separate from traditional affiliate or advertising revenue.