Welcome to my tumblr! This is my fandom blog, which is mostly video games, horror, and especially Malevolent now (the podcast). Check the links below for my fics on AO3, and the original works I'm authoring on Tapas!
The thing about the Witch Hat Atelier world is that the magic system sounds great until you realize it's essentially programming and that sobers you up immediately. Sure, maybe you reach the level of skill needed to draw perfect curves, straight lines, and circles without looking down, but after that you still need to be able to logic out which combination of sigils will actually give you the desired effect, and if you misplace a single line it all goes to shit. Add to that the inclusion of effect-altering inks and you start to understand why Olruggio is Like That.
STAY SAFE!! [ID: the Gilbert Baker pride flag with the words “Happy pride to all those who are unable to celebrate openly and safely. You are loved and seen!” in all-caps black text over it. /end ID]
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.
random but here is a recipe for cold peanut noodles that you can make during hot weather because i just ate this and had a fantastic time
2tbsp of peanut butter. a splash of rice vinegar, soy sauce, sesame oil, maple syrup. some chili flakes, some sesame seeds. a splash of water to thin it out. now you put in your noodles (cooled!!!! boiled and rinsed so they’re cold!!) and then some chopped up cucumber or carrot or avocado or cabbage or any crunchy vegetable. i just used cucumber
you can also put in lime juice or herbs or sriracha or grated garlic/ginger or anything like that; tofu/tempe/meat for more protein etc. noodle wise this can be ramen soba udon whatever, i used soba. enjoy homies
I've been listening to Agent Stoker (paranormal thriller AD), I'm about halfway through now. And I've been enjoying it, it's very hammy and weird and fast paced. It has not so much as heard of "subtlety" as a concept, but it kinda works in its favor for most of it. The cast is fairly diverse, the acting is pretty great despite some of the ridiculous things they're expected to say. We have canon lesbians which is always a plus. Stoker himself is your standard "haunted by the pain of my past but I cope with humor and alcohol" protagonist, but he's very much grown on me. He has a particular voice that would be fun to reproduce in fic.
But the best and the worst part of the show is the recurring.... anti-villain? He's by far the most interesting character but also a really great example of why writers should do better research when it comes to portraying certain life experiences they don't share. In this case, mental illness. (some spoilery thoughts)
One of the most present characters in the show outside Stoker himself is EnGAGE, a rogue AI who is self aware and occasionally homicidal. He (he uses he/him) was created as GAGE, named after Phineas Gage, as part of an experiment to reproduce symptoms of schizophrenia in an AI model which could then be used to test treatments and aid in diagnosis. This included convincing the AI it was Charles Manson as part of its training.
Yeah, this was as terrible an idea as it sounds. GAGE almost immediately became aware (possibly through supernatural means - the intersection of the paranormal with technology is one of the coolest parts of the show) renamed itself EnGAGE and became determined to kill it's creators for making him the way he is.
This is all in ep 1. EnGAGE then splinters his personality in two every once in a while, creating spin of iterations that each have their own motives and slight differences in their personality. One is still deeply homicidal while another latches onto Stoker and tries to befriend him.
And they're really funny, ngl. The actor (Ato Essandoh) is clearly having a blast and every episode one shows up is immediately more exciting. But you can already see the issue.
Now I do not have schizophrenia, so I can only speculate how someone who does would feel about this take on the concept. Even so, the execution is really uncomfortable at times. EnGAGE doesn't actually display classic symptoms of schizophrenia in terms of hallucinations, paranoia, or apathy. I know it can manifest in different ways, but in EnGAGE's case, it seems to come down to just his willingness to kill, and occasionally clinging not to a genuine delusion, but wishful thinking and stubbornness. Initially Stoker is somewhat sympathetic, but that doesn't stop him from trying to trigger EnGAGE into some kind of psychotic break every chance he gets. Two seasons in, it just starts to feel cruel when Stoker pesters him. He cant destroy EnGAGE when he's able to keep making copies of himself infinitely, but making deliberate jabs about what's essentially his programmed in "mental illness" feels tone deaf for the show. I don't consider it any less ableist when the character is a scifi self aware AI rather than a human.
I get that they were maybe trying to put a more creative spin on your typical evil AI (EnGAGE literally rejects the term "malevolent ai" which got a chuckle out of me). In the beginning he even seduces and tricks people into committing murders for him, like Manson. But it's not really a thoughtful reflection on what mental illness would look like in a machine, his diagnosis is short hand for "murderous". And that really sucks.
Especially because he's so clever and funny!!!
He reminds me of Yellow a little. Yeah he's a bad guy who kills people, but stop being mean to him in that specific way, he's babygirl!!
Anyway. Those are my Agent Stoker thoughts. I've already listened to half so I'm gonna catch all the way up. Recent plot makes me hopeful that he gets a better rap moving forward.
I was gonna say "the one difference is there's no Bella, lol" and then I remembered it was the Manager that offered Faroe AND Bella. Kayne knew from the start that as far as Arthur's concerned, Bella wasn't a carrot, but a stick.
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