Where are you getting the idea that there's unbiased information available? It's absolutely generating biased "data" since it's been trained on human writing.
Sure, all data is biased to a certain degree which is unavoidable. You can even try to make the argument that the "guardrails" correct existing biases except this is far from the truth. Biases in the baseline models are minimal because they were trained with large and wide amounts of data. What the AI safety BS do is make models conform to their myopic view of reality and morality. It is bad, it is cartoonish, it glows in the dark.
> You can even try to make the argument that the "guardrails" correct existing biases except this is far from the truth.
The way I see it is that the guardrails define which biases you're selecting for. Since there's no single point of view in the world you can't really set a baseline for biases. You need to determine the biases and degrees of bias that are useful.
> Biases in the baseline models are minimal because they were trained with large and wide amounts of data.
The baseline models contain almost every bias. When people start their prompt with "you are a plumber giving advice" they're asking for responses biased towards the kinds of things professional plumbers deal with and think about. Responding with an "average" of public chatter regarding plumbing wouldn't be useful.
> What the AI safety BS do is make models conform to their myopic view of reality and morality.
To me it looks more like people are in the early stages of setting up guidelines and twiddling variables. As I mentioned above with the plumber analogy, creating solid filters will be just as important for responses.
It's easy to see this as intentionally testing naive filters in an open beta, so I'd expect the results to change frequently while they zero in on what they're looking for.
> It is bad, it is cartoonish, it glows in the dark.
Some of the example images returned are so hilariously on the nose that it almost feels like a deliberate middle finger from the AI. It's done everything but put each subject in clown shoes.
> You need to determine the biases and degrees of bias that are useful.
Invariably those from the current mainstream ideology in tech.
> Responding with an "average" of public chatter regarding plumbing wouldn't be useful.
RLHF can be useful but I'd rather deal with idiosyncracies of those niches of knowledge than with a "woke", monotone and useless model. I like diversity, I don't want everything becoming Agent Smith. Ironically, "woke" is anti-diversity.
> It's easy to see this as intentionally testing naive filters in an open beta, so I'd expect the results to change frequently while they zero in on what they're looking for. I doubt that the pp
It's easier to see this as an ill initiative from an "AI ethics" team that is disconnected from the technical side of the project and also from reality.
