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Comment by throw101010 | Hacker News Reader

> Nature will not be publishing any content in which photography, videos or illustrations have been created wholly or partly using generative AI, at least for the foreseeable future.

Allow me a bit of a rhetorical question, what are the chances they already publish photos taken on devices that apply by default some form of AI-based generative/corrective algorithms like the "AI detail enhancement engine" by Samsung (the one they use to enhance photos of the moon)?

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I'm peripherally involved in this scene. The answer is that the journals don't want processed images, but of course the scientist doesn't always know what kind of processing happened to the image en route to their display and file system. The idea is that an image supposedly constitutes "data" and as such, should represent raw data.

Also, what constitutes "raw data" is itself a matter of debate. How raw is raw? Like any interesting pursuit, scientific publishing struggles to keep up with developments in technology.

> How raw is raw?

Certainly no jpeg image produced by any digital camera is really “raw” as it will already have been through a debayering filter

https://en.wikipedia.org/wiki/Bayer_filter

And then on top of that is the JPEG compression artifacts.

But I do wonder how many raw files also contain data that has been debayered already. I have not looked into that.

I know that with third party firmware such as Magic Lantern it is possible to get the image data without debayering. https://magiclantern.fm/

Likewise I know that the Camera Module 3 for Raspberry Pi is possible to retrieve the image data from without debayering.

The RAW data for the vast majority of MILCs and DSLRs is pre-debayering.

Even Android smartphones will give pre-debayering raw data from the sensor if you use the appropriate camera app. (There are quite a few cheap ones where the OEM debayering filter is just horrible, and the sensor is actually capable of much better quality.)

I feel like there's a meaningful difference between that stuff and the computational photography common on smartphones.

There may be a meaningful difference between demosaicing and generative AI - actually there isn't because demosaicing/upscaling/image generation are all the same problem, but there might be one since people like to think of them as different.

There isn't a difference between auto white balance and generative AI though. The colors in an auto mode digital camera picture are not real.

You can only get real colours in a raw format digital picture.

Not really. First of all the colours aren't "real" even in that raw, and second if you accept that as real, you can get per-pixel chroma information with Foveon sensors, pixel-shift systems, monochrome sensors with lens filters...

Well, a more fundamental issue is that the colors of an object in a scene aren't "real" if the lighting in the scene is not daylight white. And most of the time you're not there to appreciate the low-CRI yellow indoor lights.

I like that > Like any interesting pursuit, scientific publishing struggles to keep up with developments in technology.

I'm going to keep that in mind, there does seem to be this interesting human nature presumption that everyone keeps in sync with the latest and greatest. But that's simply not the case.

Astronomy is in for a hard time then, anything that uses false color is technically very much processed.

This is certainly not a "no processing" policy.

Where the line is drawn as to what's "generative" and what's "AI" may be blurry, but they haven't just banned traditional transform operations.

I think if it gets all the way to a computer model rendering, where the raw data input is in no way shaped like an image “yet”, the distinction between traditional and new-generative-model approaches sounds more like a difference in degree.

passing photons through a filter in front of the sensor is absolutely not even close to being the same as "AI" post processing of the data.

But the thing they pulled to give us the "photo" of that black hole absolutely is.

I’d say “raw” is light imprinted on film. I may be biased but wouldn’t mind seeing 35mm make a comeback.

that's not what "raw" means though, and this is a really weird interpretation

> I may be biased but wouldn’t mind seeing 35mm make a comeback.

It's on the rise for the past years

Yes, me too. I have a Minolta Rangefinder in a box that needs the crank spring fixed. I've kept it because it captures the lighting exactly like I see it with my eyes. I would get the best night/ twighlight/ evening pictures with it and I would specify no color correction at the developer. I don't think digital photographers understand what it is to capture the light'. I have a newer Minolta in working condition that also takes excellent quality pictures.

I mean, in digital photography "raw" has a very well defined meaning: https://en.wikipedia.org/wiki/Raw_image_format

Since enhancements (and “enhancements”) are going to get more pervasive, it feels like a good time for smartphones and cameras to add a “scientific” setting that only stores the unprocessed sensor data.

“Generative AI.” I know there are kind of weird edge cases. “My iPhone made the sunset way redder than it was in real life.” But I think we all know what they’re talking about and I suspect if you’re in a position for it to really be a concern then you will communicate with Nature and sort out what their comfort zone is.

This is fascinating and gets pretty interesting. As a computational neuroscience person, some of the more advanced neural signal processing algorithms use generate models internally to model recorded neural data. The result is likely a smoothed, simplified, and hopefully more interpretable view of neural data, but there's no guarantee that some portion of the resulting multidimensional signal isn't hallucinated.

As a result, most findings should be validated by verifying that some property of interest is present in the high dimensional raw neural data, though that's only conceptually possible sometimes.

> …AI-based generative/corrective algorithms…

Every photo is touched by "corrective algorithms". Nature is talking about generative AI specifically, which means using an LLM to generate part or all of an image. This precludes using Midjourney, Photoshop's new "generative fill", etc.

I assume that what Samsung's "Space Zoom" feature does — replacing elements with higher-quality stock photography — was already disallowed. If so, whether the elements were identified/replaced manually or automatically isn't really a concern from an editorial perspective.

