you solve the mystery of what to have for dinner one night and you think "hell yeah case closed forever" WRONG there is a dinner mystery the next night too
This is - legitimately - my favourite delivery of Shakespeare I have EVER seen (and I have seen some good-ass productions yo, in the Globe Theatre itself even). Like seriously, even though the words are unchanged, he’s stripped away ALL of the archaic pretense and assumed grandeur of ~presenting the bard~ that makes even the most wildly talented of actors and innovative of productions inherently inaccessible to a modern audience. Like, they’re still great, they can still communicate the message and (some) of the nuance, but they’re still always a step removed from being identifiable to any viewer’s lived experience. They’re still always reciting 15th century poetry. But this guy? This guy is like, screw iambic pentameter, to hell with being precious about the material, HOW WOULD AN ACTUAL PERSON SAY THIS SHIT?
Like this. And it’s beautiful. It’s beautiful to hear a soliloquy I loved so much already, and have it come to life in a way it never, ever, did before. I feel like I grasp his motivations, his twists and turns, no longer on an academic level but on a visceral, instinctive one. Because he’s presenting his mental and emotional journey in a way that speaks honestly, like a real person.
So yeah, this shit post? I love it. Deeply and sincerely.
There's a better shot of it in Aliens, right at the start when they're listing the Nostromo crew memebrs!
Also I read that there are deleted scenes of Ripley and Lambert arguing and being more overtly in a relationship. Ever since I learned that it's been my favorite ship.
They literally sail over the edge of it after passing through a hole in a wall of ice. They fall off. They get back to the other side by passing through the whole ocean.
But also there’s a globe on, like, everyone’s desk.
See, part of the conceit of the PotC trilogy is that all myths are true. Nearly every supernatural element in the franchise has a root in some real world mythology or pirate lore, although some of them are mashed together.
Another thing is that they take place at the end of the Golden Age of Piracy, and the more the map gets filled in, and the more the Royal Navy takes power, there less room there is for the mystical and supernatural in the world. This is explicitly called out in At World's End with the death of the kraken:
Barbossa: The world used to be a bigger place.
Jack: The world's still the same. There's just... less in it.
The only way to access the world of the supernatural is through the supernatural itself. You can only get to the Isla de Muerta with Jack's compass that points to whatever you desire, or if you already know where its is. You need Tia Dalma's map to find the edge of the world. To access the supernatural, you need to already be immersed in it.
The pirates world isn't flat, it's round - but because the edge of the world exists in myth, it therefore exists in reality. The pirates are able to find it through supernatural means, but if, say, someone like Norrington just sailed in the same general direction, he wouldn't end up in the same place.
If you look at it in a particular way, the Pirates specifically function by FAIRY RULES: obscure codes of law and formality that they are irrevocably bound to abide by...except when you get the wording wrong.
When we go to Tortuga (or any pirate controlled space) we leave behind the sensibilities of the real world and enter this bizarre perpetual revel of debauchery and violence that could never sustain itself in a world bound by sense.
That makes Jack our Puck character, a trickster of tricksters who can invert your fortunes just by letting him talk to you.
It also makes Will into a changeling, born of the fairyworld, adopted by mortals, and fated to return to it in what makes for a doomed love story. Hell, one might say that the reason Will's swords are SO GOOD is because they contain an element of myth about them: they're the IDEA of swords, true to how they would be in myth.
Long thought lost, a piece of writing by the prolific Middle Ages scholar Ibid provides an insight into the extent to which kennings - poetic allusions to events in myth or metaphors for a thing’s nature - are endemic in the Norse language.
The fragment recounts an otherwise unrecorded tale of the creation of one of the nine worlds that hang from Yggdrasil. In this myth, the ice giants originally inhabited Asgard along with the Aesir; there is an implication that this was one attributed reason for the antipathy between the two groups.
The war is a bloody stalemate until Odin stakes everything on a bold and controversial push towards the ice giants’ greatest hall, atop a vast mountain. The two hosts face each other, trading insults in preparation for a contest of champions. Odin mocks the enemy chief’s reliance on strength and numbers over strategy, but is in turn mocked for his desperation - the giant notes particularly that Thor is nowhere to be seen, and belittles Odin for the lack of loyalty his family show.
Then Odin and the champion of the ice giants (it is not entirely clear whether this is their chieftain or another figure) fight a duel; three exchanges of blows before full battle is joined.
Each time, Odin makes a powerful overhand strike with his spear, “splitting earth stone to its root,” but the ice giant steps away and is unharmed; then swings his sword in a powerful cut, which Odin dodges, “leaping clear over the hall” and later “leaping clear over the mountain”.
Each time, Odin stands his ground and the giant strides over to face him down, mocking his cowardice and making clear that his stamina is more than enough that tiring him out by making him climb up and down the mountain is futile. Odin’s reply translates as “Just so.”
After the third exchange, neither combatant has struck the other, so the duel is inconclusive; thus, the matter will be settled by open battle between the two armies. The two sides make their war-cries. At this signal, Thor reveals himself - he is in a small cave “at the mountain’s root,” waiting to do as Odin has commanded.
Thor grasps the mountain by its root, lifts it into the air, and “with a cry that will echo across the worlds until the end of time,” flings it away from Asgard.
The mountain of the ice giants catches on a branch lower down Yggdrasil, and is now considered a new world, ninth of the nine. It takes its name, Ibid writes, as a kenning from its creation - it is the thrown home, Yotenheim, and its people the Yoten.
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.
tangentwithtanner
kianamaiart
s-u-f-j-a-n-s-t-e-v-e-n-s
yousharknotpass
amandla
blakekathryn
