Antea21

Rebloging, and sometimes even posting!! ( but usually on side blogs @janettinniter and @bonnyjanett)

[antea21]

Hiiiii!!! :DDD

My name is Antea21, Ann for short!

She/her, tired student, and true UwU believer

Mostly reblogs, sometimes my own posts when inspiration strikes and I’m very welcome of any messages and questions about my postcard collection :>!

credit for the blinkies to pawpr1nc3, amy and anakin from here

[lipstickchainsaw]

When you're unable to solve an IT problem at work, there really is nothing quite like having it escalated all the way up the ladder. With every step, there is a degree of smugness about how real my problem is, and that yes, I was right to have trouble with this.

[crowns-of-violets-and-roses]

You can get a minor version of this if one IT person solves it but they spend a bunch of time repeating things you’ve already tried and when they eventually solve it it’s by doing something you wanted to try but didn’t have the requisite permissions to do

[coral-skeleton]

Was in a situation where neither I, nor my boss knew what was causing the problem, so we ended up calling one of the head engineers, and ive never experienced anything quite as validating as the moment where said head engineer, after spending several minutes just staring at the problem, quietly said "what the fuck"

[odyver]

alright I've got to do some quick math to explain attitudes towards AI to my boss.

we're looking to create an AI policy, and when we were talking about this, my boss (older millennial) was genuinely shocked to hear that younger people do not (seem) to view AI positively (a la the recent commencement speakers being booed)

please rb for larger sample size!

Question 1/3

What is your age, and do you feel AI is a net positive or net negative in our lives today?

under 18, AI is a net positive

under 18, AI is a net negative

18-29, AI is a net positive

18-29, AI is a net negative

30-45, AI is a net positive

30-45, AI is a net negative

46-60, AI is a net positive

46-60, AI is a net negative

over 60, AI is a net postive

over 60, AI is a net negative

Remaining time: 2 days 8 hours

[odyver]

Question 2/3

How often do you visit or interact with museums/archives (whether in person or online)?

Frequently (multiple times per month)

Often (multiple times per year)

Occasionally (a couple times per year)

Rarely (once every couple of years)

Never :(

Remaining time: 2 days 8 hours

[odyver]

Question 3/3

If you saw a museum was using AI in exhibits, marketing, research, etc., would you be more or less inclined to visit that museum?

under 18, more inclined

under 18, less inclined

18-29, more inclined

18-29, less inclined

30-45, more inclined

30-45, less inclined

46-60, more inclined

46-60, less inclined

over 60, more inclined

over 60, less inclined

Remaining time: 2 days 8 hours

Thank you for helping with this data collection. Please rb for as big a sample as possible!

🫶

[mikkeneko]

PSA to fan creators who don't have a lot of regular contact with children: They are almost always bigger than you think. A 1-year-old baby may already be walking. A toddler is likely already hip-high. A 10-year-old may already be taller than at least one of their parents. A 14/15 year old may already have reached their adult height.

[amarguerite]

Via @watertightvines

[wildjuniperjones]

Here's the link. It was actually not immediately easy to find, so I thought this might help.

[vintage-ukraine]

Green Lamp by Yakiv Kalynychenko, 1890

[fleetvale]

oh yeah i forgot to post these

i took some liberty with the colors but it’S DONE hooray

[jeaniefranklins]

Sex scene as character study is so good. What is your relationship to your body? What is your relationship to your partner? What lessons have you absorbed from the culture about yourself as a sexual being? How much do you have to trust someone before being comfortable with intimacy? What fears and insecurities come to the fore for you when you take your clothes off? It's so good.

[cartoon-fanaticism]

GLaDOS voice: "Would you like to see some artwork I generated? I've heard from other test subjects that AI-generated artwork produces an uncanny valley response in human viewers because they can't perceive it as fully real. They've told me that it looks absolutely hideous to them, that they can't imagine anything more disgusting than AI art. But, well I've been practicing and wanted your honest opinion. Feel free to let me know how ugly you find this by ranking it on a scale from 'vomit-inducing' to 'eye-bleeding'." A robotic arm lowers from the ceiling holding a hand mirror up to Chell's face

[wassuppeas]

[wheeloffortune-design]

CONTROVERSIAL OPINION ABOUT BISEXUALITY

that purple in the middle is not the right saturation, it doesn't fit with the other two colors and it drives me crazy.

[deus-ex-aquila]

all right, I think I got this, I've got dual citizenship and I have another flag we can borrow from:

step 1

step 2

step 3

[varangianviper]

[wheeloffortune-design]

This is true bi/ace solidarity.

[xthehatchick]

holy shit

[finnslay]

This is the only correct way

[transarsonist]

[Patchnotes]

swapped purple in bisexual and asexual flags for better saturation matching and color theory

[maimonideznuts]

There was this TV show in Australia on Channel 9 in the 00's called Parallax, it was about kids jumping between alternate worlds through gateways in seemingly ordinary places... Basically all of the sets were actually just Places around Kings Park (an enormous public park in Perth, Western Australia), East Perth, and popular beaches. It was actually a pretty good show, except for the part where I knew kids who acted in it and the monsters were made by my art teacher.

[sutured-infection]

Cellulose nitrate was used to make dice from the late 1860s until the middle of the twentieth century, and the material remains stable for decades. Then, in a flash, they can dramatically decompose. Nitric acid is released in a process called outgassing. The dice cleave, crumble, and then implode.

From Dice: Deception, Fate & Rotten Luck by Ricky Jay and Rosamond Purcell, 2002.

[jesus-lizard-journal]

hello beautiful

[probablybadrpgideas]

mouldy dice monday?

[wingdingle]

[c-130jsuperhercules]

[wizzardhat]

[cloverscry]

time of destiny

[babushkaat]

[babushkaat]

[lightanddarkkh]

Then perish

[marlinspirkhall]

Her greed sickens me

[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.

[irispurpurea]

This is the paper. It's excellent, highly recommend reading it.

I remember reading about Gebru's firing but I had no idea this was the paper she was fired over.

[lovesick-joey]

worldbuilding: the threequel

one | two

[mizeffect]

secret life scar ! :-J