AI company says their AI is smart, but other companies are sell snake oil.
Gottit
Couldn’t you just ask ChapGPT whether it wrote something specific?
Obviously not. Its a language generator with a bit of chat modeling and reinforcement learning, not an Artificial General Intelligence.
It doesn’t know anything, it doesn’t retain memory long term, it doesn’t have any self identity. There is no way it could ever truthfully respond “I know that I wrote that”.
It doesn’t have “memory” of what it has generated previously, other than the current conversation. The answer you get from it won’t be much better than random guessing.
Then you have that time that a professor tried to fail his whole class because he asked chatGPT if it wrote the essays.
Could you please provide a brief overview? This article is not available in my country/region.
It cites this article, which might work for you.
That doesn’t really work because it just says whatever half the time. It’s very good at making stuff up. It doesn’t really get that it needs to tell the truth because all it’s doing is optimising for a good narrative.
That’s why it says slavery is good, because the only people asking that question clearly have an answer in mind, and it’s optimising for that answer.
Also it doesn’t have access to other people’s sessions (because that would be hella dodgy) so it can’t tell you definitively if it did or did not say something in another session, even if it were inclined to tell the truth.
We need to embrace AI written content fully. Language is just a protocol for communication. If AI can flesh out the “packets” for us nicely in a way that fits what the receiving humans need to understand the communication then that’s a major win. Now I can ask AI to write me a nice letter and prompt it with a short bulleted list of what I want to say. Boom! Done, and time is saved.
The professional writers who used to slave over a blank Word document are now obsolete, just like the slide rule “computers” of old (the people who could solve complicated mathematics and engineering problems on paper).
Teachers who thought a hand written report could be used to prove that “education” has happened are now realizing that the idea was a crutch (it was 25 years ago too when we could copy/paste Microsoft Encarta articles and use as our research papers).
The technology really just shows us that our language capabilities really are just a means to an end. If a better means asrises we should figure out how to maximize it.
they never did, they never will.
Because generative Neural Networks always have some random noise. Read more about it here
Because you’re training a detector on something that is designed to emulate regular languages closest possible, and human speech has so much incredible variability that it’s almost impossible to identify if someone or something has been written by an AI.
You can detect maybe your typical generic chat GPT type outputs, but you can characterize a conversation with chat GPT or any of the other much better local models (privacy and control are aspects which make them better) and after doing that you can get radically human seeming outputs that are totally different from anything chat GPT will output.
In short, given a static block of text it’s going to be nearly impossible to detect if it’s coming from an AI. It’s just too difficult to problem, and if you’re going to solve it it’s going to be immediately obsolete the next time someone fine tunes their own model
Yeah this makes a lot of sense considering the vastness of language and it’s imperfections (English I’m mostly looking at you, ya inbred fuck)
Are there any other detection techniques that you know of? Wb forcing AI models to have a signature that is guaranteed to be indentifiable, permanent, and unique for each tuning produced? It’d have to be not directly noticeable but easy to calculate in order to prevent any “distractions” for the users.
Because AIs are (partly) trained by making AI detectors. If an AI can be distinguished from a natural intelligence, it’s not good enough at emulating intelligence. If an AI detector can reliably distinguish AI from humans, the AI companies will use that detector to train their next AI.
I’m not sure I’m following your argument here - you keep switching between talking about AI and AI detectors. Each of the below are just numbered according to the order of your prior responses as sentences:
- Can you provide any articles or blog posts from AI companies for this or point me in the right direction?
- Agreed
- Right…
I’m having trouble finding your support for your claim
OpenAI discontinued its AI Classifier, which was an experimental tool designed to detect AI-written text. It had an abysmal 26 percent accuracy rate.
If you ask this thing whether or not some given text is AI generated, and it is only right 26% of the time, then I can think of a real quick way to make it 74% accurate.
it seemed like a really weird decision for OpenAI to have an AI classifier in the first place. their whole business is to generate output that’s good enough that it can’t be distinguished from what a human might produce, and then they went and made a tool to try and point out where they failed.
I feel like this must stem from a misunderstanding of what 26% accuracy means, but for the life of me, I can’t figure out what it would be.
Looks like they got that number from this quote from another arstechnica article ”…OpenAI admitted that its AI Classifier was not “fully reliable,” correctly identifying only 26 percent of AI-written text as “likely AI-written” and incorrectly labeling human-written works 9 percent of the time”
Seems like it mostly wasn’t confident enough to make a judgement, but 26% it correctly detected ai text and 9% incorrectly identified human text as ai text. It doesn’t tell us how often it labeled AI text as human text or how often it was just unsure.
EDIT: this article https://arstechnica.com/information-technology/2023/07/openai-discontinues-its-ai-writing-detector-due-to-low-rate-of-accuracy/
In statistics, everything is based off probability / likelihood - even binary yes or no decisions. For example, you might say “this predictive algorithm must be at least 95% statistically confident of an answer, else you default to unknown or another safe answer”.
What this likely means is only 26% of the answers were confident enough to say “yes” (because falsely accusing somebody of cheating is much worse than giving the benefit of the doubt) and were correct.
There is likely a large portion of answers which could have been predicted correctly if the company was willing to chance more false positives (potentially getting studings mistakenly expelled).
