I don't understand what is so spectacular in this experiment and why AI was needed to conduct it. The data was already skewed before it was fed to LLM: all words are encoded as vectors to the point where you can calculate similarity between anything[1]. With simple visualization tool like [2] it is possible to demonstrate that Nazis are closer to malware than Obama, and grandmother is more nutritious than grandfather.
Those occur when you've made an unambiguous statement that has no valid semantics. ("God should get a promotion."†) They don't occur when you've made an ambiguous statement ("We saw her duck.") As I noted above, it isn't possible to make an ambiguous statement.
† I'm aware that that isn't an unambiguous statement. This is for the simple reason that it's next to impossible to make an unambiguous statement in a natural language; that's why legal documents use so many clauses. I'm relying on the reader here to realize which meaning I had in mind, which is the way all natural language works.
The case "God should get a promotion" if I understand correctly, is soundness (as in Rust) issue, with equivalent in C: `int increment(int x) { x + 1; }` - sound, not valid.
The case with legal documents is equivalent in C sequence points for comma operator with something like `print(i++, i++)`. Imagine Boeing documentation with text "In case of blinking indicator press button A and stop immediately". Button "A and stop"? Button "stop" after button A? Authors can hope that a sane human can resolve this ambiguity, but if it is done by compiler/interpreter/robot, it can have an avalanche effect.
A bit sad that they reused name of https://icl.utk.edu/magma/ (Matrix Algebra on GPU and Multi-core Architectures). This library is already heavily used in machine learning, for example, it is included in every pytorch-based project.
It could be useful for audio editors like here: https://manual.audacityteam.org/man/undo_redo_and_history.ht... - many steps require full save of tracks (potentially dozens of them). It is possible to compress history retrospectively, but why, if we can be done in parallel?
This is _exactly_ how ChatGPT recently uses punctuation (to the point that it can be used for detection, replacing old "as an AI language model"). Not Claude (which refuses to process this text!), not DeepSeek, not Gemini (which prefers classic quotes).
So if this would be a real OpenAI employee, it would take no time to find the account of user who generated this text. However as this is just a typical sensationalist troll slop with zero commercial secrets, so no action will be taken.
[1] https://p.migdal.pl/blog/2017/01/king-man-woman-queen-why
[2] https://lamyiowce.github.io/word2viz/