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2026-11-11 15:03
"One time, Mr. Fujino called me into his laboratory, pulled out a picture from my handout, which showed the blood vessels in the lower arm. He pointed at it and said kindly to me: "Look, you moved this blood vessel a little bit. Of course, it does look better if you move it like this. However, the anatomy diagram is not art. The real thing is like that, and we can't change it. Now that I have changed it for you, you will have to follow the picture on the blackboard in the future."
This is the article "Mr. Fujino" in Lu Xun's collection of essays "Morning Blossoms Plucked at Dusk". It is a text that every student who has attended junior high school will read. Looking at it again today, it is somewhat ridiculous - that is, Mr. Fujino has never seen AI.
Today, you don’t need to go out into the wild to be exposed to wind and rain, and you don’t need to understand the anatomy of birds. As long as you have a computer and open 8 windows to run an AI script, you can draw all the living birds in the world in less than a week. As for whether it is true or not, it doesn’t matter at all.
This is the "Uncle Hua Global Bird Sticker Library" that has recently been a high-profile hit in the developer community, but has made domestic and foreign bird enthusiasts so angry that their blood pressure has soared. Just reading the author's own project review article, there is a kind of excitement that "powerful computing power can produce miracles" is permeated between the lines.
In order to make supporting materials for his bird watching app, this independent developer who cannot write code relied on Vibe Coding to burn more than 1 billion Tokens, ran out of the weekly quota of US$200 top-level membership several times, and made 11,064 living bird species in the world (later updated to 11,011 species and 91 subspecies) into white-edged stickers, and conveniently built a support 15 A bird website that can be searched in multiple languages, and announced to the entire network that it is "officially open source".
Then it flipped over immediately.
Picking up pictures and drawing blindly, a "bird-making assembly line"
A closer look at the production black box of this set of thousands of bird stickers is enough to make science communicators and field photographers shudder.
The first is the almost "clear robbery" of grabbing and stealing reference pictures. It is very easy to make things up when AI draws birds out of thin air. The author is not a bird-watching enthusiast and does not have his own database. How to solve the problem? Write a script to go to iNaturalist and Wikimedia Commons, pluck out the real photos taken by photographers around the world in batches, put them together into a 3×3 "nine-square grid", and feed it to the raw model for redrawing.
Among the 11,064 reference photos of bird species, only 1,986 species actually adopted open licenses (CC0, CC BY, CC BY-SA). The rest were either clearly marked "Non-Commercial Use," "No Derivatives" or "All Rights Reserved." There were even 2,017 species that were stuffed into the AI without even writing down the license agreement.
After washing it into AI stickers, the author backhandedly stamped the stickers with his own authorization label (initially even using PolyForm Shield 1.0.0, which restricted the use of competing products, and later changed to CC BY 4.0), and then left a sentence: If a sticker refers to a photo you took and you don’t want us to use it, send an email to raise an issue, and it will be removed within 7 days.
This logic of "feeding the photographer's work to AI first, and then deleting it after being visited by the owner" was directly criticized in the r/birds section of Reddit's bird-watching community. "Feeding other people's work into the plagiarism machine has never been something that can be washed away by "applying to withdraw afterwards."
In order to forcibly collect more than 10,000 birds in a few days, there is another method of Thunder: If you can't find good photos of unpopular birds, you will take a few incomplete photos and piece them together; after the model is painted, it will be handed over to another AI reviewer for scoring. During the test, the author himself found that the raw drawing model frequently created ridiculous monsters - irises that were obviously red or blue-green were painted as lifeless black eyes; the toes melted into a ball of flesh; even a bird with extremely short legs like a hummingbird was forcibly attached to a pair of long sparrow-like legs.
So what should I do if I redraw it twice and still get it wrong? The principle set by the author is not to return photos, nor to leave them blank, and only those with high scores will be kept.
