We are all walking contradictions.

But not really. (And I don't mean being a hypocrite.) Allo! :> I'm into anime and other kinds of funny shit. Mostly funny shit. I tag practically every single post/reblog with the intention to organise all the shit here, and I do sometimes rant a litte in the tags. :-P I recently got into KaiSoo/KaDi (Exo) and they'rE REAL BINCHES, I'm at the point where they've consumed my entire life so catch me screaming about em half the time. Of course, the other Real Couples such as MakoHaru & InuKag are my shit too. DISCLAIMER: this is not a Studio Ghibli blog, as opposed to my appearance thing, but I sometimes reblog that too (check /tagged/Studio-Ghibli , if you will :>). I actually attempted to cram life and death into my icon so the big background header has to stick with the theme lmao

[tumblerculosis]

I’ve been obsessed with this tiktok for days now

[coffeedcait]

Person: "Hello sir." he pets the frog with one finger.

Person: "Have a good day. Actually wait a minute. You missed a bug, but I got you home boy. Hold on." he picks a bug up and throws it next to the frog.

Frog: turns around and eats the bug.

Person: "OHHHHH-" sings high pitched and angelic like he witnessed a miracle.

[swan2swan]

Where's that tweet about how American chants are "let's go [team name] and some other country (Irish?) fans are "I've made up a song about the other team's drinking problem to the tune of London Bridge Is Falling Down one two three"?

[luimnigh]

[ladydevoir]

[bareniabirkin]

😟😳😳😳

[squadron-of-damned]

#idk what this means or if i do this but ig i'll just hold my phone with my pinky stuck out from now on?? 

Good question, also no that won’t help.

shitty MS Paint 3 minutes doodle, nto entirely accurate: When you have your pinky hooked on the “bottom” edge of the phone for the extra security so it doesn’t slide out of your hand that easily, you’re wreaking damage on your hand, since the pinky is extremely askew from it’s resting position. You might have noticed that when you hold your phone like that for long time it begins to hurt, like when you are gripping a pen too tightly for example.

Green lines - the fingers are going their natural way. Red line - the pinky is way off, that’s bad.

[theclockworkjudas]

Me: Oh, good thing I never-

Me, looking down at hand: By talos this can't be happening

[h4nds4ndwich]

oh thats why my hadns have started to always be in pain ok

[eebie]

Youre the only daniel i ever gave a damn about

[euforri]

Meeting in Akershus Fortress (1412)

[aphel1on]

never stop being obnoxious about fictional character online. you will find like-minded people and it will literally save you

[wolvesintheflowers-deactivated2]

I laughed to hard at this fucking thing.

[nattousan]

[talldren]

[wolfythewitch]

Dog years

[myblogwhereipostthings1]

I recently watched Brokeback Mountain and I kind of want to do a bit-by-bit analysis of it. Jake Gyllenhaal’s acting is so good. I could write paragraphs about all of his facial expressions throughout the film. He’s such a good actor, especially during moments when he says, “I’m not queer” and in his eyes he’s lying, and he’s such a meme in real life. Where’s that one picture of him looking completely unhinged or watching a girl eat salad obsessively?

In brief summary, something about Jack is so full of life, it was really enjoyable to watch like the way that he brags about how “no filly can throw him” and gets thrown off anyway and his bad harmonica playing and jumping around taking the piss out of himself as a “rodeo boy” and him being physically affectionate and lassoing Ennis and loving to just talk and talk and hating authority. He is so full of life.

Overall, there were just some really good moments in the film. You really have a feeling for the tragedy of his character and how he feels unloved by his wife and his hopeful romanticism and the way that he talks about starting a “new life” literally all of the time with the people that he loves and generally just the way that he kisses - he puts absolutely everything into kissing and comforting Ennis when his nose was bleeding. He’s such a fundamentally romantic person.

Anyway, I could write a post about all of my favourite scenes, but I don’t know if I should. He’s really pretty too. He has such a pretty nose and eyelids and lips and hair. I might have a type.

[morningdew-and-forgetmenots]

I finally watched Brokeback Mountain and holy shit. Not only is this a beautiful, heartbreaking movie, it is also so smart with its imagery.

For example: Ennis mentions sin and being free from sin in a conversation with Jack. Shortly after, he sleeps with him. And what does he find when he returns to the sheep? One has been killed. Not only does this dead sheep introduce death immediately after their first sexual encounter (foreshadowing Jack dying as a consequence of homophobic violence, so as a reaction to the specific type of sex that they shared when/before the sheep was killed), it also reminded me of religious symbolism. Sheep as a symbol pop up often in the Bible. For one, as sacrificial animals (Jack and Ennis' relationship comes with sacrifice and will not be easy, which is why Ennis decides he does not want to risk living with him,) but also as a symbol for God`s people/humans: Seeing this sheep, murdered, meant seeing his own demise staring back at him.

Another thing that I loved: How Alma Jr. parallels her mother. There is a reason she is the daughter who gets more screentime. She is very fixated on her father and keeps chasing a closer connection to him. And in that last scene, when she reveals that she will marry, she must be roughly the same age that Alma Sr. was when she married Ennis. That is why (at least for me) Ennis asks whether her fiancé loves her: he was reminded of his younger self, who could not love Alma the way she deserved. And then the very last shot with the shirt and the fucking postcard? No words. Genius. Heartbreaking.

[phantom-threaded]

Brokeback Mountain 2005, dir. Ang Lee

[phantom-threaded]

"The truth is… sometimes I miss you so much I can hardly stand it." BROKEBACK MOUNTAIN 2005, dir. Ang Lee

[homoerotisch]

why has anyone bothered to write a kissing scene after annie proulx wrote this in 1997

[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.

[sergal]

everyone shut da fuck up this is the only thing that matters

[sergal]

Oh My God Damn

[segamascott]

got my first ever official customer complaint because when i was going over the terms of their life insurance they were like "well i don't plan to die" and i was like "well you're going to"

[clannfearrunt]

Bucketman character history “2 children stacked on top of each other” -> “some kind of fucking entity almost certainly not human in nature” -> “the former Secretary-General of the United Nations”. Conclusion is the author doesn’t ever really know what they are and therefore they can contain infinite multitudes of conflicting information