Previous posts: https://programming.dev/post/3974121 and https://programming.dev/post/3974080

Original survey link: https://forms.gle/7Bu3Tyi5fufmY8Vc8

Thanks for all the answers, here are the results for the survey in case you were wondering how you did!

Edit: People working in CS or a related field have a 9.59 avg score while the people that aren’t have a 9.61 avg.

People that have used AI image generators before got a 9.70 avg, while people that haven’t have a 9.39 avg score.

Edit 2: The data has slightly changed! Over 1,000 people have submitted results since posting this image, check the dataset to see live results. Be aware that many people saw the image and comments before submitting, so they’ve gotten spoiled on some results, which may be leading to a higher average recently: https://docs.google.com/spreadsheets/d/1MkuZG2MiGj-77PGkuCAM3Btb1_Lb4TFEx8tTZKiOoYI

  • SpziEnglish
    2•1 year ago

    Right, thanks for the corrections.

    In case of GAN, it’s stupidly simple why AI detection does not take off. It can only be half a cycle ahead (or behind), at any time.

    Better AI detectors train better AI generators. So while technically for a brief moment in time the advantage exists, the gap is immediately closed again by the other side; they train in tandem.

    This does not tell us anything about non-GAN though, I think. And most AI is not GAN, right?

    • @ilinamoratoEnglish
      2•1 year ago

      True, at least currently. Image generators are mostly diffusion models, and LLMs are largely GPTs.