AI as a Compression Problem (dkg.fifthhorseman.net)

A recent article in The Atlantic brings attention to the notion that large language models (LLMs) may effectively compress large volumes of copyrighted text, as highlighted by the academic paper by Ahmed et al. This research reveals that significant portions of copyrighted materials can be extracted from LLMs, raising urgent questions about copyright implications. The article draws a parallel with the Stable Diffusion AI model, suggesting that LLMs could be viewed as a form of lossy textual compression, capable of encoding vast literary works within their parameters. The implications of this perspective are significant for the AI/ML community, particularly concerning copyright disputes and ethical responsibilities. With hypothetical models containing billions of parameters, it's conceivable they could encode millions of texts, albeit with some quality loss. This raises critical concerns about the potential for cultural homogenization and the broader social impacts of AI, overshadowing specific copyright claims. The article emphasizes the need for a more comprehensive discourse around the ethical and societal ramifications of AI technologies, much in the vein of Ted Chiang's earlier work on the subject.