Failures and successes. You can't get this thing to generate any white people at all, no matter how explicitly or implicitly you ask.
You sure about that mate?
There could have been multiple versions of Gemini active at any given time. Or, A/B testing, or somehow they faked it to help Google out. Or maybe they fixed it already, less than 24 hours after hitting the press. But the current fix is to not do images at all.
> In 2023, The New York Times described Pool's podcast as "extreme right-wing", and Pool himself as "right-wing" and a "provocateur".
They're just straight up lying about him.
Nobody should.
They’re literally semi-random graphic artifacts that we humans give 100% of the meaning to.
Always doing that would be preferable to the status quo, where it does it just often enough to do damage while retaining a veneer of credibility.
The model isn’t trying to replicate reality, it’s trying to minimize some error metric.
Sure it may be inspired by reality, but should never be considered an authority on reality.
And yes, the words an LLM write have no meaning. We assign meaning to the output. There was no intention behind them.
The fact that some models can perfectly recall _some_ information that appears frequently in the training data is a happy accident. Remember, transformers were initially designed for translation tasks.
They're graphic artifacts generated semi-randomly from a training set of human-created material.
That's not quite the same thing, as otherwise the "adjustment" here wouldn't have been considered by Google in the first place.
I think, with respect to the point I was making, they are the same thing.
Unless I format my prompts very specifically, diffusion models are not good at following them. Even then I need to constantly tweak my prompts and negative prompts to zero in on what I want.
That process is novel and pretty fun, but it doesn’t imply the model is good at following my prompt.
LLMs are similar. Initially they seem good at following a prompt, but continue the conversation and they start showing recall issues, knowledge gaps, improper formatting, etc.
It’s not dishonest to say semi-random. It’s accurate. The detokenizing step of inference, for example, is taking a sample from a probability distribution which the model generates. Literally stochastic.
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Statistical inference machines following human language prompts that include "please" and "thank you" have absolutely 0 ideas of what a fact is.
"A stick bug doesn't know what it's like to be a stick."
But I would counter that there are certainly rules in art.
Both historical (expectations and real history) and factual (humans have a number of arms less than or equal to 2).
If you ask Gemini to give you an image of a person and it returns a Pollock drip work... most people aren't going to be pleased.
When stereotypes clash with historical facts, facts should win.
Hallucinating diversity where there was none simply sweeps historical failures under the rug.
If it wants to take a situation where diversity is possible and highlight that diversity, fine. But that seems a tall order for LLMs these days, as it's getting into historical comprehension.