"going out to get milk" is a common turn of phrase used to describe a man abandoning his family.
the "milkman" is a common figure in stories depicting a woman's infidelity and adulterous affair.
this implies that the ability to provide milk would both decrease the likelihood of a man abandoning his wife and children, as it would eliminate the need for leaving to get milk AND would secure that man's marriage, as his wife would have no need to seek milk from an extraneous source.
therefore, all men should produce milk, through various means such as:
- being a cow
- being an almond
- being a woman
- being a coconut
- being in the omegaverse
- being an oat
(list is exemplary and not finite)
in this essay, i will redefine the nuclear family and explain the seductive and inflammatory nature of the 1993 "Got Milk?" commercials.
There is literally no circumstance where I support age verification to access a website. As I've said before I'm very much the "there's nuance here" person on almost everything but on this issue there's no nuance for me, it's awful and horrible in and of itself and it also sets an awful and horrible precedent
The best part of that video is that the owner found the ORIGINAL plush later on the beach and took another video with it after their grandmother stitched it back up
You are an unreliable narrator because your coping mechanisms for your deep-seated trauma forbid you from acknowledging the reality of the situation. I am an unreliable narrator because I sincerely have no idea what the fuck is going on.
i love that star wars comic where padmé's surviving handmaidens hold a last stand against vader but honestly if i were them i would have dedicated the rest of my life to the most comprehensive and inescapable fake haunting in the history of the galaxy. man should not be allowed two successive heartbeats without seeing his dead wife's face gazing soulfully at him across the room and slipping away before anyone sees. literally what else is the point of being a highly trained operative capable of perfectly imitating your best friend if not to torment her husband for decades after she's gone.
a convo in the replies of a post where one of them is hidden because i blocked them and the other one makes a comment that i cant possibly understand due to how out of context it is is funny to me every single time
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.
A doll youtuber I watch has made a video about the history of Polly Pocket, and she's describing the plots of the dvd specials, one of which features an elderly woman named Ms. Throckmorton, and my reaction was
Doing a final project in my stats class, we have to pick a subject and collect data on it. We need at least 100 data points, and I figured this blog is big enough that a poll on here could get to that pretty easily!
Doing my project on if it’s more likely to be born in certain months :]
I have gotten the OK from my teacher to collect data using a Tumblr poll, btw. I’m also going to have to send her this post as proof of where I got the data from / proof I didn’t just make up the numbers. So. Behave