Using pure engineering thinking to avoid popular science issues, in the first-generation open source warehouse, there were as many as 3,388 defective products that did not even pass the AI passing line (80 points) set by the project itself (109 of which were even lower than 70 points), but they were still packaged and stuffed into the public image gallery. Although the author later re-ran more than 10,000 images using another set of AI standards in November and declared that all of them "passed the line," from beginning to end, this set of images had not been systematically reviewed by even an ornithological expert.
What exactly is poisonous about "popular science pollution"?
Despite a lot of shortcomings, I still enthusiastically recommend that everyone use these stickers to "make nature education courseware and make bird cards for children." It even provides a CDN interface that can be called with one line of code and structured data bound to eBird and GBIF scientific research numbers; I turned around and added a thick layer of disclaimer armor to myself in the instruction document.
"These are illustrations, not illustrated plates... If you find that the drawings are wrong, you are welcome to submit an issue. Corrections from bird friends are the most needed contribution to this project."
This is the biggest controversial point in the entire open source project: whether you have the ability to verify more than 10,000 images is a matter of ability, but whether you have thought about doing professional verification before launching is a matter of attitude. The author has never admitted directly that the production of a set of bird knowledge products requires professional knowledge in the fields of ornithology and bird watching, and that monitoring is a step that must be done after the release.
This idea is very common in code development. Programming - go online - receive issues, then modify - iterate. As a developer, as long as you develop tools, generate pictures, and open the code, your work is completed, even if it is just vibe coding. As for whether the knowledge is accurate, you can leave it to users and professional communities to correct it. This kind of thinking is a disaster in the field of natural sciences.
Take the peregrine falcon, known as the "bird of prey" in the air, as an example. If you only look at the sticker map, a layman can't see the problem at all, especially since the entire drawing style is a realistic painting style, not animation or simple line drawings.
After carefully looking at the comparison pictures posted by bird friends, we can find that the peregrine falcon, an aerial raptor that originally had thick tarsometatarsus and strong claws and toes, has been twisted into a smaller claw shape. After checking back, the author admitted that the original reference photo was taken from below a tree, and the peregrine falcon's feet were blocked by abdominal feathers and branches. When the script automatically cut out the image and removed the branches, the AI could only make up a pair of thin claws, and the AI responsible for reviewing the image did not check the ratio of the thickness of the legs and feet at all.
The sunflower parrot originally inspired the author to initiate the project and is his "favorite bird".
But after drawing, the most basic folding wing feather area (the hierarchical relationship between coverts and flight feathers) is wrong.
The statement "it is an illustration, not a illustrated book" cannot be exempted from liability. The reason is not that "bird illustrated books cannot be drawn". Traditional hand-drawn illustrated books are not equivalent to photography. But the bottom line of hand-drawn choices is that every stroke is rooted in the real anatomy of the species and is checked by knowledgeable people. Excellent naturalist painters will combine long-term observations of multiple individuals and deliberately highlight the identifying characteristics of tail feathers, beak shape, or moult period.
Not just fake bird stickers, AI fake science popularization is "poisoning" on multiple fronts
If it were just an independent developer's self-pleasure, things might not be so scary. However, such outrageous incidents of using AI to tamper with and fabricate natural images, thereby contaminating scientific records and popular science, are no longer just one or two incidents.
An academic communication published this year in Nature Ecology \& Evolution recorded a case that sounded the alarm: a birdwatcher photographed a slightly blurry bird in Brazil. In order to see it more clearly, he submitted the photo to Google's AI image tool to improve the image quality, and then uploaded the processed high-definition image to iNaturalist, one of the world's largest citizen science platforms.
As a result, this photo was once regarded as a new distribution record of the rare local "red-winged blackbird"; it was not until experts carefully compared the original photo that they discovered that it was actually just another extremely common local oriole. It was AI that added the feather characteristics of the red-winged blackbird out of thin air in the process of "clearing" the bird - just changed a little color and adjusted the size, and it was already a different bird.
The subjective intention of this birdwatcher is just enhancement and high definition, and has no other intentions. But it is precisely this kind of "inadvertent mistake" that is easier to sneak into the underlying chain of human knowledge than drawing a fake bird out of thin air. The natural images uploaded by the public are not only for others to identify, but also directly serve as scientific evidence for global species distribution research.
