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

  • Spzi@lemm.eeEnglish
    1·
    1 year ago

    And this is why AI detector software is probably impossible.

    What exactly is “this”?

    Just about everything we make computers do is something we’re also capable of; slower, yes, and probably less accurately or with some other downside, but we can do it. We at least know how.

    There are things computers can do better than humans, like memorizing, or precision (also both combined). For all the rest, while I agree in theory we could be on par, in practice it matters a lot that things happen in reality. There often is only a finite window to analyze and react and if you’re slower, it’s as good as if you knew nothing. Being good / being able to do something often means doing it in time.

    We can’t program software or train neutral networks to do something that we have no idea how to do.

    Machine learning does that. We don’t know how all these layers and neurons work, we could not build the network from scratch. We cannot engineer/build/create the correct weights, but we can approach them in training.

    Also look at Generative Adversarial Networks (GANs). The adversarial part is literally to train a network to detect bad AI generated output, and tweak the generative part based on that error to produce better output, rinse and repeat. Note this by definition includes a (specific) AI detector software, it requires it to work.