131–140 of 317 posts
Earlier quoted context omitted.
> well it can’t do my dishes or clean the house the correct response would have been "yet"
Well, 30 years later and they still can't. At least I know of no one who has a house cleaning robot. (I wouldn't classify Roomba as 'clean my house').
How did "traditional search engines" get conflated with "looking for facts"? Is there data to back the idea that this is the most common use?
This is very well written, and probably one of my favorite takes on the whole ChatGPT thing. This sentence in particular: > Indeed, a useful criterion for gauging a large-language model’s quality might be the willingness of a company to use the text that it generates as training material for a new model. It seems obvious that future GPTs should not be trained on the current GPT's output, just as future DALL-Es should…
I don't find this a useful criterion. It is certainly something to worry about in the future as the snake begins to eat its own tail, but before we reach that point, we can certainly come up with actual useful criteria. First, what makes up "useful criteria"? Certainly it can't be "the willingness of a company to use the text that it generates as training material for a new model", because that is a hypothetical situation contingent on the future. So we should probably start with something like, well, is ChatGPT useful for anything in the present? And it turns out it is!
It's both a useful translator and a useful synthesizer.
When given an analytic prompt like, "turn this provided box score into an entertaining outline", it can reliably act as translator, because the facts about the game were in the prompt.
And when given a synthetic prompt like, "give me some quotes from the broadcasters", it can reliable act as a synthesizer, because in fact the transcript of the broadcasters were not in the prompt.
https://williamcotton.com/articles/chatgpt-and-the-analytic-...
Damn, I hate to plug products on HN, but I'd say that the New Yorker is the one subscription I've loved maintaining throughout my life. First got it right out of college and appreciate it 20 years later. Everyone is publishing think pieces about ChatGPT - yawn. But only the New Yorker said, hmm, how about if we get frickin' Ted Chiang to write a think piece? (It is predictably very well written.)
This quote from the article is something I genuinely fear: > "The rise of this type of repackaging is what makes it harder for us to find what we’re looking for online right now; the more that text generated by large-language models gets published on the Web, the more the Web becomes a blurrier version of itself." I am fearful that eventually AI led misinformation is going to be so widespread that it will be impossib…
Earlier quoted context omitted.
"Blurry JPEG" for how ChatGPT "compresses" character-based knowledge into vectors. That "compression" process gives ChatGPT an ability to generalize because it learns statistics (unlike JPEG) but like JPEG it is a lossy process.
It's a terrible analogy because the entire point of ML systems is to generalize well to new data, not to reproduce the original data as accurate as possible with a space/time tradeoff.
If it were going to produce ‘explainable’ correct answers for most of what it does that would be a matter of looking up the original sources to make sure they really say what it thinks they do. I mean, I can say, “there’s this paper that backs up my point” but I have to go look it up to get the exact citation at the very least.
Damn, I hate to plug products on HN, but I'd say that the New Yorker is the one subscription I've loved maintaining throughout my life. First got it right out of college and appreciate it 20 years later. Everyone is publishing think pieces about ChatGPT - yawn. But only the New Yorker said, hmm, how about if we get frickin' Ted Chiang to write a think piece? (It is predictably very well written.)
Damn too, actually. Reading the piece, I've been thinkking this publication really dserves to be subscribed to! Amazing write up. ChatGPT is a blurry JPEG of the internet.
Earlier quoted context omitted.
You don't need to correct every wrong thing you read. In fact you will probably feel much better if you don't ever do it at all, or at least take a break for while.
Very true, but it is hard not be frustrated reading the constant stream of confidently incorrect information about this topic. I left /r/programming similar reasons and it is sad to experience the same on HN.
Earlier quoted context omitted.
Very true :). It doesn’t help that this isn’t exactly a little blog post, it’s a popular New Yorker feature…
A published "real" news article like this is actually one of the more futile things to try to "correct" IMO. Some guy on a blog might publish a correction or change their view. The New Yorker probably won't (at least not based on an HN comment).
> Can large-language models take the place of traditional search engines? ... there’s the blurriness of outright fabrication, which we consider unacceptable when we’re looking for facts. How did "traditional search engines" get conflated with "looking for facts"? Is there data to back the idea that this is the most common use?
What else are they for? Even searching for designer shoes or celebrity gossip is an attempt to search for "facts". Or better put, a search engine offers you a set of hypotheses that it tries hard to keep closely correlated with reality. There are of course some searches that don't have definitive factual answers that are known, and a search engine ends up giving you references to the most popular discussions about it, i.e.:
https://www.google.com/search?q=is+the+universe+a+simulation...