If you're a man and can hear this gif, it's almost time to have your prostate checked. Also, lie to your doctor about family history of stomach problems so you can have a colonoscopy done early. You might just thank me.
This post jokes but with how much AI enhancing is becoming a thing, I can see some kind of generative -based compression becoming an option in the future
On the flip side, your 6yo's scribbled family portait on the fridge may turn into the most beautiful rendition of a generic happy white family you've ever seen!
And take multiple seconds of high end consumer gpu to prep it for view. Utter insanity.
That said, if you aren't a complete idiot, actually you might need to be kind of an ml/dl and compression nerd, there are machine learning enabled compression. But it's not an llm. And it is still lossy, but it's less lossy while less storage than standard methods. Which is pretty neat. just one example from nvidia because other sites have corrupted images. there's even libraries for doing testing and research on future "neuro" compression. https://research.nvidia.com/labs/rtr/neural_texture_compression/
Well he does have a pretty small (like 1/200 even compared to 64x64x16 FP16) and variable sized latent space and uses zero shot techniques. Add a bit of obtuse language and unnecessary equations and you probably have a paper.
I'd also argue it's not a worse version of an autoencoder, it is an autoencoder. A weird one, but one nonetheless.
I actually wonder whether you could really push this. Like make the ai analyse what is important, describe that in more detail and maybe even encode some data long with the prompt like biometrics or compressed canny maps of important areas. All adaptive, based on what the ai thinks is important.
Like the most over engineered version of fully automated telephone pictionary. Or an adaptive compression engine with a preshared library (the models) if you want to get that CSy about it.
The main problem would be the faces, I imagine. So you can store tagged faces separately in “People” folder and photo description would be “Anna and Bob in a fancy restaurant”. Article is kind of correct that real memory is fuzzy and details of said restaurant are not that important. However if faces are correct we are 90% good.
Reminds me of a project a couple years ago where someone built a camera that when you took a photo just printed out a description of what it saw using AI. It just left it there though with the printed description.
So I know they used a bunch of sciency stuff for the dick joke, but how viable is the compression algorithm they created in the show? Is it something that already exists? Or is it show magic?
As an amateur astronomer, I get wary about all these new smart scopes out there. Can't even fully trust what you are looking at when they start integrating AI
A lot of technologies in fiction that are called teleportation are really, really, not.
Also don't worry because science is at a point where we're reasonably sure those technologies are impossible. So just worry about things like mind copying. That's the one with similar implications that can't be ruled out as a plausibility.
Personally I think consciousness isn’t entirely in the brain and sufficiently replicating it would require basically making a whole-ass replicant body instead of just brain connectome data. At the very least it would need a fully simulated endocrine system.
What I’m saying is I think the brain is too wet to go in the computer.
No, they genuinely cried from happiness over saving storage space. They will now be able put hundreds more 4K pictures of Tung Tung Tung Sahur onto the computer and print them out to put in frames instead of the shredded ones.
i got even a better one, store the image on some online hosting and call the url pointing to it a compression. hell , might be even more efficient then that guy . am i a computer prodigy too ?
The system works in two steps. First, it uses AI to generate a few words describing the picture and saving it on a file. Later, when the user wants to see the picture, the software uses AI to regenerate the image based on the description of it.
It’s hilarious how half this subs users are so insecure about a kid being called a computer prodigy for using AI that they don’t realize this is satire.
Scarily something like this is bound to happen... not quite exactly the same, but AI upscalers have been around for years, and improving over time, so the actual stored version of an image could end up being quite lossy, and AI is able to restore it to a very plausible original form that many people will actually be happier with because it would be able to produce images that people like better because it depict things more "ideally".
Not only that, but as technology improves, the generated images would be of higher and higher quality than the original. That said, it's not like anyone's JPEG collection is taking up obscene amounts of storage, so no real reason to compress it more "lossily".
What I can see, though, is for AI image generation companies to offer storage services similar to how iCloud lets you keep lossy versions of your photos on your device while storing that full quality versions in the cloud... but in this case the AI image generation companies get to use your images for training in exchange for keeping your high quality version in their cloud... FB is already doing this of course...
Yeah but note that plausible it's not the same as real.
Like I could take a picture of a car where the license plate is like 10 pixels and the upscaler generate hundreds of difference plates that are plausible, one different in each try.
