Greg Rutkowski, a digital artist known for his surreal style, opposes AI art but his name and style have been frequently used by AI art generators without his consent. In response, Stable Diffusion removed his work from their dataset in version 2.0. However, the community has now created a tool to emulate Rutkowski’s style against his wishes using a LoRA model. While some argue this is unethical, others justify it since Rutkowski’s art has already been widely used in Stable Diffusion 1.5. The debate highlights the blurry line between innovation and infringement in the emerging field of AI art.
LLMs and directly related technologies are not AI and possess no intelligence or capability to comprehend, despite the hype. So, they are absolutely the former, though it’s rather like a bandwagon sort of thing (x number of reference images had a watermark, so that’s what the generated image should have).
LLMs […] no intelligence or capability to comprehend
That’s debatable. LLMs have shown emergent behaviors aside from what was trained, and they seem to be capable of comprehending relationships between all sorts of tokens, including multi-modal ones.
Anyway, Stable diffusion is not an LLM, it’s more of a “neural network hallucination machine” with some cool hallucinations, that sometimes happen to be really close to some or parts of the input data. It still needs to be “smart” enough to decompose the original data into enough and the right patterns, that it can reconstruct part of the original from the patterns alone.
Thanks for the clarification!
LLMs have indeed shown interesting behaviors but, from my experience with the technology and how it works, I would say that any claims of intelligence being possessed by a system that is only an LLM would be suspect and require extraordinary evidence to prove that it is not mistaken anthropomorphizing.
I don’t think an LLM alone can be intelligent… but I do think it can be the central building block for a sentient self-aware intelligent system.
Humans can be thought of as being made of a set of field-specific neural networks, tied together by a looping self-evaluating multi-modal LLM that we call “conscience”. The ability of an LLM to consume its own output, is what allows it to be used as the conscience loop, and current LLMs being trained on human language with all its human nuance, is an extra bonus.
Probably some other non-text multi-modal neural networks capable of consuming their own output could also be developed and be put in a loop, but right now we have LLMs, and we kind of understand most of what they’re saying, and they kind of understand most of what we’re saying, so that makes communication easier.
I mean, it is anthropomorphizing, but in this case I think it makes sense because it’s also anthropogenic, since these human language LLMs get trained on human language.