Yes, they already had guidelines for photographs. [1]

e.g. "Digital images submitted with a manuscript for review should be minimally processed. A certain degree of image processing is acceptable for publication (and for some experiments, fields and techniques is unavoidable), but the final image must correctly represent the original data and conform to community standards. Editors may use software to screen images for manipulation."

[1] https://www.nature.com/nature-portfolio/editorial-policies/i...

Sounds like this would allow for processed astronomical photography too. Methinks this questions wasn’t in earnest.

No reason to assume bad faith. But some folks get very literal. And if you have a legitimate question, that's one of the things editors are for.

How might one use a Large Language Model “to generate part or all of an image”?

Something like this:

    Hey ChatGPT, write a prompt for midjourney to generate a realistic photo of XYZ with ABC parameters.

Then plug it into Mid-journey.

Technically the LLM isn't generating the image, and I agree, but I think their point is rather obvious and we need not be intentionally obtuse nor needlessly pedantic.

'Text-to-image' systems like Stable Diffusion really are Large Language Models: an encoder such as BERT creates a mapping from text tokens to the latent space of the image generation model. As part of this training step, the system is learning the concepts of certain words and grammatical constructs.

There are quite a few in-depth explanations of the whole system; here's one for instance: https://jalammar.github.io/illustrated-stable-diffusion/

I think you can argue that there is significant daylight between "created wholly or partly using generative AI" and the sort of ML-based noise reduction, sharpening, etc. that you see in products like Lightroom and Photoshop. Of course, the whole area will evolve and rules like these will have to evolve as well. News photography has dealt with this since pre-digital although different publications may have different standards.

They'll have to use an AI to determine what manipulation counts as AI.

There is also a big distinction between fully disclosing that an image has AI vs the non post production edits. So why not just ask people to just be honest and disclose about the means of creating the content and disclose the images as being "AI" created vs telling people they should to be purists in their craft. Is the claim that the art made by AI itself is harmful or that the act of making it causes harm. Where is the real harm in this given people are properly informed as the the type of content they are looking at? The reasons why is because this has nothing to do with art and expression and everything to do with control under the guise of fear.

For them purposely asking stable diffusion(not ok) for an image vs iPhone image processing(ok) would be your baseline for distinguishing. Picking at small details seem like a nice way to waste time, you just got to keep it simple

> devices that apply by default some form of AI-based generative/corrective algorithms like the "AI detail enhancement engine"

Isn't this a contradiction, though? My understanding is that generative AI is created entirely from software, using a network of previously created images as input. A corrective filter modifies an image taken directly from a sensor instead.

I personally don't mind aesthetic corrective modifications to photos. I was an astronomy observatory last night and learned that most of the magnificent images we've seen of distant nebulas and galaxies have post-production coloring applied, they mostly look black and white coming off the sensor. Does the coloring fundamentally change our understanding of what it is that we're looking at? I don't think so, and that's where I draw the line.

>Artists, filmmakers, illustrators and photographers whom we commission and work with will be asked to confirm that none of the work they submit has been generated or augmented using generative AI

"or augmented"

There's no hard line in the technology. This means that a ban is pointless because the landscape is going to keep changing.

It's interesting to compare this to other situations where, say, law tries to create lines that aren't really there and the incentive to ignore imaginary ones is greater than the incentive to keep them.

This seems to be a very common phenomenon with technology.

In the case of Nature, it functions as a statement of values that scientists publishing with Nature will be happy to comply with to the best of their abilities.

I doubt that the editors are under some illusion that the nominal ban will create a hard line in reality. I'd be surprised to learn that that is their idea of success with this measure.

The context matters. There are image manipulations I might do to a photo I'm going to hang on the wall that wouldn't be kosher if I were shooting an event for a newspaper especially with respect to removing objects from the photo.

Some generative AI tools let you input a base image to work off of. You can definitely use generative AI for just sharpening in these tools.

> My understanding is that generative AI is created entirely from software, using a network of previously created images as input. A corrective filter modifies an image taken directly from a sensor instead.

Your understanding is incorrect, generative AI can modify an image taken from any source, as well as creating from scratch.

It was not a problem when presenting the EHT results and subsequent "images" - https://www.space.com/first-ever-black-hole-image-ai-makeove...

Were those produced by a generative AI?

No, but under the same algorithmic principles. How do you think that yellow color come about?

The part of the article discussing problems with generative AI seems to lay emphasis on "attribution". The problem with generative AI is not that they want raw and unedited images, but that they are unable to properly attribute generated images to their sources. That's what makes the distinction between a generative AI and regular image editing tools like cropping, scaling, filtering, etc. So something like "AI detail enhancement engine" should not be problematic under such a definition as the tool can simply be attributed to Samsung.

Probably close to 100% at this point.

I am not in the game at all but as I understand it Samsung doesn't make high quality DSLRs of the type used by photographers. I reckon that photographers would be asked to disclose this sort of thing if submitting to Nature in future.

> what are the chances they already publish photos taken on devices that apply by default some form of AI-based generative/corrective algorithms

100%?

On the other hand, I really hate what these algorithms do to my photos, even my new iPhone which is considered good tech. So I get it.

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