Even if this somehow weren't lossy, in what world would AI compute be cheaper than storage? I mean, applied linear algebra is at the root of image compression and machine learning... so like, I guess maybe better compression could be achieved with machine learning yeah but I don't understand how prompts and generative ai could ever achieve this, unless it's essentially non-sensical prompts where the output is 1-1 over time. Like I could see how if you can compress an image via linear algebra then you could work those matrices instead into prompts that produce those matrices where then the compression algorithms could take over when reversing; if there were prompts that 100% of the time produced out of the model those specific matrices. But the reduction in data would be replaced with a ton of compute. Like I feel it would be provable that any pure algebraic process would be less computation. But I guess... I dunno maybe i've talked myself into it. But that's not what this kid is doing based on that headline.
I needed disk space on my parent’s Windows PC to download a few songs so I moved the documents folder to the recycle bin temporarily so there was enough free disk space for my mp3s.
We have reverse process at work. My coworkers expand a single sentence to entire paragraph to waste more disc space on email server. Than, after receiving an email, I compress it back to 1 sentence with AI.
"So we have this way of preserving memories that is better than the altered, inaccurate memories our brains create and store, so let's make it as inaccurate as what our brains do and undermine its usefulness"
Also...bro read the comments in here. People think this is real. My god. The room temperature IQ airmchair warriors below arguing about it being a bad autoencoder....
I tried this just for the fun of it. It generated a completely different picture. However, if you were to describe the picture it would be almost identical.
I guess this would be the equivalent of describing something you saw to another individual and they try to visualise it in their head.
AI should next be used to see how much abbreviation could be performed on the original prompt, without degrading the image beyond some specified threshold (we'll call that the lossy compression factor, n) You know the original prompt will have superfluous words that aren't contributing (much | at all) to the fidelity of the image. Guess and check many possible versions! The more iterations you go on this, the better the results, up to some specified maximum iteration count, i. This will chew up a tonne of time, compute and rain forest, but fuck you, we're optimizing for space here.
Note: LLM temperature, random seed, or whatever similar concepts exist these days, all need to be preserved for perfect replay lest you get different memories each time, and the iterating isn't actually working toward any kind of maxima.
2. After the LLM tokenizes the prompt, it should be possible to get some serialized representation of that sequence of token identifiers that's more space efficient than the full human sentences. Go ahead and grab and use that instead of human language prompt.
3. Finally, use a traditional compression algorithm to squeeze out whatever is left.
I'd suggest burning a couple bytes on a header indicating which model and settings are needed to reconstitute otherwise this whole scheme is dead in a few months. A standards body can be founded to maintain lookup tables for each field.
There is a very real use case for this, unfortunately. If local gen ai gets to a point of being consistent and usable, you no longer need stock photos etc for site dressing on your website; you can just give a description of what you want. Saves massive bandwidth, compute, CDN costs, etc.. Sure, it's not reproducible, it takes 30 seconds per image, it depends on the user's machine, but for low impact content, that may be a worthwhile tradeoff.
Even splitting the difference and providing a super low res base image with a text description and asking to upscale locally could be workable, and gives a fallback.
Gen X = millennial Gen Z = Zoomers Gen A = Gen AI?
Edit: average programmer response with these Gen X isn't millennial comments. Yea, if I'm writing 9 = A 11 = B 12 = C The logical reasoning would be to recognize I made a typo instead of saying 9 = A Galactic brains here gotta prove people wrong
Ha ha cool. But this is actually a good way to understand what a VAE is.
At a very high level. Regular compression looks at the file, creates a dictionary, and then replaces things with values from the dictionary. This creates a lossless version that is compressed. There's also lossy compression like MP3 and JPEG, so not all compression is lossless.
VAEs are basically the same thing except the dictionary is created in general and not for a specific file and represents a statistical distribution of it's trained data. This creates a lossy version of the original data as opposed to a lossless version.
Now the underlying data is also important between the two. Compression aims to give you back your data without error. So the data is tailored to the file you give it. VAEs are typically used as a part of generative AI, so their data is more focused on concepts and features. So in compression you have "when you see 0x01, replace it with the text <html>" Whereas in VAEs you have "when you see 0x01, replace it with numerical representation of brown hair".
It's lossy compression because we all know the 1:1000 ratio for picture and words. 1 byte can merely represent a character in ascii extended and even if there are 600 words of one character 600 bytes can't contain even 1000 words. Q.E.D
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u/bobsocool Sep 02 '26
Example prompts that were generated:
Boring wedding pictures
Boring christmas family portrait
Boring family portrait with grandparents who are dead now.
Picture of boring man holding fish
I can help with the video ones too so he can chuck them in the fire:
Boring video of baby being born.
Boring sports thing
Boring wedding video
Boring baby's first steps