Visual artists fight back against AI companies for repurposing their work::Three visual artists are suing artificial intelligence image-generators to protect their copyrights and careers.

  • MentalEdge@sopuli.xyz
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    1 year ago

    I am able to answer your questions for myself. I have lost interest in doing so for you.

    • FooBarrington@lemmy.world
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      1 year ago

      But can you do so from the ground up, without handwaving towards the next unexplained reason? That’s what you’ve done here so far.

      • MentalEdge@sopuli.xyz
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        1 year ago

        Yes.

        I once held a view similar to the one you present now. I would consider my current opinion further advanced, like you do yours.

        You ask for elaboration and verbal definitions, I’ve been concise because I do not wish to spend time on this.

        It is clear we cannot proceed further without me doing so. I have decided I won’t.

          • MentalEdge@sopuli.xyz
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            1 year ago

            Not today. I have too much else to do.

            And it’s not like my being concise makes my argument absent.

            • FooBarrington@lemmy.world
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              1 year ago

              The issue isn’t you being concise, it’s throwing around words that don’t have a clear definition, and expecting your definition to be broadly shared. You keep referring to understanding, and yet objective evidence towards understanding is only met with “but it’s not creative”.

              • MentalEdge@sopuli.xyz
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                1 year ago

                Are you suggesting there is valid evidence modern ML models are capable of understanding?

                I don’t see how that could be true for any definition of the word.

                • FooBarrington@lemmy.world
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                  1 year ago

                  As I’ve shared 3 times already: Yes, there is valid evidence that modern ML models are capable of understanding. Why do I have to repeat it a fourth time?

                  I don’t see how that could be true for any definition of the word.

                  Then explain to me how it isn’t true given the evidence:

                  Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello. Although the network has no a priori knowledge of the game or its rules, we uncover evidence of an emergent nonlinear internal representation of the board state. Interventional experiments indicate this representation can be used to control the output of the network and create “latent saliency maps” that can help explain predictions in human terms.

                  https://arxiv.org/abs/2210.13382

                  I don’t see how an emergent nonlinear internal representation of the board state is anything besides “understanding” it.

                  • MentalEdge@sopuli.xyz
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                    1 year ago

                    Cool. But this is still stuff that has a “right” answer. Math. Math in the form of game rules, but still math.

                    I have seen no evidence that MLs can comprehend the abstract. To know, or more accurately, model, the human experience. It’s not even clear, that given a conscious entity, it is possible to communicate about being human to something non-human.

                    I am amazed, but not surprised, that you can explain a “system” to an LLM. However, doing the same for a concept, or human emotion, is not something I think is possible.