It's kind of amazing how much this is the identical strategy. Focusing on the distress and harm of parents over the trans or autistic person themselves. Pathologizing the condition treating it as an epidemic, including calling it a social contagion causing a worrying "explosion" of diagnosis because it is "trendy."
Ignoring the experts on the subject while also appealing to "common sense" and dismissing all research that contradicts them while also appealing to "basic science."
Plus it's all the same quacks and actors behind previous medical and social moral panics saying them same things in the same ways and no one seems to acknowledge that.
she says in the article that she was inspired to think this way about autism after she started reporting on detransitioners. Her Twitter mentions are filled with prominent voices in the detrans terf movement. This isn't just similar, It is quite self-consciously an extension of the same movement.
Jonathan Joss was an Indigenous, gay man who was murdered on the first day of Pride month as well as Indigenous History Month. He died protecting his trans husband. Homophobia and racism aren’t marks of the past, and this is a heart breaking reminder of that.
Praying for a safe journey back to the spirit world, Uncle ❤️🩹🦅
Today is the anniversary of the death of Jonathan Joss (King of the Hill, Parks and Rec). Jonathan Joss was an Indigenous, gay man who died protecting his transgender husband, on the first day of Pride month. Today we remember him and how he protected his family.
RIP Marjane Satrapi, author of the amazing graphic novels Persepolis about living during the fundamentalist revolution in Iran in the 70’s and 80’s. She also created the animated movie based on the graphic novels, which is where these gifs come from.
Reblogging in honor of Marjane Satrapi, one of THE great graphic novelists. Her comic Persepolis was a crucial text for shaping my belief that comics can deeply explore identity, culture, politics, and history.
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.
Scientists invented a fake disease. AI told people it was real: Nature.com
I'm a bit frightened for the time when someone less ethical than the person that did this decides to repeat the experiment but leave out the part where they come in later and announce that it was fake and people wind up diagnosed with the fake condition and all kinds of wacky hi jinks ensues.
"exercise will give you more energy" gets said a lot as a common piece of health advice but I think it needs to be expanded into "exercising will make you tired while you do it, and you will continue to be tired immediately afterwards, sometimes even the next day too, but over months of consistent exercise, your muscles will get stronger and therefore get less tired out by everyday activities, making you feel like day-to-day life takes less physical energy than it used to"
reminder to visit museums, even if you feel out of place. you feel out of place because there is an established concept of inaccessibility of "high culture" to the masses, purposefully developed to distinguish between social classes.
take up space, read the plaques, get the audioguides. you are just as entitled and right in being there. visit museums, boycott museums, be expressive about your opinions about museums.
a lot of museums are free, or discounted for youth and students. take advantage of that. check your local art museum. check your local history museum. museums are there for you, they are there to educate the public, not to distinguish between class. it isn't a private collection, it's a public exhibit.
baratheonpilled
titsay
