New APPS: Art, Politics, Philosophy, Science

Another Platonism in LLM Debates, Part 2. The Wittgensteinian strategy

Last time, I started a look at the work of the early AI researcher Margaret Masterson of the Cambridge Language Research Unit (CLRU).  As demonstrated by Lydia Liu in a pair of articles (here and here), Masterson proceeded from Wittgenstein to a thorough deconstruction of traditional ideas of word meaning, moving instead to treating meaning as a function of a word’s associations, as we might find in a thesaurus.  This approach is a clear forerunner to the distributed view of language applied in current LLMs.  Here I’ll outline the basics of the Masterman approach and show how it applies to LLMs.

Masterson’s starting point is a Wittgensteinian point about the distinction between a word and a pattern. Counting with words would be “one, two three.”  Counting with patterns would be “-, --, ---.”  But what if we counted “one, one one, one one one.”  Can words function as patterns?  Masterman applies the thought to the classical Chinese character “zi” (字, which I’ll write here as “zi”), the meaning of which depends on its context and placement in a given text.  Thus, “for Masterman, the zi is what makes the general and abstract category of the written sign possible, for not only does the zi override the Wittgensteinian distinction of word and pattern, but it also renders the distinction of word and nonword superfluous” (Witt., 442).

AI and Copyright: Training Data and Transformative Fair Use

I’ve been loosely tracking the AI and copyright cases, most notably the Thaler litigation, where Thaler keeps losing the argument that work solely by an AI should get copyright protection.  To summarize: everybody who has ruled on that said that only work involving humans can get copyright protection.  As I said at the time, I think a good policy reason in support of this argument is that if pure AI could get copyright, it could produce millions of copyrighted images in almost zero time.  That’s got nothing to do with incentivizing human creation.  It was easy to miss given the deluge of atrocious Supreme Court decisions, but last week, a pair of district court judges ruled on a different (but not unrelated, in terms of markets) AI copyright question – whether scraping online text for training data is fair use.  Both cases are in the Northern District of California, so we can expect the 9^th Circuit to have the first appellate decision on this topic.

By way of background: fair use is an affirmative defense against copyright infringement.  That means that if you accuse me of infringement, I can defend myself as having engaged in “fair use,” which basically means “use that the copyright owner doesn’t like, but that we as a society think should be allowed for policy reasons.”  It could also mean “use that everybody thinks is ok, but for which licensing would be so inefficient that a licensing market would never emerge.”  Fair use is supposed to be decided case-by-case.  It depends on four factors: the “nature and purpose” of the (allegedly infringing) use, the nature of the copyrighted work, the amount of it used, and the market effects of the infringing use.  The middle two factors tend not to matter much.  The first factor is usually decided by a deciding whether the use in question is “transformative.”  For example, consider parody; the Supreme Court ruled back in 1994 that a 2 Live Crew parody of Roy Orbison’s “Pretty Woman.” was fair use.  The most closely analogous case I know to the training data was an appellate decision about Google thumbnails.