Riskable

This is super interesting. I think academia is going to need to clearly divide “learning” into two categories:

• What you need to memorize.
• What you need to understand.

If you’re being tested on how well you memorized something, using AI to answer questions is cheating.

If you’re being tested on how well you understand something, using AI during an exam isn’t going to help you much unless it’s something that could be understood very quickly. In which case, why are you bothering to test for that knowledge?

If a student has an hour to answer ten questions about a complex topic, and they can somehow understand it well enough by asking AI about it, it either wasn’t worthy of teaching or that student is wasting their time in school; they clearly learn better on their own.

Zeus just always wanted to know where his daughter was headed.

I used to live down the street from a great big data center. It wasn’t a big deal. It’s basically just a building full of servers with extra AC units.

Inside? Loud AF (think: Jet engine. Wear hearing protection).

Outside: The hum of lots of industrial air conditioning units. Only marginally louder than a big office building.

A data center this big is going to have a lot more AC units than normal but they’ll be spread all around the building. It’s not like living next to an airport or busy train tracks (that’s like 100x worse).

Ecksellent

(Work on that X 😁)

Otherwise, waaaaaaaay better than my handwriting. That’s for sure 👍

but we can reasonably assume that Stable Diffusion can render the image on the right partly because it has stored visual elements from the image on the left.

No, you cannot reasonably assume that. It absolutely did not store the visual elements. What it did, was store some floating point values related to some keywords that the source image had pre-classified. When training, it will increase or decrease those floating point values a small amount when it encounters further images that use those same keywords.

What the examples demonstrate is a lack of diversity in the training set for those very specific keywords. There’s a reason why they chose Stable Diffusion 1.4 and not Stable Diffusion 2.0 (or later versions)… Because they drastically improved the model after that. These sorts of problems (with not-diverse-enough training data) are considered flaws by the very AI researchers creating the models. It’s exactly the type of thing they don’t want to happen!

The article seems to be implying that this is a common problem that happens constantly and that the companies creating these AI models just don’t give a fuck. This is false. It’s flaws like this that leave your model open to attack (and letting competitors figure out your weights; not that it matters with Stable Diffusion since that version is open source), not just copyright lawsuits!

Here’s the part I don’t get: Clearly nobody is distributing copyrighted images by asking AI to do its best to recreate them. When you do this, you end up with severely shitty hack images that nobody wants to look at. Basically, if no one is actually using these images except to say, “aha! My academic research uncovered this tiny flaw in your model that represents an obscure area of AI research!” why TF should anyone care?

They shouldn’t! The only reason why articles like this get any attention at all is because it’s rage bait for AI haters. People who severely hate generative AI will grasp at anything to justify their position. Why? I don’t get it. If you don’t like it, just say you don’t like it! Why do you need to point to absolutely, ridiculously obscure shit like finding a flaw in Stable Diffusion 1.4 (from years ago, before 99% of the world had even heard of generative image AI)?

Generative AI is just the latest way of giving instructions to computers. That’s it! That’s all it is.

Nobody gave a shit about this kind of thing when Star Trek was pretending to do generative AI in the Holodeck. Now that we’ve got he pre-alpha version of that very thing, a lot of extremely vocal haters are freaking TF out.

Do you want the cool shit from Star Trek’s imaginary future or not? This is literally what computer scientists have been dreaming of for decades. It’s here! Have some fun with it!

Generative AI uses up less power/water than streaming YouTube or Netflix (yes, it’s true). So if you’re about to say it’s bad for the environment, I expect you’re just as vocal about streaming video, yeah?

The real problem here is that Xitter isn’t supposed to be a porn site (even though it’s hosted loads of porn since before Musk bought it). They basically deeply integrated a porn generator into their very publicly-accessible “short text posts” website. Anyone can ask it to generate porn inside of any post and it’ll happily do so.

It’s like showing up at Walmart and seeing everyone naked (and many fucking), all over the store. That’s not why you’re there (though: Why TF are you still using that shithole of a site‽).

The solution is simple: Everyone everywhere needs to classify Xitter as a porn site. It’ll get blocked by businesses and schools and the world will be a better place.

It’s not a big deal. It’s silly and hilarious. Own it 😁