The great man turns his back on the island.
Now he will not die in paradise
nor hear again
the lutes of paradise among the olive trees,
by the clear pools under the cypresses. Time
begins now, in which he hears again
that pulse which is the narrative
sea, at dawn when its pull is strongest.
What has brought us here
will lead us away; our ship
sways in the tinted harbor water.
Now the spell is ended.
Give him back his life,
sea that can only move forward.
it does suck that the government defunded PBS but it's also so fucking funny that now that they don't take uncle sam's slavery dollars they're running videos like "How america's foundation was built on genocide"
PBS Origins my beloved! for the unfamiliar, channel link here. they've been pointing out how fucked up USA history is for a while, but not quite that overtly.
PBS Origins is the home of history shows from PBS Digital Studios. Subscribe to dive into inclusive, intersectional history content that hel
link to the specific video from the screenshot above here:
it's part of their series "A People's History of Native America," playlist link here.
Hosted by comedian and actor Tai Leclaire, A People's History of Native America is a series that explores the current social climate in Nati
and while I'm here I'll plug some other channels because PBS does solid work. also, iirc they are (...were? I'm not actually sure what applies to them now that they've been defunded) legally required to include captions and they actually do that, so you won't run into auto-generated nonsense.
I haven't checked out PBS Documentaries yet, but they have some stuff tackling similar topics. (I am adding things to my watchlist as we speak.) channel link here.
Welcome to the PBS Documentaries channel—presented by PBS Digital Studios and Independent Television Service (ITVS), dedicated to documentin
PBS Terra doesn't pull punches on climate change. channel link here.
PBS Terra is the home of science and nature shows from PBS Digital Studios. Subscribe to explore the frontiers of science and tech, our mind
PBS Eons has some super cool videos on the history of life on Earth, channel here, and Storied does awesome work on linguistics and mythology, channel here.
Join hosts Kallie Moore, Michelle Barboza-Ramirez, Gabriel Santos, and Blake de Pastino as they take you on a journey through the history of
Storied is the home for arts and humanities shows from PBS Digital Studios. Subscribe to explore art, culture, mythology and much more!
The
aaand while we're talking about defunded USA public media that doesn't pull punches when critiquing our history and government, I am once again going to plug a couple NPR podcasts. Throughline does deep-dives on history, culture, laws, and so on (link here); I'm especially partial to their We the People miniseries, which covers our rights from the Amendments. Code Switch covers culture, focusing on race and minority groups, and has been doing some especially good coverage on what the Trump administration's fuckery means on a practical level (link here). (these aren't the only NPR podcasts that talk about this stuff, but they're the big ones afaik.)
Throughline is a time machine. Each episode, we travel beyond the headlines to answer the question, "How did we get here?" We use sound and
What's CODE SWITCH? It's the fearless conversations about race that you've been waiting for. Hosted by journalists of color, our podcast tac
Found myself wanting to say that "consuming text is easy and passive, unlike video which requires active effort to watch" and then realized this is the opposite of what basically every other human being would say.
reading a historical romance novel and reflecting on the way these stories often present woke nobility for the contemporary reader. a big thing is servants. you can’t not have servants in those times but many modern readers think “but I would never have servants. it would be so weird to have servants” and in order to make the protagonists of the story more relatable they are actually friends with the servants. but flip your perspective and think of it from the side of the servants. wouldn’t it be so awful if your boss was always trying to be friends with you. a really common thing you’ll see is the woke baronet having tea in the kitchen with the servants bc he’s not like other baronets. but what if your boss wanted to hang out and talk during your lunch break every day. not so charming when you think about it that way
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.
I just went looking for the post on X and got a message that it didn't exist
I searched for Guri Singh and got search results showing his account existed, but when I clicked on it I got a message his account did not exist
Does anyone know if he made his account private or if he got nuked by Elon? Or do I just suck at X (because I never go there)?
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