Really enjoy Madelaine showing up for 1 Thanksgiving and on a 9 she has for several months distracted the entire Whittaker family and got them infighting.
[ID: A drawing of Taako and Lup, skinny twin elves with light brown skin and freckles. Lup, on the left, has long, slightly wavy, dark brown hair in a ponytail. She wears a collared shirt striped with teal, pink, white, and yellow, with the sleeves rolled up and the front opened slightly to show her black bra underneath. She also wears blue shorts, black fingerless gloves, and black boots. Her hands are on her hips as she looks downward slightly and smirks. On the right, Taako has wavy blond hair that partly hangs loose, with a pair of long, small braids done into it. He wears a green, short-sleeved collared shirt with a pattern of cactuses on it, a blue flannel tied around his waist, black pants, and red shoes. He also has on a tall green wizard hat with an extremely wide brim, which he lifts out of the way slightly with one hand as he cocks his eyebrow at something. End ID.]
you know even if a homeless person or a starving person is in that position because of their own "bad decisions" i don't care. it doesn't matter. no supposed financial misstep is enough to condemn someone to homelessness or poverty.
Search and rescue teams do not ask if a hiker was properly equipped and prepared before they go out to look for them.
EMTs do not ask if a driver checked their mirrors before they take them to the hospital.
Lifeguards do not ask if a swimmer made a mistake by going into a riptide before they dive in after them.
Judgement doesn't help anyone, including you, the person doing the judging. Just help people. Just shut the fuck up and help people.
“but shrouded black figures are scary!” not when ur muslim. its the funniest fucking thing. this is labeled on pinterest under shit like “classic horror” “scary phone wallpaper”
but that LITERALLY just looks like a niqabi or someone in a jilbab. Like Look at this pic of me (from a self photoshoot, now w/o the dramatic lighting and dark background)
or this pic of me
or this pic of me
like its so funny i can’t be scared of shrouded figures it just looks like me.
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.
Just a quick grab for some of what's being discussed:
"4. UNFATHOMABLE TRAINING DATA
The size of data available on the web has enabled deep learning
models to achieve high accuracy on specific benchmarks in NLP
and computer vision applications. However, in both application
areas, the training data has been shown to have problematic characteristics [18, 38, 42, 47, 61] resulting in models that encode stereotypical and derogatory associations along gender, race, ethnicity, and disability status [11, 12, 69, 69, 132, 132, 157]. In this section, we discuss how large, uncurated, Internet-based datasets encode the dominant/hegemonic view, which further harms people at the margins, and recommend significant resource allocation towards dataset curation and documentation practices."
"6. STOCHASTIC PARROTS
In this section, we explore the ways in which the factors laid out in
§4 and §5 — the tendency of training data ingested from the Internet to encode hegemonic worldviews, the tendency of LMs to amplify biases and other issues in the training data, and the tendency of researchers and other people to mistake LM-driven performance gains for actual natural language understanding — present real-world risks of harm, as these technologies are deployed. After exploring some reasons why humans mistake LM output for meaningful text, we turn to the risks and harms from deploying such a model at scale. We find that the mix of human biases and seemingly coherent language heightens the potential for automation bias, deliberate misuse, and amplification of a hegemonic worldview."
ID: A youtube comment with 11 likes by Niceone, it says "I've lived 46 years without knowing this. How nice of life to save some of the best bites for later." End ID.
Normally, people tend to get frustrated, even jokingly, if they miss out on something. This comment was on a song from 1974 and it made me smile quite much. Simply appreciative. Like a dessert after dinner.
It is genuinely mind blowing to me just how many Tumblr posts have changed my life for the better and taught me to be happier. Not all of the thoughts originate on Tumblr, but the way people collect and frame them has literally changed my brain chemistry.
Sometimes Lou rolls really well and convinces the Baba Yaga to help the party. Sometimes he rolls well and convinces a college student to start an Instagram for his dog.
Remember when joining fandom as a younger person meant lurking for a bit and figuring out the vibe and etiquette instead of coming in on day one and calling people weirdos for liking weirdo shit in the weirdo factory.
that man has been trying to climb this tower since he was 16. he has asked multiple times, and every time they said no, but now he’s famous enough & variety was able to convince them to do a shoot on the tower. it all led here. it was all for this.
i love seeing best friends goofing around in public. i love seeing couples laughing together. i love seeing little kids walking hand in hand with their parents. i love seeing dogs running happily back to their owners. i love seeing closeness and positive relationships, and knowing all that love is out there
in this house we have endless respect for cosplayers from the days before VCRs.
You couldn’t just rewatch the episode to look at all the details of the costume. You got lucky with press photos showing up in magazines or you just watched the episode/movie while sketching furiously
thinking about that one woman who made a Star Wars flight suit in 1977 entirely from trading cards and sketching details in the theater. or stories I’ve heard about old school Trek cosplayers getting the bizarre seam placements right by photocopying magazines onto overhead transparencies and projecting them onto butcher paper.
I’m a semi old school cosplayer (started in 2001) so some of the old school techniques are still things I learned on (I’ve sketched from stuttering VHS tapes on pause and used the overhead transparency trick)… what we have access to now for costume recreation blows what they had out of the water just in terms of reference material, let alone specialty costume supplies like thermoplastics and cosplay wigs.
That Star Wars fan who made the flight suit from trading cards and movie sketches is TJ Burnside and she is still with us. In fact, I am adding to her Fanlore page with info about the flight suit (and how it went viral on Twitter and Redditt a few years back). Fanlore.org, is a fan run fandom history wiki. Stay tuned.
Her (sadly) barebones Fanlore page is here: https://fanlore.org/wiki/T.J._Burnside
Hey tumblr friends, in case I haven't told you lately, I have no idea what the FUCK half of you are on about and I WISH I didn't know what the rest of you are on about. Great work. Keep it up.
pixelskylines
turnnoffyourmind
dededos
kianamaiart
