AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source code created by developers over decades. However, they do so by allegedly stealing the open-source code stored in virtual libraries, known as repositories.

I might just be super biased but this article seems much more like personal musings than any sort of academic investigation - this description of how LLMs work isn’t even close. Also, you don’t get to say “allegedly” then just assume it’s true!

The authors here are, in turn:

- a lawyer/entrepreneur in copyright-related business (?) and a bunch of random shit like houses made out of mycelium

- the administrator who ran the US Air Force “accelerator” that paid for this research - supposedly an accomplished scientist in many fields but also how likely is it that they significantly contributed?

- an “air force judge advocate” who’s been named to investigate these issues and supposedly writes about technology

- the 2l law student who probably actually wrote the article and did all the work (typical, lol)

All of these people have impressive resumes, but none that make me question my initial assessment that they are going off their gut, rather than looking at what LLMs actually do during training

More importantly, all of this is moot and a waste of resources IMO - the LLMs are already trained well on language, and further improvements will not come from just plugging in a bigger corpus. I also really really don’t think Microsoft will have to crawl GitHub repos to teach GPT6 to program in python 4.0; generative examples, reading the spec, and agential systems make that concern moot.

More gripes because I’m feeling cranky any this paper makes me smile:

The rise of Generative Artificial Intelligence systems (“AI systems”) parallels the Greek myth of Pandora who was overwhelmed with curiosity and opened the Box “[r]eleasing curses upon mankind.” Pandora’s Box is not solely about evil or curses as the artifact-looking Box included Elpis, the personified spirit of Hope, and is a clear reminder that a lot of good can come out of the development of AI systems.
To add some more context to the analogies here, AI systems can be thought of as: “[t]he monster plant Audrey II in Little Shop of Horrors, constantly crying out ‘Feed me!’”Why? Because ChatGPT and other AI systems provide a natural language response (output) to questions or requests by accessing vast libraries of content created over decades.

Are we allowed to start academic literature like that…? That’s goofy af

AI systems trained on code are nothing more than detection calculators or processors that can suggest or simulate statistical patterns, which is certainly not the equivalent of human-like reasoning.

They quote an argument for this that I can’t find so fair enough, but regardless can we agree that “certainly” is misused here?! Let’s stick with “apparently” or “we argue”

AI systems are certainly here to stay and will replicate as the mythical Lernaean Hydra,

which seems to support the idea that the only way to defeat the monster plant is to cut off its food supply.

Literally the only reason I read this paper was to figure out what a lenaean hydra is, and AFAICT it’s just mentioned this one time in the conclusion. And for all that, the metaphor seems… random? “LLMs are like hydras because we can only kill them through starvation” doesn’t relate very clearly to the actual thesis, which is “the law should mandate that companies pay individuals for the use of their content in AI training sets”