text:  For instance, AI researchers note that the recently released Common Corpus dataset is an “invaluable 
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  resource” but “comes with limitations. A lot of public domain data is antiquated—in the US, for example, 
  copyright protection usually lasts over seventy years from the death of the author—so this type of 
  dataset won’t be able to ground an AI model in current affairs or, say, how to spin up a blog post using 
  current slang” and the “dataset is tiny.” Thus, while it is possible to train an AI model on the data, those 
  models will have more limited utility on some dimensions than current frontier models trained on a 
  broader array of data. See Knibbs, Kate,*Here’s Proof You Can Train an AI Model Without Slurping*
  *Copyrighted Content | WIRED*. (2024, March 20), at https://www.wired.com/story/proof-you-can-train-ai- 
  without-slurping-copyrighted-content/. 
  
   Our workshop discussion did note that some widely available datasets for AI training have also 
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  pursued more direct licensing agreements. For instance, the SILO LLM was created by working with 
  scientiﬁc journal publishers to make works available for both download and AI training. While this might 
  be viable in the context of particular, narrow classes of works, the barriers to eﬃcient licensing 
  mentioned above would remain a problem for any broader efforts. See Min, Sewon, et al. “SILO 
  Language Models: Isolating Legal Risk in a Nonparametric Datastore.”*ArXiv (Cornell University)*, 8 Aug. 
  2023, https://doi.org/10.48550/arxiv.2308.04430. Accessed 14 Dec. 2023. 