So many anti-science (and particularly, anti-vax) people apply their diet and orthorexia culture to medicine. That is one of the things you will hear over and over again. “I actually read the ingredients of this vaccine and it has [SUBSTANCE] in it so I will not let my child get vaccinated.” which is exactly how these people grocery shop, I’m sure. No understanding of chemistry, no research into why that ingredient is in there, what it is doing to help make the vaccine safe and effective and what amount would make it toxic to the human body. They will be like “Oh my god, there is hydrochloric acid in there! I’m not going to research into why it is in there (in small amounts as a PH stabilizer), it’s hydrochloric acid (the human body creates it naturally) and they’re trying to inject this into our children!”
i would almost love to meet one of these people and say ah yes i love drinking Dihydrogen Monoxide with heavy amounts of Citric Acid, Glucose and Limonene (this is a very terrible lemonaid) these ingredients are water, the sour of a lemon, sugar, and lemon flavoring
or just asking if they know the chemicals in a lemon.
as a child creepypasta and args had me thinking i'd be finding cheap copies of recent video games at garage sales aaaallllll the time, and frankly i have never once found even a cheap cursed book. like when will it be my turn to find a free laptop in a storage locker with minecraft already installed
see what your looking for is estate sales. Ive gotten a gameboy color and a copy of pokemon yellow there. Cursed art supplies and funky mirrors. there is so much weird cool shit at estate sales even if its not "recent" it can still be modern enough
-Hands of ONLY 3, 4 and Ace are a new hand type [Full Home], worth a large amount of points
-Enables appearance of [Green Sun] Planet card (Increases value of [Full Home] hands)
I’ve started using chewing sticks to brush my teeth and tbh I’m surprised that zero waste people aren’t all over these things.
Chewing sticks are basically sticks from specific trees that you strip some of the bark off, chew to loosen up the inner fibers, and then brush your teeth with said fibers. You cut off the fibers every other day and strip off more bark so the sticks also force you to keep replacing your toothbrush a bit better than normal ones do because you’re literally whittling them down to nothing over time.
No toothpaste required because the sticks naturally have fluoride in them and being, you know, sticks, they’re biodegradable by nature. So no plastic from the toothpaste because there is no toothpaste and no plastic from the stick because it’s a stick.
Some brands sell them wrapped in plastic but you know, not all of them do that. And you can get a reusable container to put them in when you’re not using them.
But because zero waste people are super into finding complicated ways to reduce plastic usage I was surprised I’ve never heard of these things being discussed in the zero waste YouTube channels I’ve watched. They talk about like bamboo toothbrushes and refillable toothpaste and whatnot. Not chewing sticks.
I’m using them because I can just keep one in my pocket in its little container and they’re super stiff and my gums need strengthening according to my hygienist. (Note if you decide to use these things your gums might bleed the first few times because the fibers are so stiff)
I’m not really using them for environmental reasons but they seem like the sort of thing that extreme tree huggers would like. But no, the only people using chewing sticks seem to be me and people trying to make sure they don’t accidentally swallow toothpaste during Ramadan.
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.
As you might be aware I have big feelings about all besnouted creatures, but recently something has been grating on me. Sometimes, when people draw a snout animal, they draw the snout as a nose with the mouth as a separate entity underneath. This is a stylistic choice I greatly dislike because a snout or trunk is, by definition
A COMBINATION OF THE NOSE AND UPPER-LIP.
You can see here that the snout is not a separate entity from the mouth in these mammals, but is a fleshy protuberance emanating from the upper jaw.
You can see in this illustration how a more anatomically accurate snout position gives an anthropomorphic pig a degree of charm that an inaccurate snout position does not.
Snout positioning can make or break a character design for me.
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