god israel's destruction of tyre is like especially depressing tbh like thats one of the oldest inhabited cities in the world. people have lived there since the bronze age. there is so, so so much history and world heritage there that's being blown up and destroyed forever because its existence doesn't fit israel's nationalist vision of history.
lots to say about israel's politicized and propagandized use of history and archaeology to further their narrative tbh. here's a good article about it.
Weaponizing antiquities is part of Israel's colonial legacy, says Rafi Greenberg, whose colleagues have largely remained silent about Gaza's
I legitimately never thought I could love a white-haired twink the way I love my son Qifrey, his beleaguered wife Olly, and their increasingly large hoard of unruly daughters.
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 will vote for any candidate who promises to go scorched fucking earth on every tech company. Break every single one of them up into companies based around a single product and then split those in thirds. Weaponize existing antitrust laws to the hilt and pass the most draconian versions of them ever seen on this planet. Nationalize google search specifically. Pass consumer privacy protections strict enough to kill the data harvesting industry for good. Make all of these fuckers go bankrupt for this rent-seeking shit
pains me to see classmates who slip into homemaking roles like clockwork when playtime starts. sisters i urge you to realize greater ambitions. you could be a steel worker who earns the respect of your peers through your quiet humility and work ethic. or an embattled party official, drafting the next five year plan in the wake of lower than projected crop yields. you could even be Dinosaurs,
accidentally tabbed into the explore page and the first thing trending on tumblr is good omens... kind of blows my mind how a man being a violent rapist who pretty explicitly used the social capital he gained by being a famous author to abuse women is not enough to turn you off of doing Fandom Stuff with their work. but fictional men being more real to the average tumblr user than real women is not surprising so I guess I don't have any right to act scandalized. it's just depressing man
like there's a conversation to be had for sure about what the appropriate ways are to engage with the work of a still-living creative who has been proven to use the goodwill their fame has bought them to get away with abusing people. because I don't think asking for gaiman or anyone else's entire body of work to be completely removed from any kind of public discussion is reasonable or practical. but I don't know man I am just personally of the belief that Fandomposting identically to the type of fandomposting you were doing before these facts came to light is really not it no matter how much you might talk about hating him in between those fandomposts.
you are too old to be misunderstanding what "death of the author" means this badly. it's not very complicated at all. it refers to the audiences interpretation of a text superseding authorial intent, and has absolutely nothing to do with whether or not you excuse platforming someone who uses that platform to commit abuse. and to defend the "meaning and joy" people get out of publicly celebrating and legitimizing the work of a man who is currently still alive and whose numerous victims are still in a protracted legal battle to receive some semblance of justice for the hideous abuse they were subjected to is vile.
every time the general public is given the chance to stand by abuse victims they prioritize their own personal comfort and desire for entertainment, and there's nothing I can do to stop that but what I can do is tell them all they should be ashamed of themselves. because they should be and I have no qualms saying it no matter how "meaningful" gaimans work has been to them
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