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Stupidity has nothing to do with it. AI articles and comments are now posted everywhere and presented as human. It's becoming harder and harder to determine whe

Stupidity has nothing to do with it. AI articles and comments are now posted everywhere and presented as human. It's becoming harder and harder to determine whether text was written by humans or AI. Where are they supposed to find content to train AI on that isn't polluted with AI content that'll result in a feedback loop? It's like trying to get pure soil and water for growing that's not contaminated with microplastics/nanoplastics and PFAS. There was a time where it was possible. Not anymore. The filth is everywhere and impossible to filter. And it's simply not reasonable for AI companies to have human hands read through individual comments everywhere from beginning to end to build their training data. There isn't enough time in the universe to advance AI while doing that and also being accurate. Something will always slip through.

Cthulhu_ 7mo ago
If you can devise a tool that can detect AI generated content, you can use it to filter data. But the harsh truth is that "gold standard" training data is from before 2022 or whenever the cutoff was. And even that needs to be curated because before AI tools there was bot content filling up the internet. ...and even without bots, a lot of human authored content are low value, poorly written, etc. There are (probably) companies out there whose business is to create, curate and improve training sets.
- kdheiwns 7mo ago
  And if there's a way to detect that content is AI generated, then there's demand to generate content that seems more human. And we're already at a point where most people have been tricked into believing AI content was real at some point and never even realized it. It'll only get worse. Probably the only real way to validate content is real is building a validation system into devices. Confirm when a photo is taken and send an ID to a server, then when photos are shared, its ID is compared to the image on the camera/phone manufacturer's server. For text, validate every little key press. And there are still ways to game these systems, but I would not be surprised if they're introduced to mitigate AI diffusing everywhere.
eru 7mo ago
You lack imagination. Why would human review be the only possible way to remove enough of the tainted training data? > Where are they supposed to find content to train AI on that isn't polluted with AI content that'll result in a feedback loop? If nothing else: you could look for old data. At the moment, training assumes that input data is essentially without limit. But machine learning has lots and lots of old and proven techniques for what to do when your training data is limited. You can also look into techniques for avoiding model collapse. Just because one group of researcher showed that this happens with some specific models, doesn't mean it needs to happen in general.
- kdheiwns 7mo ago
  If AI data recognition can be automated, AI data that avoids recognition can be automated. It has nothing to do with imagination. It's simple reality.
  - eru 7mo ago
    You still lack imagination! Assume it's exactly the arm's race you suggest. So OpenAI develops a new AI-content detection today, and tomorrow someone brings out something that defeats it. In a month OpenAI brings out another new technique that is unbeaten for only a day. Rinse and repeat. Is that what you had in mind? In any case, OpenAI knows exactly what it crawled when. So if it has a technique that only got beaten at timestamp X, then everything they crawled up to is good to use.
    - kdheiwns 7mo ago
      That solves nothing because that's already what it's doing. The problem of training is adding new data beyond what's currently validated and into the future. And it'll get harder to validate it.
      - eru 7mo ago
        Huh? With the approach outlined above you can continuously keep adding new validated data. > And it'll get harder to validate it. Sure, and that's true in general: advances become harder, because we pick the low hanging fruit first anyway. Nothing new about that. The arms race I described is also only one approach. Model trainers will also want to investigate economising on training data, making their approaches more robust to model collapse, multi-modal training, and a million other strategies and tricks that I can't think of in thirty seconds.

Cthulhu_ 7mo ago

If you can devise a tool that can detect AI generated content, you can use it to filter data. But the harsh truth is that "gold standard" training data is from before 2022 or whenever the cutoff was. And even that needs to be curated because before AI tools there was bot content filling up the internet. ...and even without bots, a lot of human authored content are low value, poorly written, etc. There are (probably) companies out there whose business is to create, curate and improve training sets.

kdheiwns 7mo ago
And if there's a way to detect that content is AI generated, then there's demand to generate content that seems more human. And we're already at a point where most people have been tricked into believing AI content was real at some point and never even realized it. It'll only get worse. Probably the only real way to validate content is real is building a validation system into devices. Confirm when a photo is taken and send an ID to a server, then when photos are shared, its ID is compared to the image on the camera/phone manufacturer's server. For text, validate every little key press. And there are still ways to game these systems, but I would not be surprised if they're introduced to mitigate AI diffusing everywhere.

eru 7mo ago

You lack imagination. Why would human review be the only possible way to remove enough of the tainted training data? > Where are they supposed to find content to train AI on that isn't polluted with AI content that'll result in a feedback loop? If nothing else: you could look for old data. At the moment, training assumes that input data is essentially without limit. But machine learning has lots and lots of old and proven techniques for what to do when your training data is limited. You can also look into techniques for avoiding model collapse. Just because one group of researcher showed that this happens with some specific models, doesn't mean it needs to happen in general.

kdheiwns 7mo ago
If AI data recognition can be automated, AI data that avoids recognition can be automated. It has nothing to do with imagination. It's simple reality.
- eru 7mo ago
  You still lack imagination! Assume it's exactly the arm's race you suggest. So OpenAI develops a new AI-content detection today, and tomorrow someone brings out something that defeats it. In a month OpenAI brings out another new technique that is unbeaten for only a day. Rinse and repeat. Is that what you had in mind? In any case, OpenAI knows exactly what it crawled when. So if it has a technique that only got beaten at timestamp X, then everything they crawled up to is good to use.
  - kdheiwns 7mo ago
    That solves nothing because that's already what it's doing. The problem of training is adding new data beyond what's currently validated and into the future. And it'll get harder to validate it.
    - eru 7mo ago
      Huh? With the approach outlined above you can continuously keep adding new validated data. > And it'll get harder to validate it. Sure, and that's true in general: advances become harder, because we pick the low hanging fruit first anyway. Nothing new about that. The arms race I described is also only one approach. Model trainers will also want to investigate economising on training data, making their approaches more robust to model collapse, multi-modal training, and a million other strategies and tricks that I can't think of in thirty seconds.