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📖Amazon Buys Rare Books for AI Training

Rare books are Amazon's new data source

TL;DR

Amazon is acquiring rare books, cutting their spines, and scanning them for AI training. This move highlights the value of pre-2022 texts in avoiding 'model collapse'.

Amazon has started purchasing rare books through commercial channels, a practice that involves removing the spines to scan the content for AI training purposes. The company places tracking devices on these books before they reach specific facilities like VGT3, which is marked with a dinosaur symbol holding a book. This acquisition underscores Amazon's need for diverse and high-quality text data to train its language models (LLMs). Rare books, especially those published before 2022, offer unique training material since they were not generated by AI. By using such texts, Amazon aims to avoid 'model collapse', where LLMs degrade in quality after ingesting too much AI-generated content.

Amazon Buys Rare Books for AI Training — TechCrunch

Key Points

1

Amazon purchases rare books through commercial channels to train its LLMs.

2

The company cuts spines off rare books before scanning their content.

3

A tracking device was placed in a book that arrived at Amazon's VGT3 facility.

4

VGT3 is marked with a dinosaur symbol holding a book, indicating AI focus.

5

Rare books published before 2022 are especially valuable for avoiding model collapse.

Why It Matters

If you're training an LLM and want to avoid 'model collapse', rare pre-2022 texts offer unique value. Amazon's move shows the importance of diverse data sources, but also raises ethical questions about book preservation.

AmazonLLM trainingrare booksAI ethics

Frequently Asked Questions

Why does this matter?

If you're training an LLM and want to avoid 'model collapse', rare pre-2022 texts offer unique value. Amazon's move shows the importance of diverse data sources, but also raises ethical questions about book preservation.

What happened?

Amazon is acquiring rare books, cutting their spines, and scanning them for AI training. This move highlights the value of pre-2022 texts in avoiding 'model collapse'.

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