(Some of) the professions that women could have in medieval Armenia
1. Վարձակ - Vardzak
An actress, singer and dancer in ancient and medieval Armenian theatre. Vardzak performed either naked or semi-naked.
2. Լալկան կանայք - Mournful women
These women sang sorrowful songs during burial ceremonies and were paid for it. They also wrote the songs. The lead singer was known as Եղերամայր 'Yegheramayr'. The Armenian medieval poet Grigor Narekatsi wrote that he had learned tear-inducing lines from them. This profession was practised up until the 19th century.
3. Դյութ (Dyut)֊ Witch/Warlock
Dyut could be anyone who was literate, including both women and men. They were considered to be those who used different substances and objects, such as flour, stones, water and sand.
4. Թովիչ (tovich) - Enchanter
These women were said to be able to cure illnesses through their sweet and enchanting speech. They "cured" rib pain by rubbing a sword on the patient's ribs, and they "cured" jaundice by slapping the patient without warning to make the illness run away.
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.
So whats the real difference between western and eastern armenian? And if you speak one can you communicate with the other or would it be like an english speaker trying to understand scots?
standard/literary western and eastern armenian are not very different. just some minor grammar and vocabulary differences—things like how verbs are formed and how nouns are inflected. one can understand the other just fine.
My family is mixed, with one of my parents raised with eastern and the other with western. my siblings and i speak both, but went to school in the diaspora, so we learned western armenian grammar and vocabulary. I myself consider western armenian my native language.
i thought i knew eastern armenian until i had to go to high school in armenia. there was a learning curve which frustrated me — i thought i spoke this version of my language, why am i having such difficulty applying it to science and literature and analysis?
i got told a few times that i have an audible accent and that its “cute”, which was well intentioned as most Hayasdantsi armenians i’ve met (at least those close to my age) are very interested in western armenian, but hearing that annoyed me every time. not that i’m ashamed of speaking western armenian — again, as someone who fluently spoke both, i was frustrated with the differences that remained.
I learned the slang. I learned which russian expressions and words to pepper into my speech. to this day, a native eastern speaker can safely identify that i am from the diaspora, but i suppose all that matters is that communication is entirely doable. the difference in pronunciation and some differences in sentence structure are what’s most likely to throw you off, but otherwise, as long as slang is limited, a western armenian speaker in armenia will have no trouble communicating.
i have a question for you: is նմանիլ used for physical characteristics only (like Աղջիկին աչքերը հօրը կը նմանին) or for actions being similiar too? (eg. Տղուն վազելը պապային կը նմանի)
hello sireli mutual! dw about the dhmu shad lurch cher lmao
նմանիլ is okay to use for both physical characteristics and actions, but your example sentences are missing the objects!
Աղջիկին աչքերը հօր աչքերուն կը նմանին։
Տղուն վազելը պապային վազելուն կը նմանի։
I’ll double check with a more credible source later but as long as you specify the object of comparison, you should be fine. 💛
You are a disapointment for the Armenian culture you fucking idiot faggot twink, i am ashamed every time i see you 'represent our culture' go fuck yourself
when we talk about հոլովում (declension), we categorize them by how the noun inflection changes in the genitive/dative case (because only the gen/dat changes between types of declension). so ի հոլովում, means that we add an ի in the genitive and dative case; the rest of the cases don't change.
at least, that's the case for modern armenian. classical armenian, of course, absolutely had to make an already complicated language even worse. aside from declining adjectives too, classical armenian had a few extra things going with its nouns.
they still had ի, ոյ, ոջ, օ, եան, ուան, but also այ, ին/ան (which evolved into -ան), and some irregular stuff that modern armenian inherited.
of course, that's not to say that everything is irregular. again, we categorize these declensions by their change in the genitive and dative case.
for example աթոռ, belonging to the ոյ հոլովում, becomes աթոռոյ
որդն in the ան հոլովում becomes որդան
some nouns are one type in the singular and another in the plural; կամ -> կամի; կամք -> կամաց
the plurals have their own funky stuff and there's also a unique set of rules for nouns that end in ր and ղ. krapar.org does a way better job of explaining it so i think it's worth it to check out their page on the topic
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