From my understanding, AI is the general field of automating logical (“intelligent”) tasks.
Within it, you will find Machine Learning algorithms, the ones that are trained on exemplar data, but also other methods, for instance old text generators based on syntactic rules.
Within Machine Learning, not all methods use Neural Networks, for instance if you have seen cool brake calipers and rocket nozzle designed with AI, I believe those were made with genetic algorithms.
For procedural generation, I assume there is a whole range of methods that can be used:
Unreal Engine Megaplants seems to contain configurable tree generation algorithms, that’s mostly handcrafted algorithms with maybe some machine learning to find the parameters ranges.
Motion capture and 3D reconstruction models can be used to build the assets. I don’t believe these rely on stolen artist data.
Full on image generation models (sora, etc.) to produce assets and textures, these require training on stolen artist data AFAIK (some arrangements were made between some companies but I suspect it’s marginal).
I agree with the ethical standpoint of banning Generative AI on the grounds that it’s trained on stolen artist data, but I’m not sure how tenable “trained on stolen artist data” is as a technical definition of what is not acceptable.
For example, if a model were trained exclusively on licensed works and data, would this be permissible? Intuitively, I’d still consider that to be Generative AI (though this might be a moot point, because the one thing I agree with the tech giants on is that it’s impractical to train Generative AI systems on licensed data because of the gargantuan amounts of training data required)
Perhaps it’s foolish of me to even attempt to pin down definitions in this way, but given how tech oligarchs often use terms in slippery and misleading ways, I’ve found it useful to try pin terms down where possible
From my understanding, AI is the general field of automating logical (“intelligent”) tasks.
Within it, you will find Machine Learning algorithms, the ones that are trained on exemplar data, but also other methods, for instance old text generators based on syntactic rules.
Within Machine Learning, not all methods use Neural Networks, for instance if you have seen cool brake calipers and rocket nozzle designed with AI, I believe those were made with genetic algorithms.
For procedural generation, I assume there is a whole range of methods that can be used:
I agree with the ethical standpoint of banning Generative AI on the grounds that it’s trained on stolen artist data, but I’m not sure how tenable “trained on stolen artist data” is as a technical definition of what is not acceptable.
For example, if a model were trained exclusively on licensed works and data, would this be permissible? Intuitively, I’d still consider that to be Generative AI (though this might be a moot point, because the one thing I agree with the tech giants on is that it’s impractical to train Generative AI systems on licensed data because of the gargantuan amounts of training data required)
Perhaps it’s foolish of me to even attempt to pin down definitions in this way, but given how tech oligarchs often use terms in slippery and misleading ways, I’ve found it useful to try pin terms down where possible