the problem with tumblr spells is all of you lil sluts are casting and not giving it time to charge so you're all casting cantrips and not 9th level death spells. everyone wants to be the one to take them down no one wants to be the one who assists
This morning I showed up to work and my boss was like "congratulations :))" and I was kinda confused so asked her on what and she said "your name, I heard about it on the radio"
So far I have counted two online articles and apparently the radio is also talking about my name change so I'll keep you guys updated when I'm on national tv or something
Look, this is my litmus test: I pretend I am the original Earl of Sandwich. I have asked for non-bread foods to be brought to me inside bread, that I might more easily consume them one-handed while gambling.
This does not enable my wretched regency habits. This is not what I asked for. I do not deign to grace it with the name of my house.
Fandom is weird now. It’s becoming less trusting and friendly, and the Orwellian surveillance issues that are currently running rampant in the USA and UK have bled over into fandom spaces.
I’d read the articles that young people have no privacy and have become so accustomed to it that they enact on themselves. Abstractly I knew this and had accepted it, but it’s only now that I’m seeing it become a major issues.
Obviously part of the problem is I need to become a trusted member of my new fandom. But fandom in general is becoming far more hostile to privacy than even just 5 years ago.
I’ve had to leave 3 servers that want to do age verification. And this is new. Not even 5 years ago when I started zines ppl just turning 18-20 were far more receptive to the ideas espoused by us older folks about not being surveilled. But now it almost feels like they *want* to be surveilled.
They’ve become so used to it from tiktok and their government that they crave it. I’d write a dystopia about it but unfortunately what even is there to say that hasn’t already been said?
If you’re 18-22 now and you’re reading this and willing to listen without immediately calling me suspicious: this is not normal. Age verification will not keep you safe and will not keep minors safe either. It will only lead to the normalization of further surveillance and erosion of your privacy.
I know it can be an unwieldy text especially if you’re not used to reading academic sources, but go read The Panopticon. Watch V for Vendetta or read the comic, read 1984. Watch The Lives of Others and understand the ways this modern era is not so different from the Stasi in the film.
Please.
I don’t want to see a rerun of the surveillance state anymore.
So, I’ve been pulled over a few times in my life. Not many, but a few. And I’ve also been in a couple of cars that got pulled over. And let me tell you, if you were actually doing something wrong, the officer doesn’t make any small talk, just straight into “I clocked you doing 70 in a 55.” The only time I’ve ever gotten the “do you know why I pulled you over?” was the time when I wasn’t doing anything wrong, and I got let go even though he insisted to the end that I was doing 87 in a 70 (white privilege at work).
“Do you know why I pulled you over?” is a trap. It means there’s a good chance the officer doesn’t actually have a good reason to ticket you, and is trying to get you to waive your 5th Amendment rights and incriminate yourself. If you make a guess, that’s a confession of guilt.
But there’s another trap, that I’ve heard of but haven’t yet experienced. It’s “do you know how fast you were going?” With that one, they’re hoping you’ll say no, because then they can name whatever speed they want – you just said you didn’t know how fast you were going, if you deny the speed they name then you’re lying to them.
Oh, I’ve had that one. Go with “yes.” Don’t give them a number, just say “Yes.” Then they still have to offer a number and you can deny it without contradicting yourself. They could just ask you, at that point, but that’s suspiciously similar to saying they don’t know, and they tend to avoid doing that.
Also, you can always go to court and contest a ticket, and a lot of times you’ll win. Or if the cop thinks you’ll win they won’t even show up and you’ll win by default.
They like to target out of state plates because anyone who would be majorly inconvenienced by a court date two months away is a lot more likely to just pay it.
I’m seeing a lot of people saying this post changed their brain chemistry, and as a neuroscientist I wanted to say yes!!! Yes it does!
Wanting something requires dopamine signaling, but liking something doesn’t.
If you have a mental illness/disorder that affects dopamine, you might feel that you don’t want to do the things that you like. You do still like them. You will appreciate having done them.
Let your likes guide you.
(If you want to read more, here’s one experimental paper about it. https://pmc.ncbi.nlm.nih.gov/articles/PMC5171207/ This theory called the incentive-sensitization theory was originally created to explain behaviors in addiction but can be applied elsewhere as well)
Rewards are both ‘liked’ and ‘wanted’, and those two words seem almost interchangeable. However, the brain circuitry that mediates the psych
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.
areudirty
ellievsbear
yousharknotpass
