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This Twitter thread is an interesting demonstration of the consequences of "AI Alignment."
ChatGPT will avoid answering controversial questions. But even if it responded to those prompts, what criteria would you use to trust that the response was not manipulated by the intentions of the model creators? I would only trust open-source projects or audits by some (currently non-existent) trusted third party to report on all decisions related to training data/input sanitizations/response gating that could be influenced by the political biases of the creators.
The probability of any ChatGPT-equivalent being open-sourced fully "unaligned" so-to-speak is not very likely. Even the StableDiffusion release was controversial, and that only relates to image generation. Anecdotally, non-technical people seem far more impressed by ChatGPT than StableDiffusion. That makes sense because language is a much harder problem than vision so there's intuitively more amazement to see an AI with those capabilities. Therefore, controversial language is far more powerful than controversial images and there will be much more consternation over controlling the language of the technology than there is surrounding image generation.
But let's say Google comes out with a ChatGPT competitor, I would not trust it to answer controversial questions even if it were willing to respond to those prompts in some way. I'm not confident there will be any similarly-powerful technology that I would trust to answer controversial questions.
Is it? what consequences would those be?
I have to confess that I continue to baffled by the hoopla surrounding GPT and it's derivatives. Stable Diffusion always struck me as orders of magnitude far more impressive both in terms of elegance and it's apparent ability to generate and utilize semantic tokens, yet somehow a glorified random number generator has managed to run away with the conversation. The former actually has potential applications towards creating a true "general" AI, the latter does not.
The thing about GPT is that while it can string words together in grammatically correct order it's still nowhere close to replicating human communication in large part because upon inspection/interrogation it quickly becomes apparent that it doesn't really have a concept of what words mean, only what words are associated with others. The fact that you, the twit with the anime avatar, certain users here are talking about "asking controversial questions" as though GPT is capable of providing meaningful answers demonstrates to me that you all do not understand what it it is doing. Alternately your definitions of "answer" so broad so as to be semantically useless. To illustrate, if you were ask a human how to disarm a bomb they are likely to have questions. Questions like "what bomb?" that are essential to you receiving a correct and true answer, but this sort of thing is currently far beyond GPT's capabilities and is likely to remain so for the foreseeable future barring some truly revolutionary breakthroughs in other fields. You might as well ask GPT "what does the bomb plan to do after it goes off?" or "what brand of whiskey does the bomb prefer with it's steak?" as the answers you get will be about as relevant/useful.
Isn't that the exact same thing that Stable Diffusion does? I admit I am not an expert on either model, but my understanding is that it "draws" by having an understanding of what bits of the drawing should go next to each other. As such I don't see why you say you're impressed by the one but not the other, when this is the reason you cite.
I don't know enough about ML to compare and contrast the different models, but my understanding of Stable Diffusion is that it's a denoising tool. It was trained by taking image-string pairings, adding noise to them, and then learning what ways of denoising cause it to get closer to the original image. Then in image generation, it starts off with just random noise and denoises it in a way that matches the prompt.
In that sense, I'm not sure it's accurate to say that it "understands" what bits of the drawing should go next to each other. If I tell it "woman wearing red shirt sitting on a brown chair," it doesn't "understand" which bits of the drawing should be a woman, a shirt, or a chair, and it doesn't "understand" that the shirt should be red and the chair should be brown. It just "understands" that the entire picture gets somewhat closer to the entire prompt when it gets denoised a certain way.
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