Ellie 21 she/it trans girl!!!!!!! I am a cat and a robot and a mech pilot :3 I like planes, rockets, cats, jerma, and girls. dm me!! I am addicted to making friends :3 NSFW blog, some hard kink. silly silly :3 (autism)
im ellie, 21, she/it! poly plural pilot, robotgirl, and kittygirl extraordinaire. i am also agnes tachyon.
this is gonna be my blog for when i rly like something or have something to add to a post, or for original posts :3 i feel like i reblog too much stuff and it buries the stuff i really like so i made a sideblog for most of my reblogs! @robotgirlellie-spam! hopefully that'll keep the quality on this blog higher.
nsfw blog + untagged kink, usually not anything graphic but i get horny on main often enough that you should expect it. feel free to bother me! i love talking to people smmmm and im addicted to making friends! tell me about your interests or how youre doing or literally anything you want at any time <3
ok tag list:
#my poasts - my poasts
#my opinions - my opinions
#faves - cherished posts
will add to this post as needed, mwah love u all <3333
it's easy to make fun of marxists for being like "we have a special way of understanding society called Thinking About Things That Are Real" but to be fair if you ask the average person what their ideas about society are based on they will start listing some of the Least Real things to ever not exist
anyway, I know this post is mostly aimed at people who think history is secretly a 6,000-year-long metaphysical turf war orchestrated by The Jewsâą, or who read every political event as a religious morality play with different costumes, but academia absolutely has its own version of this impulse.
the respectable academic variant, especially in the post-Cold War West, was the enormous cloud of intellectual febreze called post-structuralism. a lot of post-structural approaches, postcolonialism, liberal feminism, green studies, discourse-oriented theory generally, became institutionally dominant during the late neoliberal turn of the 1980s and 1990s, precisely when capitalism itself was reorganising into more decentralised, flexible forms of accumulation. suddenly theory is less about totalising structures and political economy, and more about fragmented subjectivities, localised identities, fluid meanings, discourse, hybridity, micro-narratives. which is not to say these frameworks are useless or wrong in their entirety, because many produced genuinely valuable critiques, but it is hard not to notice how neatly their intellectual grammar mirrored the restructuring of capitalism itself. academia became increasingly comfortable with critiques that could be endlessly discussed, deconstructed, and circulated without necessarily threatening the underlying organisation of production or power. the sharper material edges get softened into language that institutions can metabolise safely. and honestly, this is probably the fate of almost every new theory that enters academia; eventually it stops being insurgent and becomes part of the furniture because everyone still needs salaries, grants, tenure, conference invitations, etc.
these murderous idiots are teaching young girls and women to be afraid of calories when eating a LOT of calories is literally vital for performing well in competitive sports. and then claiming it's 'natural' to uphold sex segregation bc of this imposed malnutrition. it's fucking crazymaking.
the mechanics here are truly horrific. systematically deny [cis] women athletes adequate nutrition and hydration, overwork and injure them. deprive them of the ability to realize the true extent of their strength and skill. segregate athletics based on false ideas of "sex" based on generations of malnourishment and poor training. blame trans woman athletes for the mere possibility that one might beat a cis woman, while claiming that the cis woman's defeat is inevitable. create emotionally stressful and often sexually humiliating tests to "purify" competitive athletics rather than foster the physical health of athletes. continue punishing all women for daring to play sports + have bodies.
Some people might think Iâm an optimist but Iâm not. Iâm a realist thatâs going to try to increase the good things in this world if it kills me.
I want to severely push back on the idea that cynicism is any more realistic than optimism. There are good things in the world and there are bad things in the world. Totally ignoring one or the other doesnât make you more correct.
You also have the power in your individual life to crowbar the long arc of the universe towards justice. Donât just expect that things will be bad or good. Start yanking. Beat the darkness back with a stick, dammit.
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.
nothing more frustrating than recognizing an accessibility problem that could easily be solved by clever and reasonable use of LLMs in a good and just society, and knowing for certain that it will never be solved in this way.
dude stop trying to garner context and character traits from the objects in my room i know youre doing it. stop clicking on shit im not gonna tell you about - oh that picture is of me and my dad. yeah he's not really in my life anymore i just keep it around cause im sentimental- DUDE
