In relayed news, a recent study that concluded I am, not just the smartest person in the universe but also the smartest that has every been or will ever be.
I recently did a AI study that concluded that I am not only the cutest catgirl on lemmy but deserve free unlimited hrt :3
Well, a broken clock is right twice a day.
Your typos and use of commas betrays you, fake study
Fake news!
Youβre absolutely right!
β Hereβs why it matters:
ALL HAIL DONALD TRUMP
"the idea is tantalizing"
No the fuck it isn't, and that's not even a Fuck AI type opinion just basic fucking scientific principles
Lying, cheating, stealing, exploitation and propaganda all sound "tantalizing" when you're a criminally corrupt sociopath.
We're just lucky capitalism doesn't reward sociopaths with wealth and power /s
Alt text.
A recent Axios story on maternal health policy referenced "findings" that a majority of people trusted their doctors and nurses. On the surface, there's nothing unusual about that. What wasn't originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor's note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it's suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.
They were so busy thinking about the fact that they could that they didn't stop to think if they should. How much of an idiot can you be?
I dont know the Axis was ever the most trustworthy source out there but if they're doing this then less trustworthy sources are also doing it.
Axios updated the story:
Editor's note: This story has been updated to note that Aaru is an AI simulation research firm.
But still stands by their claim:
New findings by Aaru, an AI simulation research firm, for Heartland Forward show that a majority of people trust their own doctors and nurses
What kind of bullshit "fact checking" is this?
"New findings by Smegma, an Xbox chatroom research firm, show that your mother is a woman of loose morals who has had sexual intercourse with dozens of Xbox gamers."
It's ironic that the survey companies, who I thought wanted to avoid noise and bullshit, would pay for noise and bullshit that any RNG could fill.
wtf...
Wasnt it axios that had that controversy recently where some github admin ended up in a flame war with an ai, and axios made up quotes?
Or was that someone else?
Yes, but how much of the training data is synthetic data? Because I expect this startup has no idea. Microsoft uses ML to crawl files on OneDrive to build aggregate models of document types, then use that for LLM training.
It's just all slop all the way down, huh? Just a fuzzy picture of a fuzzy picture hit with the "sharpen" filter 20 times?
I instantly thought "fuck no, this can't be true", then read the AI part.
I was interested in this idea, because although LLMs are not good at many things, what they absolutely are good at is taking large data sets of writing and finding a kind of "average" of that data. I can understand why this would make sense. I think it's a situation where the further you go from the training set the less reliable your "silicon sample" will be, because it has less and less relevant information to draw from, but I can also kind of see it working in some circumstances.
So, anyway, I have done a little research into this and the concept does show some definite promise. I think this is the study that kicked off the concept, and their results are quite impressive. GPT-3 manages to be close to human respondents on a variety of topics and in a variety of contexts (guessing preferences, tone, word choices, etc).
There are some issues I don't see addressed:
One important part from the article:
These studies suggest that after establishing algorithmic fidelity in a given model for a given topic/domain, researchers can leverage the insights gained from simulated, silicon samples to pilot different question wording, triage different types of measures, identify key relationships to evaluate more closely, and come up with analysis plans prior to collecting any data with human participants.
"Algorithmic fidelity" is a term that I think they have coined in this paper, it refers to how accurately the model reflects the population you are sampling. Roughly what they suggest is - take a known dataset of the population you want to assess, in the general area you are researching, and compare the real results of that with the LLM results. If this is successful you have an indication that the model can predict the population/area of interest, and you can adjust your questions to your specific topic. They don't really highlight enough that without this your results could just be completely bogus. Who knows what this company Aaru are doing.
I do think this is quite an interesting and potentially promising use of the technology. Despite the fact it might on the surface seem to be just "inventing" data, in a way the LLM has already surveyed many more heads than any "real" survey ever could hope to. I would like to see more research before being sure of any of this though, I'm certainly going to continue reading about it to see what limitations there are beyond my first assumptions. GPT-3 is not the latest model, and I wonder about how much AI generated content is out there now... Are the later generations of models starting to eat their own tails? There's obvious manipulation of online conversations through bots, could someone poison the well in this way and cause these "surveys" to produce skewed results?___
No, even in the absolute best case scenario, the LLM analysis is a trailing indicator. There's no way that it indicates current views, just possibly an indication of past views.
Personally I think this entire line of thinking ("silicon sampling") is dangerous af.
That's a good point, although I imagine a dedicated company could refine a model using more recently sampled general data to improve the recency.
Yeah, I'm not saying a tool akin to LLMs can't be used as part of a suite of software workflows for parsing through and analyzing large datasets (seems rather obvious to say that), but forgoing the real work of live data gathering and statistics evaluation in order to do a sort of "vibe polling" sounds extremely off to me.
I agree, which is why I find the results they got interesting, the fact that the initial study was able to, arguably quite correctly (well, debatable if it was correct, as I pointed out their results are not the easiest to evaluate), predict real results is pretty impressive.
I'm eagerly waiting more studies on AI psychosis. Make sure to participate if you get the chance.
I think I was overall pretty critical of the idea? I just find it interesting.
It seems like the kind of thing that could eventually be useful for helping to survey companies figure out how to word surveys and which surveys are even worth doing for a given group, rather than replacing the surveys themselves. Unfortunately it seems like the companies currently just want to replace the actually useful product with ai slop, as per usual
Yes, it can obviously never entirely replace real surveys. I would assume that survey results forming a part of the training set is a big part of why they're able to get good results in the first place, and as I said I think its a significant risk that the evaluation is done it performs well because the data being evaluated against are (unbeknownst to the researcher) present in the training set.
The "average" they're finding is an average of the training set. That can't actually apply to public opinion polls because the data in the set is going to be biased towards people that express their opinions. There's already polling bias towards people who are likely to answer polling questions, now imagine that bias being applied to the loudest, most opinionated, most prolific posters.
Yes that's definitely also an issue, although as you point out is already an issue with public opinion polling. I'm not sure how you would evaluate how much of a gap there is between the two.
It's worse, because pollsters at least go out and solicit opinions from people who might not otherwise express their opinions.
An LLM is collecting opinions from people who happily and freely share their opinions without even being prompted, and completely ignoring people who don't post their opinion. No attempt is made to account for the people who don't post opinions because no one reaches out to them, they're invisible. I don't think there's even a way to account for this, it's just inherently busted.
Wtf
What an interesting but absolutely horrible idea.
"We did it, Patrick! We made a technological breakthrough!"
A place for all those who loathe AI to discuss things, post articles, and ridicule the AI hype. Proud supporter of working people. And proud booer of SXSW 2024.
AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.
