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Comment on OpenAI confirms that AI writing detectors don’t work

Eufalconimorph@discuss.tchncs.de ⁨1⁩ ⁨year⁩ ago

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.

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stevedidWHAT@lemmy.world ⁨1⁩ ⁨year⁩ ago
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
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- Theharpyeagle@lemmy.world ⁨1⁩ ⁨year⁩ ago
  See Generative Adversarial Network (GAN). Basically, making new AI detectors will always be harder than beating current ones. AI detectors have to somehow find a new “tell”, the target AI need only train itself on the output of the detector to figure out how to trick it.
  
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  - stevedidWHAT@lemmy.world ⁨1⁩ ⁨year⁩ ago
    ChatGPT isn’t a GAN network.
    
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- dack@lemmy.world ⁨1⁩ ⁨year⁩ ago
  At a very high level, training is something like:
  
  generate some output
  
  give the output a score based on how much it looks like real human text
  
  adjust the parameters slightly to improve the score
  
  repeat
  
  Step #2 is also exactly what an “AI detector” does. If someone is able to write code that reliably distinguishes between AI and human text, then AI developers would plug it in to that training step in order to improve their AI.
  
  In other words, if some theoretical machine perfectly “knows” the difference between generated and human text, then the same machine can also be used to make text that is indistinguishable from human text.
  
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  - stevedidWHAT@lemmy.world ⁨1⁩ ⁨year⁩ ago
    Exactly right, I mentioned this in a comment elsewhere but basically we can’t have our cake and eat it too.
    
    We can’t have a perfect NL impersonator that can also be detected as not NL. (Best case, obviously things are perfect for any AI model so technically detecting those mistakes could be used to help identify perhaps, but who’s to say what the FP rate would look like!)
    
    Ultimately the cat is out of the bag and I’m not quite sure there is anything we can do now. Ultimately some smart fingerprinting solution would be ideal but I just don’t know how feasible that would remain.
    
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