Call me Dav. 30’s. NB. Artist. QL enthusiast. Jack & Joker permanently altered my brain chemistry. Art blog is davastationart - tag is: #dav does art. I do not control the many transient hyper fixations 🤷
you have to be careful reading too many things that are good/smart/well-written bc then you encounter something that isnt and you get confused like ? why didnt they just make this good ? were they stupid
I love that four different people on my feed scheduled this joyous person to reblog by 8am on June 1. I look forward to seeing this a dozen more times today.
[video description: mark kanemura lip-syncing and dancing along carly rae jepsen’s cut to the feeling, the chorus just starting im the video. he is wearing nothing but rainbow briefs, a rainbow flag wrapped around his hips, and a rainbow scarf hanging from his neck, while on his head multiple bob-length, colored wigs are stacked, which he plucks off along to the beat and throws off-screen towards the camera. every wig is colored in the scheme of the pride rainbow; the final bob-length wig he dramatically removes with the scarf, revealing a longer wig underneath thats in a rainbow gradient. pulling out a fan and opening it with a snap, he fans himself, then throws that away too, then grabs the last wig and shakes confetti out of it on top of his head. he throws the wig at the camera, and the video ends /end description]
You dared to risk your life because your man’s going to dump you. How about this? If I help you with this, can we consider my debt paid? If you can prove it with evidence, I might consider forgiving your debt. Then don’t worry. Other people’s business is my area of expertise, Mr. Boiling Tofu. Just don’t be all bark and no bite. I need results too, Mr. Black-Smoke Barking Dog.
A DOG AND A PLANE (2026)
dir. Jojo Tichakorn Phukhaotong
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.
proponents of the sex binary love to say that the Essence of your sex is stored only in one or another part of your body, and this Essence loves to move the moment it's pointed out that that aspect is not always aligned with one's other sex characteristics via being intersex or transsexual
"sex is in your genitals, I mean it's in your chromosomes, uh I mean it's in the size of gamete your body produces, uh I mean it's in the size of gamete your body is intended (???) to produce"
there are plenty of body parts that are regularly attributed to sex, and yet people are terrified of the idea that they may not always come in one of two particular configurations
all the talk about which part your Sex Essence really lies in is just drawing and redrawing linguistic boundaries as a method of erasing intersex and transsexual people. it's pure word games played to deny reality and define those of us who don't fit into the sex binary out of existence
the mythology of the Sex Essence, by the way, is how the patriarchy attempts to keep people bound to their assigned classes so it can categorize and control. you don't have a sex. you have a configuration of body parts and you have a state assigned class loosely based on only one of those body parts
when they say "sex is real" they really mean "Sex Essence is real"
btw when a lot of you say "amab" and "afab" it's obvious you're actually referring to Sex Essence rather than referring to the process by which the state assigns a class
Always remember that the EU did a study in 2013 about the effects of piracy on media publishers and found that there is no correlation between piracy and sales! (And then they tried to hide that study bc that's not the result they wanted)
So piracy is at worst not even a problem, and at best it's free advertisement.
Source: (the link to the actual study is in the article)
In 2013, the European Commission ordered a €360,000 ($430,000) study on how piracy affects sales of music, books, movies and games in the EU
#piracy is always a net good in my eyes#because the fact someone is saving and archiving that shit is what I want the most #also idk sometimes it’s not like a certain thing is even readily available in my country #so hey whoa thank god there’s a piracy site with this obscure show I wanna watch
tangentwithtanner
dailybridgerton
areudirty
theartofmadeline
origamiaround
stormborns
