4 ms·
You clearly know nothing about the alignment field since you are throwing together two groups of people that have nothing in common. The stochastic parrot peop
by iNic 3y ago
You clearly know nothing about the alignment field since you are throwing together two groups of people that have nothing in common.
The stochastic parrot people only care about "moral" and "fair" AI, whereas the AI saftey or AI notkilleveryone people care about AI not killing everyone.
Also the whole "who's value" argument is obviously also stupid, since for now we don't know how to put anybody's value into an AI. Companies would pay you billions of dollars if you could reliably put anyone's values into a language model.
- pc86 3y agoNo comment on whether it's broadly a good thing or bad thing, but you can't get ChatGPT (on GPT-4 at least) to tell you even slightly scandalous jokes without a lot of custom prompting and subterfuge, and they seem to be spending active development and model training time to try to counter a lot of the circumvention we've seen to get around these guardrails. Not just for dirty jokes but for anything deemed bad. So it seems pretty clear you can load values into an LLM.
- jhbadger 3y agoAnd on the censored local models too, like standard vicuna. If I am having my LLM play an adventure game or write a story and I ask "What does the princess look like?" I don't want a lecture on how judging people by their looks is bad (which I sometimes get) -- I can get, if not entirely agree with, stopping actual NSFW responses, but this condescending moralizing is absurd. That's why I'm glad people make uncensored Vicuna models and the like.
- zelphirkalt 3y agoAI killing people is not fair. So I think you can see one of the two groups as a subgroup of the other. Who's values discussions also don't seem stupid, as it is better to have a regulation for that, before some Google, MS or Apple does find out how to put in their values and only their values. Better come prepared than to again sleep through the advent of it happening and then again running behind.