realizing many people don't know about infinity train creator owen dennis' among us show from years ago, which has been trapped in unreleased limbo all this time and was just dumped on streaming this morning with no advertisement. they don't even know about its weirdly stacked cast
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.
Eridian Grace, as in culturally Eridian, not the species Eridian, is so important to me.
Grace who does jazz hands and taps when he's asking a question and says some words three times like Rocky. Grace who makes chirps and trilling noises when he's happy.
Grace who starts thinking and dreaming in Eridian, who sometimes has dreams that are just sound, no visual. (Grace who, if he ever goes back to Earth for whatever reason after years on Erid, forgets some words in English and speaks in the sentence structure of Eridian because he has to translate from Eridian to English when he speaks outloud).
Grace who does scarification (even if Rocky protests at first) - I've read some fics where he does the Voyager pulsar map, or dots and lines showing his route from Sol, to Tau Ceti, turning around towards Sol, and then the line going all the way back to Erid. Even though he already has the scars from Rocky saving him, maybe Rocky's family crest. A scar showing his rank as Captain of the Hail Mary - maybe something to represent her, too.
Grace who wears Eridian gems, as earrings, in a chain for his glasses, in strings draped over him that clink together when he walks - the most important ones, like the earrings he wears all the time, the color of the blueish-green gems (or spots?) in/on Rocky's carapace.
Grace who wears a bracelet on each arm that have ribbed marks like on Rocky's arm so he can properly say goodbye (not something he had to do, but something that felt right to do, something that made him feel more like himself), sound included.
Grace who considers himself an Eridian!! Grace who IS an Eridian!!
I'm baffled that some people seem to think this video/post is a joke & find it funny that someone ascribed emotions & music to the salmons' journey, as if that's new. Humans around the world have told stories about salmon since time immemorial. Salmon are sacred symbols of life, loss, and recovery. In many places, depletions tell stories of colonialism and genocide, and their recovery is a victory hard-won by the actions of Indigenous and colonized Peoples.
It's impossible to overstate the necessity Salmon have been to entire cultures globally for tens of thousands of years.
I get that the humor around this video may not be "that deep," but capitalism and colonialism divide us all from the sacredness and inherent value of all lives. Many of us have become completely disconnected from the cycles of life which support us every day and from the food that nourishes us. If a music edit about salmon moves you, hold onto that. Foster that! Salmon have always moved us.
Listen to a story in Tlingit about the Salmon People:
Shanyaak’utlaax̱: Salmon Boy Told in Tlingit (with English Subtitles) - YouTube
Find stories about Salmon in the Irish Folklore Collection:
Bailiúchán béaloidis é seo a chnuasaigh páistí scoile in Éirinn le linn na 1930idí.
This collection includes every aspect of the Irish oral tradition.
A post containing many links to read about Indigenous TK (Traditional Knowledge) as valid scientific methodologies:
Tumblr is a place to express yourself, discover yourself, and bond over the stuff you love. It's where your interests connect you with your
Read about what Salmon are to Peoples of the North Pacific, from the Ainu people to the Ulchi people:
Information on Indigenous salmon management, research, history, and long-term plans:
There's this really obscure forgotten DC hero named the Heckler, who's basically buggs bunny as a superhero, not having any powers or physically strong, but just really good at pissing people off until they accidentally deal with themselves.
Now they're interesting, but the REAL star of the show is one of his villains, John Doe the Generic Man, who's this guy in a stark white suit with flat pink unshaded, untextured skin with no features or anything who talks like chatGPT and has black text over his face that explains what he's feeling at the moment. That guy is fucking fascinating.
I first heard about this guy from the "League of regrettable superheroes" Books, (The supervillain one, obviously) and He stuck with me because its such an interesting concept. not only is HE generic, but he has the power to make anything he TOUCHES generic too. I never actually got to experience his whole deal as an actual character, since this was just an info book that tells you about the character, so seeing these panels it really cool.
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vaporwave
kianamaiart
jbbartram-illu
baratheonpilled
2087
dededos
