2 ms·

Determinism is not a copyright violation. ("If a human artist...")

mjburgess 4y ago
Right, but underneath your premise is a scam, right? A NN has not learnt to paint: it doesn't coordinate its sensory-motor system with its environment through play, it hasn't developed any taste, it does not discern the aesthetic good from the bad, it has no judgement, and so on ad infinitum. A NN is just a kNN with an extra compression step. The way all gradient based "learners" work is to compute distances to pregiven training data. In the case of kNN that data is used exactly, in NN its compressed. There is no intelligence here, there is no learning: it's a trick. It turns out that interpolating a point between prior examples can often look novel and often fool a human observer. This is largely due to how incredibly tolerant to flaws we are in the cases where NNs are used to perform this trick. We go to great lengths to impart intention, fix communicative flaws, etc. and this is exploited by "AI" to make simple crap seem great by having the observer fill-in the details, perceptually. I see it as a kind of proto-schizophrenia that all people have which usually works if we're dealing with a human, but on everything else produces religions. In any case, a NN is just a case of a kNN -- which is capable of fooling people exactly the same way, and clearly violates copyright and is a case of theft of intellectual work to make a product you can sell. Adding compression seems irrelevant.
- FeepingCreature 4y ago
  I don't think this interpretation of NNs is correct. There's been a few papers purporting to show this, but afair they used a very tortured definition of "interpolation". Stable Diffusion is certainly capable of differentiating good from bad. That's why you can tell it to draw good or draw bad. Not that this point is relevant to my comment. "Play", "taste" and "judgment" can be just as deterministic as a sequence of large matrix operations interspersed with nonlinear layers.
  - mjburgess 4y ago
    Sure, but then who is torturing matrices to turn them into organic bodies which adapt their musculature to their environment? Interpolation is forced in the case of NNs, it's a training condition. And the "kNN interpretation" isnt an interpration, kNNs define what "ideal learning" is in the case of statistical learning, and hence show, it doesnt count as actual learning. In actual learning we're not interested in whether you can solve prespecified problems but how well you cope when you can't. This is, by definition, not a problem which can be formulated in statistical learning terms and the particular "learning" algorithm here is irrelevant. In other words accuracy isnt a test of learning. Accuracy is a "non-modal condition" in being fit to a history that actually took place. Learning "in the usual sense" is strictly a modal, "what if" phenomenon, and is assessed by the quality of failure under adverse conditions, not of success. If one gave any AI/ML system in existence adverse conditions, posed relevant "what ifs" and observed the results, they'd be exposed as the catastrophe they are. None survive any even basic test of "coping well" in these cases. This is why all breathless AI public relations, ie., academic papers published in the last decade, do not perform any such tests.

Right, but underneath your premise is a scam, right? A NN has not learnt to paint: it doesn't coordinate its sensory-motor system with its environment through play, it hasn't developed any taste, it does not discern the aesthetic good from the bad, it has no judgement, and so on ad infinitum. A NN is just a kNN with an extra compression step. The way all gradient based "learners" work is to compute distances to pregiven training data. In the case of kNN that data is used exactly, in NN its compressed. There is no intelligence here, there is no learning: it's a trick. It turns out that interpolating a point between prior examples can often look novel and often fool a human observer. This is largely due to how incredibly tolerant to flaws we are in the cases where NNs are used to perform this trick. We go to great lengths to impart intention, fix communicative flaws, etc. and this is exploited by "AI" to make simple crap seem great by having the observer fill-in the details, perceptually. I see it as a kind of proto-schizophrenia that all people have which usually works if we're dealing with a human, but on everything else produces religions. In any case, a NN is just a case of a kNN -- which is capable of fooling people exactly the same way, and clearly violates copyright and is a case of theft of intellectual work to make a product you can sell. Adding compression seems irrelevant.

FeepingCreature 4y ago
I don't think this interpretation of NNs is correct. There's been a few papers purporting to show this, but afair they used a very tortured definition of "interpolation". Stable Diffusion is certainly capable of differentiating good from bad. That's why you can tell it to draw good or draw bad. Not that this point is relevant to my comment. "Play", "taste" and "judgment" can be just as deterministic as a sequence of large matrix operations interspersed with nonlinear layers.
- mjburgess 4y ago
  Sure, but then who is torturing matrices to turn them into organic bodies which adapt their musculature to their environment? Interpolation is forced in the case of NNs, it's a training condition. And the "kNN interpretation" isnt an interpration, kNNs define what "ideal learning" is in the case of statistical learning, and hence show, it doesnt count as actual learning. In actual learning we're not interested in whether you can solve prespecified problems but how well you cope when you can't. This is, by definition, not a problem which can be formulated in statistical learning terms and the particular "learning" algorithm here is irrelevant. In other words accuracy isnt a test of learning. Accuracy is a "non-modal condition" in being fit to a history that actually took place. Learning "in the usual sense" is strictly a modal, "what if" phenomenon, and is assessed by the quality of failure under adverse conditions, not of success. If one gave any AI/ML system in existence adverse conditions, posed relevant "what ifs" and observed the results, they'd be exposed as the catastrophe they are. None survive any even basic test of "coping well" in these cases. This is why all breathless AI public relations, ie., academic papers published in the last decade, do not perform any such tests.