hey real quick can anybody help me find this image that I’ve seen before here on tumblr. it looks like this
the button doesn’t necessarily say “Elucidate the Rapture” but it does say something that’s kind of lengthy and has religious connotations. the woman pushing the button has an expression of indescribable smugness. there might be other buttons on the machine (?) she is pressing.
I cannot believe that this is a website where you can ask “hey i think i saw a weird image once” and put a bad stick figure drawing of it and someone will be like “oh yeah that’s the first installment of a 12-part post-ironic apocalypse fever dream photoshop series” and just hand you a dozen of the most unhinged images you’ve ever seen in your life, that still have a better three act structure than most modern cinema
this is far more than “random person in 2005ish creates surreal visual narrative” - let’s go deeper down the rabbit hole, shall we?
to Immanentize the Eschaton means to bring about utopian conditions and create heaven on Earth
the phrase is first cited in the Discordian religious text, Principia Discordia (1963), for whom Discord (aka Eris) is goddess:
the first line of Robert Anton Wilson and Robert Shea’s 1975 The Illuminatus! trilogy: “It was the year when they finally immanentized the Eschaton.”
in Frederik Pohl’s The Eschaton Sequence (1997 - 1999), the human race is caught up in a galactic war between two alien races attempting to immanentize the eschaton
in Ken Macleod's The Stone Canal (1997), one of the chapters is “Another crack at Immanentising the Eschaton”
Crystal Thierry’s (aka the modern Discordia / Eris) narrative lives in esteemed company
here’s what the warning in the image says btw because i wanted to read it but it was too blurry so i had to spend several minutes hunting for a version with better resolution, so I’m posting it here so nobody else has to make that same journey
“DO NOT OPERATE THE ESCHATRON 9000 UNLESS YOU’RE REALLY, REALLY SERIOUS ABOUT DESTROYING THE WORLD”
D&D worldbuilding posit, for all WotC-canon and homebrew games: In days of yore on a plane whose name is forgotten, there was a mighty and much-gloried king called Gi’zhas Kreist. He wanted to be mightier and more-gloried yet, so he made a deal with Asmodeus himself, Lord of all the Hells. Asmodeus gave him the power to build a sprawling, interplanar empire, and promised that his name would be spoken until the end of time.
Gi’zhas Kreist was of course a shit king and an even worse emperor, and was eventually overthrown, his empire pulled down and eroded, all lost to history except maybe the stumped legs of a single statue in a desert somewhere…
And his name, which became and remained a curse throughout all the worlds. Because Asmodeus always follows through, and because Fuck That Guy. So anytime a character, PC or NPC, says something that in English would probably be transliterated as “Jesus Christ”, they are in character, and they won’t know it without a 30+ History check but they’re implicitly saying, “only this dead asshole should have to deal with the bullshit I’m dealing with right now.”
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.
Feed your dashboard by answering my question, blogger.
Best? Pfft. Entirely subjective notion. What do I like? I am a sucker for the maps of Icewind Dale. The whole theme of the game was pretty good. Set the tone well.
The Mass Effect series is also a favourite.
Enslaved Odyssey to the West is beautiful in its way.
Masterlist of Mass Effect transcripts (trilogy, MEA)
Today is a good day to start this masterlist of Mass Effect Lore transcripts! A new transcript is available: Garrus' Normandy conversations in ME2 (romance scenes included).
I made some changes to include all romance scenes for Ash, Kaidan and Liara in their Normandy conversations transcripts for ME1.
I also completely changed the structure of the Citadel transcripts. Before, they were all put together and linked to Expose Saren.
Now you can check one specific file to see Citadel assignments and another file to check the chatter, squadmate comments and other conversations happening in different Citadel locations.
As I state in the intro of each of those transcripts, they're a work in progress. And a labor of love! I'm doing my best. They're not perfect, I'm constantly working on them to add more dialogue options or scenes. If you see a mistake, don't hesitate to send me a message so I can improve them :)
ME1:
Normandy Conversations :
Ashley (romance scenes included)
Kaidan (romance scenes included)
Liara (romance scenes included)
Tali
Wrex
Garrus
Joker
Main Missions :
Prologue: Find the beacon (with Additional scenes)
germansingergirl
origamiaround
amandla
yousharknotpass
ellievsbear
gummygunk
tangentwithtanner
