On the other hand, I did recently find ChatGPT very useful when writing a string manipulation function in C++. I had to use some (to me) weird Windows APIs. ChatGPT wrote most of what ended up in my production code.
Some random person that's not going to get credited for their work wrote most of what ended up in your production code.
I haven’t seen more obvious examples since GitHub implemented a feature to prevent this from happening. I probably miss some tweets, but I assume it’s rare.
That does not mean other coding-optimized AI models won't be able to do much more, symbolic reasoning about the data, maintaining consistent variable and function names, understand libraries, respecting language constraints and invariants etc.
You might have moral qualms with this but you won’t find much of any support from the court system.
I'm sure courts will clear this mess up really soon, and I'm betting money the rulings won't subscribe to the "it's mine now" mantra of the AI crowd.
“In 1901, Edgar Purnell Hooley was walking in Denby, Derbyshire, when he noticed a smooth stretch of road close to an ironworks. He was informed that a barrel of tar had fallen onto the road and someone poured waste slag from the nearby furnaces to cover up the mess. Hooley noticed this unintentional resurfacing had solidified the road, and there was no rutting and no dust.”
I think the only thing that will chip away at this sentiment at this point is when the US federal court system rules in favor of GPT/et al, which seems very likely.
What a coincidence!
What’s strange is how little effort it would take for you to use these tools. What I don’t find strange is that you feel confident having an opinion about this regardless of your lack of experience.
It is becoming increasingly clear that there is a phase-change in behaviour when these models get large enough, such that they can solve new tasks outside of the training distribution.
See work by Hattie Zhou or Laura Ruiz for example.
It is clear to those following the research or using these models that they are not just copy-pasting… (Even if you can cherry pick examples were the LLM recalls highly occurring dataset items like fast square root or whatever).