This is purely ignorant magical thinking.
You're ignoring the very real facts of how ChatGPT works and fabricating a fantasy that you prefer to believe.
In any case, the point is that it's an objective thing anyone can observe: compare the two outputs. See if they differ.
> This is purely ignorant magical thinking.
I counter that you ascribe "magical" thinking to the human condition.
GPT is remarkable to me not for its failings, but because of how far it can get when it's only trying to win the game of "predict what token comes next".
GPT simply gives you a complete hallucination. This can be gotten around imperfectly with prompt engineering to get GPT to admit it doesn't know. But still, hallucinations are pretty dangerous failure modes in many applications, and definitely something we don't want in a future iteration. I don't know how much of a change it is to create a GPT without hallucinations, but I suspect that it is non-trival.
It's a fallacy to anthropomorphize a pile of linear algebra, relate it to a scale of human development, and extrapolate that AI is on a similar trajectory of progress/potential.
Relevant xkcd: https://xkcd.com/605/
If a parrot's brain grew exponentially every year for 20 years, then I might expect it to. :)
> It's a fallacy to anthropomorphize a pile of linear algebra, relate it to a scale of human development, and extrapolate that AI is on a similar trajectory of progress/potential.
I don't believe I said any of those things.
I don't know about parrots, but if you could take a corvid and grant it the same rate of improvement in brain size and training input that these models are receiving, I wouldn't be the least bit surprised to see it doing calculus within 10-20 years.
None invented their own algorithm and confidently claimed it to be the way.
Though they are definitely better at saying I don't know than ChatGPT et al, which have basically been trained to never admit they don't know and always bullshit instead.
How so?
It is a very impressive accomplishment what a large language model can do. It can piece together coherent text from a Google-sized corpus. But I don't thinking describing the process as "reason" is a useful description.
For a subject as purely logical as AI is, it sure seems to draw a in a lot of wishful thinking.