Random ravings

Nonbinary Pan Autistic Parent ;)

[exploring-the-spiritual]

One of the things that initially attracted me to the LDS Church was the idea that every person, even after death, will get the chance to hear the gospel and that most people will end up in some degree of glory. That always seemed like the work of a loving God to me.

[thecolorofthegame]

I know this trophy is supposed to represent a triathlon, but it looks like a cyclist award for attacking pedestrians

[animentality]

[flardark]

while the threat of a 10 page paper absolutely had me not finishing my associates degree, there were other reasons I quit. And other people definitely have written them

[sreegs]

[the-quasar-hero]

More game devs need to understand the following concepts:

There is a finite amount of memory on any given device.

No matter how good your game is, players will want memory for other things on their devices.

Not everyone has a stable internet connection so making single player games needing an internet connection is not good.

[nightvale1stward]

Night Vale First Ward

It's pride month, you know what that means!

𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘

𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘

𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘

𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘

𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘

𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘𐐘

[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: 3 days 14 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: 3 days 14 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: 3 days 14 hours

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

🫶

[creationsdecleo]

Autumnal embroidery hoop 🍂 Yes, I know tis' not the season, but I love those colours.

My website My Instagram

[personal-progress-dropout]

To my dear brothers, sisters, siblings, and fellows in Christ,

It’s been a hard couple of months, and probably a hard couple of years. I’m still struggling with reconciling my faith and my identity as a genderfluid lesbian, but here’s the message I feel like I’m getting from my Heavenly Parents. This goes for all of you:

We love you. We want you here. We delight in your identity, because it is beautiful, bright, and a part of you.

Happy Pride, everyone.

[berlynn-wohl]

a marriage of humankind's two greatest design achievements: the illuminated manuscript and the cool S

[sexy-people-contests-2026]

Tumblr Sexyman Contest 2026 Round 1 Part 73

The Onceler (The Lorax)

Bobby (Kpop Demon Hunters)

Remaining time: 1 day 9 hours

Onceler art by @teysifox

[sexy-people-contests-2026]

Tumblr Sexyman Contest 2026 Round 1 Part 75

Cecil Gershwin Palmer (Welcome to Nightvale)

Red Guy (Don’t Hug Me I’m Scared)

Nagito Komaeda (Danganronpa)

Remaining time: 1 day 9 hours

Cecil art is by @littleulvar

[kateammann]

Happy Pride Month!!! 🌈💕🌼✨

[turnnoffyourmind]

Autumn Symphony

Torres del Paine National Park, Patagonia Chilena.

[nihilnovisubsole]

me explaining goncharov to my mother: so tumblr made up this scorsese crime film and they're arguing about its themes and-

my mother, completely deadpan: well are they reviewing the theatrical release or director's cut.

me:

my mother: what does frances ford coppola think of it

[dancinbutterfly]

Your mother is the only one who understands me

[feluka]

i hate the way fat antagonists have their weight moralized and used as a metaphor for greed and corruption and i hate the way it's overcorrected into fat people being "soft squishy friend-shaped cupcakes who look like they give incredible hugs" and i long for the day we have nuanced, interesting, and complicated fat characters and most of all i long for the day people are normal about fatness

[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.

[drawn-on-phone]

My job is to be the Anti-Gen-AI person at a college (and teach the field of Artificial Intelligence). When people ask my why i dont trust Gen AI i just send them this paper.

Managers hum and haw, but everyone gets real quiet when i ask if they read the paper and still want to push Gen AI.