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this post was submitted on 24 Oct 2023
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Don't get me wrong, it's incredible. But it's still a variation of the Chinese room experiment, it's not a real intelligence, but really good at pretending to be one. I might trust it more if there were variants based on strictly controlled datasets.
I have read more than is probably healthy about the Chinese room and variants since it was first published. I've gone back and forth on several ideas:
Since the advent of ChatGPT, or, more properly, my awareness of it, the confusion has only increased. My current thinking, which is by no means robust, is that humans may be little more than "meatGPT" systems. Admittedly, that is probably a cynical reaction to my sense that a lot of people seem to be running on automatic a lot of the time combined with an awareness that nearly everything new is built on top of or a variation on what came before.
I don't use ChatGPT for anything (yet) for the same reasons I don't depend too heavily on advice from others:
I've not yet seen anything to suggest that ChatGPT is reliably any better than a bullshitter. Which is not nothing, I guess, but is at least a little dangerous.
what often puts me of that people almost never fakt check me when i tell them something wich also tells me they wouldn't do the same with chatgpt
So theoretically could you program an AI using strictly verified programming textbooks/research etc, is it currently possible to make an AI that would do far better at programming? I love the concepts around AI but I know fuckall about ML and the actual intricacies of it. So sorry if it's a dumb question.
Yeah, this is the approach people are trying to take more now, the problem is generally amount of that data needed and verifying it's high quality in the first place, but these systems are positive feedback loops both in training and in use. If you train on higher quality code, it will write higher quality code, but be less able to handle edge cases or potentially complete code in a salient way that wasn't at the same quality bar or style as the training code.
On the use side, if you provide higher quality code as input when prompting, it is more likely to predict higher quality code because it's continuing what was written. Using standard approaches, documenting, just generally following good practice with code before sending it to the LLM will majorly improve results.
Interesting, that makes sense. Thank you for such a thoughtful response.
The Chinese room thought experiment doesn't prove anything and probably confuses the discussion more than it clarifies.
In order for the Chinese room to convince an outside observer of knowing Chinese like a person the room as a whole basically needs to be sentient and understand Chinese. The person in the room doesn't need to understand Chinese. "The room" understands Chinese.
The confounding part is the book, pen and paper. It suggests that the room is "dumb". But to behave like a person the person-not-knowing-Chinese plus book and paper needs to be able to memorize and reason about very complex concepts. You can do that with pen and paper and not-understanding-Chinese person it just takes an awful amount of time and complex set of continuously changing rules in said book.
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