For example, eBird photos will enter the Macaulay Library of the Cornell Lab of Ornithology and feed back the image and sound recognition training set of Merlin, a famous bird identification tool. If the bird in the picture has its feather color, toes or beak shape quietly changed by AI, the error will never stop at a distorted photo. It will form a series of domino reactions, changing how subsequent researchers and AI tools understand the real nature.
In the field of public science publishing, the rate of loss is equally alarming.
At the end of October this year, the "South China Sea Marine Life Popular Science Series" published by China Agricultural Press was openly questioned by readers. On the cover and inside illustrations of the volume introducing Penaeus monodon, a strange "two-headed shrimp" with two heads suddenly appeared.
Faced with ridicule from the entire Internet, people opened the preface of the book and were shocked to find that it said: "The vast majority of the pictures in this series of books were generated by AI drawing tools." In the end, the staff of the publishing house involved had to respond and said that they were notifying the entire network to remove the book and recall the book, and Xinhuanet publicly criticized it.
A "two-headed shrimp" with two heads is too scary and can be picked out by readers at a glance, but those AI popular science illustrations that only draw a wrong wing vein, add an extra toe, or tamper with the tail feather markings, once they are formally typeset and printed, and put on the shelves of bookstores with the authoritative aura of "popular science books", how should ordinary readers and children distinguish them?
For serious scientific records, sometimes the most dangerous images do not even have the appearance of "AI illustrations", but sneak into the database under the guise of "real photos".
Return the face of nature to patient observation
In the face of the surge of generative images, professional natural observation databases have been building high walls, giving a much harder access red line than "please use AI with caution".
For example, eBird and the Macaulay Library explicitly prohibit the uploading of any AI-generated images, sounds, and videos, and the use of generative tools to augment or reconstruct the original feathers, eyes, and other details of the photo is strictly prohibited; only cropping, general exposure and color adjustments, and slight noise reduction that does not falsify details are allowed. In the event of violation, the material will be labeled as "over-edited" or "artificially generated", and accounts with repeated violations may even have their permissions restricted. The core of its judgment is not which photo editing software you used, but whether the uploaded object can be used as real evidence of the local biological individual at that time.
iNaturalist has also launched a targeted interception mechanism. Once the community discovers photos or recordings that are mainly generated by AI, they can mark them as "artificially generated content" and the original images will be replaced with warning images. The observation record will also be directly downgraded to "Casual" and permanently kicked out of the "Research Grade" sequence. For cases where real photos are distorted due to AI retouching, a special data quality assessment channel will be used. Since iNaturalist mainly shares licensed research-grade records with the Global Biodiversity Information Platform (GBIF), this downgrade mechanism is equivalent to putting brake pads on the source level of scientific research data.
These platform rules at least defend a bottom line: completely separate "good-looking" and "can testify". An original photo that is as blurry as a mosaic can still be an honest scientific record; while a large high-definition photo with delicate feathers and eyelights filled in by AI has no value in the face of scientific evidence.
However, the defense of a professional database is far from enough. The identification of fake images still relies heavily on human professional vision. Proof of origin cannot replace the review of content accuracy. "Bad money drives out good money" on the public Internet, making it difficult to contain "poisoning" operations.
In citizen science databases, a picture is first of all an on-site evidence; in museum illustrations and popular science books, an illustration is a professionally verified knowledge contract; and on social entertainment platforms, it becomes a post that attracts traffic, a fashion item that says "I am good at using AI".
"Computing power can replace expertise" is full of arrogance. Without the control of experts in the field and the authorization of original photographic works, people cannot, or at least should not, regard rigorous natural science as a place to show off Token consumption and AI skills competitions, nor should they transfer serious scientific review obligations to the community for "free demining."
Nature has never been a script that can be generated in batches by prompt words. In this era where generating 10,000 pictures is much easier than going to the park to identify a bird, we need to be more cautious than ever before.
Selina
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