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‘Impossible’ to create AI tools like ChatGPT without copyrighted material, OpenAI says
(www.theguardian.com)
this post was submitted on 09 Jan 2024
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People do not consume and process data the same way an AI model does. Therefore it doesn’t matter about how humans learn, because AIs don’t learn. This isn’t repurposing work, it’s using work in a way the copyright holder doesn’t allow, just like copyright holders are allowed to prohibit commercial use.
It's called "machine learning", not "AI", and it's called that for a reason.
"AI" models are, essentially, solvers for mathematical system that we, humans, cannot describe and create solvers for ourselves, due to their complexity.
For example, a calculator for pure numbers is a pretty simple device all the logic of which can be designed by a human directly. For the device to be useful, however, the creator will have to analyze mathematical works of other people (to figure out how math works to begin with) and to test their creation against them. That is, they'd run formulas derived and solved by other people to verify that the results are correct.
With "AI" instead of designing all the logic manually, we create a system which can end up in a number of finite, yet still near infinite states, each of which defines behavior different from the other. By slowly tuning the model using existing data and checking its performance we (ideally) end up with a solver for some incredibly complex system. Such as languages or images.
If we were training a regular calculator this way, we might feed it things like "2+2=4", "3x3=9", "10/5=2", etc.
If, after we're done, the model can only solve those three expressions - we have failed. The model didn't learn the mathematical system, it just memorized the examples. That's called overfitting and that's what every single "AI" company in the world is trying to avoid. (And to do so, they need a lot of diverse data)
Of course, if instead of those expressions the training set consisted of Portrait of Dora Maar, Mona Lisa, and Girl with a Pearl Earring, the model would only generate those tree paintings.
However, if the training was successful, we can ask the model to solve 3x10/5+2 - an expression it has never seen before - and it'd give us the correct result - 8. Or, in case of paintings, if we ask for a "Portrait of Mona List with a Pearl Earring" it would give us a brand new image that contains elements and styles of the thee paintings from the training set merged into a new one.
Of course the architecture of a machine learning model and the architecture of the human brain doesn't match, but the things both can do are quite similar. Creating new works based on existing ones is not, by any means, a new invention. Here's a picture that merges elements of "Fear and Loathing in Las Vegas" and "My Little Pony", for example.
The major difference is that skills and knowledge of individual humans necessary to do things like that cannot be transferred or lend to other people. Machine learning models can be. This tech is probably the closest we'll even be to being able to shake skills and knowledge "telepathically", so to say.
I’m well aware of how machine learning works. I did 90% of the work for a degree in exactly it. I’ve written semi-basic neural networks from scratch, and am familiar with terminology around training and how the process works.
Humans learn, process, and most importantly, transform data in a different manner than machines. The sum totality of the human existence each individual goes through means there is a transformation based on that existence that can’t be replicated by machines.
A human can replicate other styles, as you show with your example, but that doesn’t mean that is the total extent of new creation. It’s been proven in many cases that civilizations create art in isolation, not needing to draw from any previous art to create new ideas. That’s the human element that can’t be replicated in anything less than true General AI with real intelligence.
Machine Learning models such as the LLMs/GenerativeAI of today are statistically based on what it has seen before. While it doesn’t store the data, it does often replicate it in its outputs. That shows that the models that exist now are not creating new ideas, rather mixing up what they already have.