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naraburns  nihil supernum  1yr ago (text post)   35417 thread views

Culture War Roundup for the week of October 3, 2022

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This week's revolutionary AI advance:

Imagen Video

It's not really revolutionary, as people have been pointing out this is the obvious next step for ages months now. But it still is a milestone worth noting.

As for this:

While our internal testing suggest much of explicit and violent content can be filtered out, there still exists social biases and stereotypes which are challenging to detect and filter. We have decided not to release the Imagen Video model or its source code until these concerns are mitigated.

Google's made a habit of this. They announce an amazing advance, and then say no one can have access to it because it can be used for Evil. No matter: Stable Diffusion will have something comparable out in a couple months.

ETA:

Actually, this out of DeepMind might be the bigger advance today, if less flashy:

Press: Discovering Novel Algorithms with AlphaTensor

Paper: Discovering faster matrix multiplication algorithms with reinforcement learning

My greatest fear for AI content generation is it being dominated by woke megacorps, with independent creators permanently locked out of contributing to culture. It looks like Google is investing heavily in that dystopia.

Novelai and stable diffusion being mostly uncensored has been a big white pill so far, but it feels like the shoe is about to drop.

Reading this with its assorted replies along with @Primaprimaprima's post earlier in the week leaves me feeling like I have a fundamentally different understanding of art, and experience it in a vastly different way from the majority of commenters here. I'm not sure what to make of your stated concern because I don't see how it even relates.

While I grant that I may be falling for the "passing tranny fallacy" (IE transwomen who pass don't get recognized as transwomen) pretty much every example of AI generated text and images I've encountered has struck me as painfully obvious. Whether it's GPT-x or DALL-y it's all very obviously not human, not even close, and thus I find myself wondering what all the furor is about.

Am I really an outlier in my ability to notice that randomly strung together words, even if they are grammatically correct, aren't conveying any meaning? Ditto the visual medium, I've gotten used to cheap sci-fi book covers having nothing to do with the plot, but am I really the only guy who's noticed?

Seems to me that you're painting in apocalyptic terms what to me looks like just another Thursday and that raise the question of which, if either, of us is actually out of line.

While I grant that I may be falling for the "passing tranny fallacy"

I keep pointing out when people mention this: You can see a distribution, and notice that the distribution gets sparser when you get towards "better at passing, but where I can still notice them". You can then deduce that there aren't many who are so good that you won't notice them at all.

Even if we assume a uniform distribution, wouldn't it still be the case that the distribution of those you notice would still get sparser when you get towards "better at passing, but where I can still notice them?"

Like, imagine a toy model where there are 300 total MTF trans people, with 100 who are bad at passing, 100 who are better, and 100 who pass perfectly. You'll always notice the 1st 100, you'll notice the middle 100 about 50% of the time, and you'll never notice the last 100. By your observations, then, you'll notice 100 poorly-passing MTF and 50 better-passing MTF. Since the middle group is better-passing, you'll also notice that those 50 are qualitatively different - i.e. noticeably closer to actually passing - from the 100 in the 1st group, besides just noticing them only 50% of the time. But it would be wrong to conclude from this that then it follows that there are even fewer people in the 3rd group.

That's true if "better at passing" means "more often doesn't get noticed", but it's not true if "better at passing" means "still noticed, but doesn't look quite as bad". I was suggesting the latter scenario.

OK, I had to think about this for a while, but I think I follow. You're really only looking at those who are so far away from passing that they would fail to pass 100 times out of 100, and looking at the distribution of how badly they fail at passing? I think that makes sense, and you're probably correct in your inference.

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