Formerly Lurker Bees!

Hi! I'm a hobbyist artist and author who enjoys video games. PFP by @tsurudraws

[itsmetovri]

Hello!

Hi, I'm Tovri. Some folks also call me Nel or Lurker Bees, or a handful of other names. I'm a hobbyist artist and author who enjoys video games and the occasional anime.

I'm currently also able to be found at the following:

Carrd: https://itsmetovri.carrd.co/

BlueSky: https://bsky.app/profile/itsmetovri.bsky.social

I'll probably post some of my drawings and doodles, maybe some writing snippets, and pictures of my pets.

[jackzarts]

Here are the Greater Demon rewards I did for my supporters of May 😈

🌱 Verin for @discordedmuffins 🌱 Araeno for @opalkire 🌱 Astra for @ashentia 🌱 Reka for @itsmetovri 🌱 Kaleb for fligan 🌱 Halos for @abstractkryptid 🌱 Gloria for @erikaisfine 🌱

Patreon - Ko-fi - VGen

[jackzarts]

🌱 April Patreon rewards for my Greater Demons 🌱

🌱Cidere for @ashentia 🌱 Tiānruì for @itsmetovri 🌱 Nifend for VariansVault 🌱 Ren for fligan 🌱 Voices Whisper for @opalkire 🌱 Veronica Law for erikaisfine 🌱 Elias for discordedmuffins 🌱 Alaric for @abstractkryptid 🌱

Patreon - Ko-fi - VGen

[kateammann]

Happy Pride Month to all of my fellow aces!! 🖤🩶🤍💜

[bumblesteak]

A necromancer AND a gardener (5 year redesign) 🌸

[justalurkr]

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.

[depsidase]

[mundanesalad]

it came to me in a vision

[bug-lovers-island]

Been thinking about hot rod horseshoe crab all day

[raspberrybluejeans]

does anyone know how to draw. has anyone ever drawn anything ever.

[nolaplantdaddy]

I love when people ask "how did you learn this skill?" I just started, there's no secret. that's it. a vast majority of the time the only thing holding you back is your trepidation to start.

[noegrets]

[museiums]

artist: felicia chiao

[oliviawhen]

Job hunting in a hostile environment. ✨

[jackzarts]

Here are some fun Silly Goobers I did back then 😈

🌱 The first one was for @billvolkgames 🌱 Then Muse for @itsmetovri 🌱

Commission me - Patreon - Tip me

[talos-stims]

trying to groom her brother | source

[sky665]

[brawltogethernow]

Look what my friend with no social media posted to the chat with the damning staggered timestamps of 1:11, 1:37, and 1:42 A.M.

[depsidase]

[beemovieerotica]

if you work in a creative field...or if you do creative hobbies like writing or drawing...you need to make friends with people who don't do those things. you need to befriend normie Steve who has never written a story in his life. and this is because when you are in a creative job or hobby and spend all your time doing that thing, surrounded by very capable people, who you inevitably compare your own progress and skills to, you forget what the baseline human skill at that thing is. and it's usually zero. normie Steve has not written a story since the 3rd grade when his teacher made him do it. he's very good at other things that are not storytelling - but if you tell normie Steve that you wrote a full 300-page book from start to finish, he will think you're some kind of savant. he does not know ANYONE else who has done this. you need this perspective. because when you're constantly on Let's Write Stories dot Com then everyone on Let's Write Stories dot Com will inevitably be like "oh of course everyone on earth has written a book or several at this point!" and you canNOT let yourself think that. that is not even close to the average human experience. you are in a bubble. do not put yourself down. do not give up.