Skip Navigation

InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)PH
Posts
1
Comments
314
Joined
2 yr. ago

  • You say that yet I initially responded to someone who was comparing an LLM to what a comedian does.

    There is no unique method because there's hardly anything unique you can do. Two people using Stable Diffusion to produce an image are putting in the same amount of work. One might put more time into crafting the right prompt but that's not work you're doing.

    If 90% of the work is handled by the model, and you just layer on whatever extra thing you wanted, that doesn't mean you created the thing. That also implies you have much control over the output. You're effectively negotiating with this machine to produce what you want.

  • Yeah but the difference is we still choose our words. We can still alter sentences on the fly. I can think of a sentence and understand verbs go after the subject but I still have the cognition to alter the sentence to have the effect I want. The thing lacking in LLMs is intent and I'm yet to see anyone tell me why a generative model decides to have more than 6 fingers. As humans we know hands generally have five fingers and there's a group of people who don't so unless we wanted to draw a person with a different number of fingers, we could. A generative art model can't help itself from drawing multiple fingers because all it understands is that "finger + finger = hand" but it has no concept on when to stop.

  • A comedian isn't forming a sentence based on what the most probable word is going to appear after the previous one. This is such a bullshit argument that reduces human competency to "monkey see thing to draw thing" and completely overlooks the craft and intent behind creative works. Do you know why ChatGPT uses certain words over others? Probability. It decided as a result of its training that one word would appear after the previous in certain contexts. It absolutely doesn't take into account things like "maybe this word would be better here because the sound and syllables maintains the flow of the sentence".

    Baffling takes from people who don't know what they're talking about.

  • The problem isn't the misinformation itself, it's the rate at which misinformation is produced. Generative models lower the barrier to entry so anyone in their living room somewhere can make deepfakes of your favourite politician. The blame isn't on AI for creating misinformation, it's for making the situation worse.

  • What I've learned is that people don't like to be told what to do even if it is for the benefit of those around them. I've seen it mostly in the US where the mask mandate was seen as an attack on personal freedoms and not a response to a public health crisis. As usual, people thinking they are the main characters. Just put the mask on and stfu, no one cares.