Anthropic's Controversial Book Scanning for AI Training
Anthropic has been revealed to have destroyed millions of print books to train its AI model, Claude. This operation was part of a legally sanctioned fair use process, as detailed in recent court documents. The company hired Tom Turvey, formerly of Google Books, to lead the acquisition and digitization of these books, aiming to replicate Google's successful book digitization strategy.
The process involved purchasing used books in bulk, removing their bindings, and scanning the pages into digital formats, after which the physical copies were discarded. This method was deemed transformative by Judge William Alsup, who ruled that the operation qualified as fair use because Anthropic legally purchased the books and did not distribute the digital copies.
The decision highlights the AI industry's demand for high-quality text data, which is crucial for training large language models like Claude. These models rely on vast amounts of well-edited text to improve their capabilities, making the acquisition of such data a competitive necessity. Despite the legality of the process, Anthropic's earlier use of pirated materials had initially complicated its legal standing.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like AI Policy Brief or Daily AI Brief.
Also, consider following us on social media:
More from Regulation
Nov 1 East African Community adopts regional AI strategy and university training plan Nov 1 Türkiye Plans AI Scoring for Open Ended School Tests Nov 1 Sadiq Khan Calls for Cap on London Robotaxis Nov 1 SpaceX AI Loses Bid to Dismiss NAACP Air Pollution Lawsuit Oct 31 Canada Joins EuroHPC Supercomputing and Quantum NetworkAI Policy Brief
Weekly report on AI regulations, safety standards, government policies, and compliance requirements worldwide.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Canadian News Publishers Sue Anthropic Over Claude Training
Anthropic Automates Search for Training Data That Evades Safety Monitors
Anthropic publishes its first Public Benefit Report, including where it fell short
Anthropic Cuts Internet Access for Internal AI Evaluations
Anthropic Values Internal Claude Use Above Employee Pay
Daily AI Brief: the AI news that matters, in your inbox.