serhei
Joined 19 years ago
Joined 19 years ago
This one was interesting to see, because cases like this make the difference between luddite exclusion (anything that transitively comes from 'AI' is spiritually-tainted bits) and accurate disclosure (LLM output is distinguished from human product). Either policy is a valid choice, but lumping the two together as equally 'AI-assisted' tends to confuse rather than help.
This is similar to the 'no gray areas' problem with compiling a list such as https://codeberg.org/ethical-foss/ai-free-foss-alternatives repo and needing a complex system of tags to avoid mixing projects that have measured/conservative stances short of full exclusion, with projects that have gone full slop-cannon.
The correct way to think about this:
The hard part is ensuring the product does not exhibit undesired behaviours. Writing code is one possible tool for ensuring this. Replacing writing code with LLMs only works to the extent that LLMs accurately predict your definition of 'undesired'. The more unique and innovative your product, the more likely the LLM prediction is to be inaccurate.
"Clearly the ability to eliminate opposing viewpoints was a huge part of the Chinese government's decision, but was far from the only one. Stability and economic prosperity are also very important. My grandma can obey simple laws. But telling her to go to research contradictory political perspectives and decide the correct course of action is a completely different story. Not to mention how many people will just vote for the candidate that pushes their emotional buttons, and read whatever crap on Facebook and click next, next, next until they've elected Donald Trump."
[1]: https://www.folklore.org/StoryView.py?story=Diagnostic_Port.... -- "[Steve] would also rather have them buy a new 512K Mac instead of them buying more RAM from a third-party."
Really, Lake Ontario makes any NY-Detroit train line much longer than it could be otherwise.