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Joined 1 year ago
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Cake day: June 16th, 2023

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  • I think even that goes back around to business interests. We can’t store that many physical copies in shrinking, expensive housing. Digital purchasable media is somehow just as expensive despite having tiny manufacturing and logistical costs, on top of being unreliable due to DRM.

    Subscriptions so far seemed like a better value proposition but between splitting and vanishing libraries, increasing prices and the addition of ads, that’s becoming more questionable. Even average people aren’t so thrilled of having to subscribe to a dozen different services to watch, listen and play what they want.



  • This result is clearly wrong, but it’s a little more complicated than saying that adding inclusivity is purposedly training it wrong.

    Say, if “entrepreneur” only generated images of white men, and “nurse” only generated images of white women, then that wouldn’t be right either, it would just be reproducing and magnifying human biases. Yet this a sort of thing that AI does a lot, because AI is a pattern recognition tool inherently inclined to collapse data into an average, and data sets seldom have equal or proportional samples for every single thing. Human biases affect how many images we have of each group of people.

    It’s not even just limited to image generation AIs. Black people often bring up how facial recognition technology is much spottier to them because the training data and even the camera technology was tuned and tested mainly for white people. Usually that’s not even done deliberately, but it happens because of who gets to work on it and where it gets tested.

    Of course, secretly adding “diverse” to every prompt is also a poor solution. The real solution here is providing more contextual data. Unfortunately, clearly, the AI is not able to determine these things by itself.