• irmoz@reddthat.com
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      3 months ago

      A person sees a piece of art and is inspired. They understand what they see, be it a rose bush to paint or a story beat to work on. This inspiration leads to actual decisions being made with a conscious aim to create art.

      An AI, on the other hand, sees a rose bush and adds it to its rose bush catalog, reads a story beat and adds to to its story database. These databases are then shuffled and things are picked out, with no mind involved whatsoever.

      A person knows why a rose bush is beautiful, and internalises that thought to create art. They know why a story beat is moving, and can draw out emotional connections. An AI can’t do either of these.

    • Prunebutt@slrpnk.net
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      3 months ago

      LLMs analyse their inputs and create a stochastic model (i.e.: a guess of how randomness is distributed in a domain) of which word comes next.

      Yes, it can help in a creative process, but so can literal noise. It can’t “be creative” in itself.

      • Even_Adder@lemmy.dbzer0.com
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        3 months ago

        How that preclude these models from being creative? Randomness within rules can be pretty creative. All life on earth is the result of selection on random mutations. Its output is way more structured and coherent than random noise. That’s not a good comparison at all.

        Either way, generative tools are a great way for the people using to create with, no model has to be creative on its own.

        • Prunebutt@slrpnk.net
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          3 months ago

          How that preclude these models from being creative?

          They lack intentionality, simple as that.

          Either way, generative tools are a great way for the people using to create with, no model has to be creative on its own.

          Yup, my original point still stands.