| I disagree. I don’t care much about what is profound. I think most of it is not. Things that we call profound are really just astute observations of patterns in the real world, and there’s nothing wrong with that. However profundity doesn’t need to factor into the debate of whether ai should or should not be allowed to train on things. If we allow humans to copy things, then Humans ought to be allowed to copy things with dumb non sentient ai too. Ai in the current state is just a tool, much like a paint brush. Cue the inevitable appeal to copying exact works, rebuttals to training on human painted mimicries and then bam, you’ve got the authors special style learned by the model with extra steps. It’s annoying and pointless. Art that is merely visually intriguing is not very interesting. If an artist makes something without a particular idea to communicate, it’s just aesthetics. It is not profound. If an artist has an idea and creates a work that represents it, then maybe it is profound. But it doesn’t matter if it was made with paint or a computer. The idea is the profound thing. AI is not sentient. It’s still the user. The appeals to pareidolia are wrong. Synthesis of ideas from past data is natural. But the AI does not choose things. What you’re really complaining about is creation of art from apparent randomness. Not the AI model alone but monkeys on a typewriter getting something compelling from the AI. What do we do when the tools are so powerful that a monkey creates a profound work that the monkey doesn’t understand? Shrug. |
> The appeals to pareidolia are wrong. Synthesis of ideas from past data is natural. But the AI does not choose things. What you’re really complaining about is creation of art from apparent randomness. Not the AI model alone but monkeys on a typewriter getting something compelling from the AI.
No, you've failed to understand what I'm saying entirely (because, again, you've responded to some other post that only exists in your mind).
What I'm talking about is intention and its relationship to meaning, in the philosophical sense (and not... copyright or whatever it is you're rambling on about).
Witness: when ChatGPT famously mis-asserts the number of characters in a word (say, that there are twelve characters in the word "thirteen"), it's not that it's trying and failing to count, because it's confused by letter forms or its attention wanders like a 3 year old or its internal representation of countable sets glitches around the number 8 or something – it never counted anything at all, it's simply the case that twelve is the most statistically likely set of tokens corresponding to that input prompt per its training set. And when it produces a factually correct result (say, "there are 81 words in the first sentence of the declaration of independence"), it produces it for exactly the same reason – not because it has counted the words and formed an internal representation and intends to mean its internal understanding, but simply because 81 is the most statistically likely set of tokens corresponding to that prompt per its training set.
And yet when it produces these correct results, people ooh and aah over how "smart" it is, how much it has "understood", how "good it is at counting; better than my son!", and when it produces incorrect results people deride it as dumb and so forth, and and all of this, all of this, is pareidolia; it is neither smart in the one case nor dumb in the other, it does not learn in the sense the word is normally used, it does no counting. We're anthropomorphizing an algorithm that is doing nothing like what we imagine it to do, because we mistake the statistical order in its expressions for the presence of a meaning intended by those expressions. It's all projection on our end.