Baroness Lubinska who presided over the famous duel between Princess Pauline Metternich and the Countess Kielmannsegg in 1892, insisted that the duelists remove their clothing above their waists to avoid infection in the event that a sword pushed clothing into the wound it caused. Being a doctor, the baroness had seen many instances of septic infection in soldiers for this very reason throughout her years of medical training.
OP: Why couldn’t traditional Chinese Yinpiao银票/silver drafts be forged if they were merely slips of paper? (cr大明宝钞，渐越)
Traditional Chinese yinpiao/silver drafts were paper vouchers issued by private banks starting from the Song Dynasty(960–1279). People could exchange these slips for physical silver at bank branches across the country.
Silver drafts were made in multiple copies with matching serrated seal edges. One copy went to the customer and others stayed at the bank. All edges had to fit perfectly together to withdraw silver. The unique split edge marks were almost impossible to copy.
This mechanism is known as qifeng骑缝 (split-joint seal) in China. It first originated in the Western Zhou Dynasty (1046–771 BC). The Rites of Zhou records that contracts were written on bamboo or wooden slips in duplicate. Notches and marks were carved in the middle before splitting the slips, with each party keeping one half. The two halves would be matched by their notches for verification.
During the Spring and Autumn and Warring States periods (770–221 BC), this idea evolved into hufu虎符/tiger tally tokens. A military tally was split into two pieces with identical inscriptions carved along the split edge. Troops could only be deployed if the patterns and characters on both halves perfectly aligned, serving as a metal version of the split-joint anti-counterfeiting system.
The technology matured in the Tang Dynasty (618–907). Government documents and private contracts commonly used split-joint seals stamped across the dividing line. The Chinese character "hetong合同" (contract) was written across the middle before the paper was torn apart, so the complete characters would only appear when the two halves were put together. This split-coupon system was later adopted for Song Dynasty (960–1279) jiaozi paper money and yinpiao/silver drafts of the Ming and Qing dynasties (1368–1912).
Official Song dynasty paper money (Jiaozi交子) was abolished in 1107. Private silver drafts issued by Qing-era piaohao票行 (ancient exchange banks) vanished completely in 1951, hit hard by modern banks and currency reforms. Nowadays silver drafts no longer circulate as currency. Their collectible value depends on their rarity and physical condition.
Split-joint seals (骑缝章qifengzhang)are still widely used on important paper documents in modern China, an anti-tampering technique passed down from ancient times. They are applied across the edge of multi-page contracts, bidding documents and official archives. If any page is removed or replaced, the broken seal pattern can prove the file has been altered.
OMG I got so excited about this because they used a really similar (though far less refined) version of this for contracts in the European medieval period!
First they were called "chirographs", but later the word "indenture" (in its earliest meaning as just a legal document of any kind between two people) came to be used, originating from the practice of a contract being written twice on a single piece of parchment and then cut in half with serrated edges (as in dent, "teeth" -> indents -> indenture) in order for each party to take one half, so they could later piece them together and verify that there had been no forgery -- same as the Chinese silver drafts!
(Charter of the Clerecía de Ledesma, 1252, showing the serrated indents at the top -- presumably they are cutting rather than tearing because they're using parchment, which I expect is much harder to tear than wood-pulp paper like the Chinese were using)
Delights me when human beings find similar ways to solve the same problem at two different ends of the world. <3
its terrible for any number of reasons, but i think if we invent immortality there should be an extreme sport called civilizational speedrunning where teams of 20 go into the wilderness somewhere and try and be the fastest build the first internal combustion engine. i bet you could get it down to like 3 years tops
I got a job at a gas station across from a casino and a guy keeps coming in to buy whole cartons of cigarettes and talks in the third person calling himself "snake eyes pete" and every time I tell him his total hes like "tell ya what if old Pete rolls a snake eyes why dont you give it to him for free?" and he rolls the same set of loaded dice on the counter and it does not matter how many times he rolls snake eyes I will not give him the cartons for free he gets so mad
Snakeyes Pete is from Toronto he gave me a button once that said "Canadians have the best luck" and i said i thought that was the Irish and he said "I fucking hate the irish" and i said okay
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
They say that sudden, intense cravings for very specific foods are usually a sign of a vitamin deficiency. If so, that I'm presently making myself french toast, scrambled eggs, and a bacon and cheese sandwich at 11:45 PM must mean the vitamin I'm missing is grease.
I cross-posted this to Bluesky and got followed by a bunch of folks with blog headers clarifying which exact flavour of Christianity they adhere to because they thought I was making a hashtag-relatable post about cheating on your diet. Now we get find out how many of them are also comfortable with posts about werewolf cock.
I'm sure that's true, but something tells me googling "nun tied up by other nuns" "hot wax" "confessing sins" will likely get what you might call rather un-academic results
