AI companies are increasingly purchasing printed books published before 2022 as training data to avoid contamination from AI-generated text. ISBNdb facilitates bulk acquisitions, claiming these books provide high-quality, curated information that doesn't suffer from issues like model collapse.
AI companies are actively seeking old, printed books as a resource for training data. Due to the prevalence of AI-generated text on the internet, these older books provide a fresh and reliable source. ISBNdb, an existing provider of book data and services, emphasizes that printed titles from before 2022 are untainted by current AI slop.
ISBNdb has pivoted to serve the needs of AI companies by offering high-volume book acquisition services, allowing purchases ranging from 1,000 to 1 million books. This move capitalizes on the growing demand for quality training data, ensuring AI labs can acquire and digitize printed books efficiently.
Concerns are rising about the risks associated with training AI models on data that includes AI-generated content. The phenomenon known as model collapse may occur if these models are trained on contaminated data, leading to increased errors. Thus, the integrity of data sources is critical for the effectiveness of AI systems.
Recent legal actions, notably a copyright lawsuit involving Anthropic, highlighted controversies surrounding the acquisition of printed books for AI training. The internal documents revealed plans to scan and then potentially destroy these books, raising ethical questions about data sourcing in AI development.
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AI companies are increasingly purchasing printed books published before 2022 as training data to avoid contamination from AI-generated text. ISBNdb facilitates bulk acquisitions, claiming these books provide high-quality, curated information that doesn't suffer from issues like model collapse.