All of the dynamic content on each listing is generated via a series of different machine-learned AI models.
 where n is normally 2 or 3
"Thus serve a 24 hour security pournising. Also hype is requested as much as you please empty and have a prestige less um be restriction, day or night."
It's close, though! But maybe not close enough for people to worry -- the last 10% of the security pournising always takes 90% of the development time.
This fear of fake information from ML misleading everyone is ridiculous and kind of arrogant. It assumes that the world is full of "other people" who are too stupid to make decisions for themselves and usually concludes that "us smart people" have to somehow control what they see to protect them from themselves. We've had fake information since forever and we've developed systems to deal with it. Citing sources, trustworthy organizations, multiple sources agreeing with each other, people pointing out mistakes, Google favoring popular sites, confirming it yourself, etc. Some fake information still gets through and it is a problem but it already happens and the world keeps turning. For casual internet searchers who don't care how reliable their information is, let them believe whatever nonsense satisfies them. They aren't trying to be right, they're just entertaining themselves.
At the very least, I think we need to train people in a lot more media literacy. But traditional approaches to that rely on media being scarce and expensive to create, which gave people enough time to carefully vet what they were consuming. As it becomes cheaper to create media than to vet it, we'll have the same problem as spam: it'll be impossible for humans to effectively filter it manually.
I think the real solution is automated vetting tools, so no information is presented without provenance. Basically, any time somebody sees an image or a video, there should be a link that lets you find out about the source, the editing, and who, specifically is vouching for it. And warnings for things that lack that. That still gives the viewer agency, but brings the problem back to human scale.
By the way, there are over 400,000 naturally occurring species of beetles. Beetles make up 25% of all animal forms according to Wikipedia.
Of course, this method would have to compete with a related model just trying to classify a photo.
Consider the "morphogenetic puzzle" of a bi-valved seashell that shuts with perfect water-tight seal. There is a constraint to this design: survival!
A lot of gameplay involves testing for this boundary... Trying to figure out whether you can actually do things that are implied by the art.
Are there any modern games where 100% of the art exists inside the game world?
Baba Is You
Also, the clouds.
I noticed that the transformations seem to be fast through a transition and then seemingly paused. Is this intentional or does this have something to do with the model?
But maybe there’s some way to deal with that.
Although I’m sure there’s a smarter way.
One (the discriminator) is trained with a bunch of images showing what beetles can look like. It detects a real or fake image of a beetle.
The other (the generator) is just generating images with a convolutional neural network. The generator optimizes itself based on how close it is to passing the discriminators test - that is its "loss function".
So over time, the generator gets better and better at making things that look like beetles. The process takes a very long time and is aided by many GPUs (as mentioned in the article)
The machine here doesn’t even know that those are beetles (because nobody told it), it is “just” arranging pixels in a similar manner as the pixels from the source images. It does understand that each generated image must have “legs”, “eyes”, “shells”... and other features that it detected are common in the original images.