Put in the tags the completely finished (whether cancelled or wrapped up on its own terms) TV series that has YOUR perfect ending, however you define that
Please don’t include huge spoilers for the specifics of the endings, and it would also make me happy if people don’t use this to talk about the shows whose endings they hated
“Because the truth is, tech doesn’t have an image problem. It doesn’t have a message problem. It has an intention problem. What’s wrong with the axe murderer who broke into my house is not that he hasn’t successfully persuaded me to buy into his narrative. What’s wrong is that he’s trying to kill me with an axe. Similarly, when you launch a product that’s designed to put millions of people out of work, block access to sources of verifiable truth, replace human creativity with slop, and lower the barriers to every sort of atrocity, the problem isn’t that you haven’t told the public a good story about those things. The problem is that you are trying to do them.”
there are all of these completely bizarre and basically unprecedented ills in society now, including irrational economic pessimism, which are having obvious negative effects on society (like electing trump over and over), and which are located entirely inside the human mind - like there's no reasonable explanation other than "many people have really disordered thinking, they have warped views of themselves and society" and all of these ills appeared in like, the last 15 years, and accelerated during covid, and yet so many people refuse to believe it has anything to do with social media and constant access to social media via the phone. it's actually really funny. like far and away the most obvious and logical conclusion to draw and people just, i guess, do not want it to be true
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.
everyone acting like caleb shomo cheated or annihilated a family or should be sued for ruining and wasting a woman's life for [checks notes] coming out as gay after a long term heterosexual marriage. is making my eyes bleed
holy shit caleb shomo..... i used to scream along to their lyrics as a teenager... feeling weird about my queerness and so fucking depressed singing "i dont wanna be sick and disgusting". oh my god i love him so much. im so fucking emotional about this.
i remember seeing BMTH in 2015 with beartooth as support, didn't really know them. i remember feeling intimidated watching them all walk on stage (so so masc lol) and then the music started...to this day still one of the best live performances I have ever seen. the crowd felt as one, so energised and united.
i cant wait to see them live again later this year!!!
holy shit caleb shomo..... i used to scream along to their lyrics as a teenager... feeling weird about my queerness and so fucking depressed singing "i dont wanna be sick and disgusting". oh my god i love him so much. im so fucking emotional about this.
every time i have to relearn that being disabled means not being able to do all of the stuff other people can i get so pissed. you mean its just like this forever? i want to do stuff...
tl;dr: all "algorithmically" pushed stuff on a newsfeed is mostly ads. nothing that's really surprising form this vulture article, but it is dismal and makes me grateful for one website where you only see things from people you follow WITHOUT horrible short-form video content
dude it's just politics, it doesn't matter. all it does is shape every single aspect of the society you live in from the second you are born until forever
using violence to liberate people from sweatshops, unsafe mines, and grinding poverty isn't the same as using violence to impose those things on people. the idea that violence is morally repugnant regardless of context is a belief that every oppressor throughout history would love for the oppressed to hold
origamiaround
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kaledo-art
dailybridgerton
stormborns
turnnoffyourmind
discoholicmusic
yousharknotpass
baratheonpilled
