That's not a problem, we already have copyright laws that prevent people from distributing mirrors of copyrighted works. They don't care about how the works were copied.
yes, isn't that what very many people are saying?
Humans are capable of both facsimile and imitation.
The fact that ML is able to perform facsimile far better than a human can is not evidence that this is “not the same” learning. Only that ML learning is superior. ML is far superior in feats of both imitation and facsimile.
If I show a 3 year old a single picture of a Tiger, and tell him this is a tiger, the child is able to recognize a Tiger fairly accurate in real life without further input. Though the child might say that a house cat is tiger,,,,
ML learning needs millions of pictures to do the same, and still might mistake an elephant for a tiger...
ML is nothing more than graph approximation, there is no logical reasoning
ML is currently capable of the tiger case you mention. It’s generally called “few-shot” or “one-shot” learning. In the context of an image generation model, having never seen a tiger before, if you show it a few pictures of a tiger, it could immediately draw you thousands of tigers in any variation or scenario you can think of, which is way more than a child can do.
As for the need to train on millions of images for the base model, I believe you are trying to say something about “sample efficiency”, and how ML differs from the brain in this regard outside of the few/one-shot contexts (which ML is absolutely capable of). I would argue that sample efficiency of the brain is actually also quite low, much lower than people assume. It’s irrelevant to an argument that ML is not superior, because ML is clearly is capable of learning richer, more effective representations in a shorter wall time than we can, whether it is sample efficient or not. And in the sample efficient few/one-shot contexts (learning what a tiger looks like from one picture), it also outperforms humans in speed accuracy and creativity. It’s not even close.
As for classification errors, ML is capable of some errors we are not, actually by virtue of being superior at learning representations we are not even close to being capable of learning. But those are edge cases, and they are fixed by various means. In the main cases, ML outperforms humans in speed, accuracy and class complexity, all exponentially.
You said something about graph approximation but it doesn’t make a lot of sense. I’m talking about learning and you’re complaining that machine learning is not “logical reasoning”. Whether ML is currently capable of logical reasoning is another discussion. Certain models do demonstrate some types of it today.
“Graph approximation” is a type of learning task. ML is a billion times better than humans at it so it also doesn’t help you argue that ML isn’t superior (in that regard).
you don't need permission to train on books, but you do need to buy the books or take them from the library one at a time.
"training" these machines so far is not like human learning as becomes apparent when they spit out source code that mirrors individual repositories. And you know that humans are required to both remix their own creations and follow copyright law at thbowe same time, and also adhere to the social and institutional stigmas against extensive uncreative cut and paste paraphrasals.
when training AIs on copyright law trains them in obeying copyright law, they'll be ready for the Turing test, or even to be called AIs.