“If a society puts half its children into short skirts and warns them not to move in ways that reveal their panties, while putting the other half into jeans and overalls and encouraging them to climb trees, play ball, and participate in other vigorous outdoor games; if later, during adolescence, the children who have been wearing trousers are urged to “eat like growing boys,” while the children in skirts are warned to watch their weight and not get fat; if the half in jeans runs around in sneakers or boots, while the half in skirts totters about on spike heels, then these two groups of people will be biologically as well as socially different. Their muscles will be different, as will their reflexes, posture, arms, legs and feet, hand-eye coordination, and so on. Similarly, people who spend eight hours a day in an office working at a typewriter or a visual display terminal will be biologically different from those who work on construction jobs. There is no way to sort the biological and social components that produce these differences. We cannot sort nature from nurture when we confront group differences in societies in which people from different races, classes, and sexes do not have equal access to resources and power, and therefore live in different environments. Sex-typed generalizations, such as that men are heavier, taller, or stronger than women, obscure the diversity among women and among men and the extensive overlaps between them… Most women and men fall within the same range of heights, weights, and strengths, three variables that depend a great deal on how we have grown up and live. We all know that first-generation Americans, on average, are taller than their immigrant parents and that men who do physical labor, on average, are stronger than male college professors. But we forget to look for the obvious reasons for differences when confronted with assertions like ‘Men are stronger than women.’ We should be asking: ‘Which men?’ and ‘What do they do?’ There may be biologically based average differences between women and men, but these are interwoven with a host of social differences from which we cannot disentangle them.”
— Ruth Hubbard, “The Political Nature of ‘Human Nature’“
Image description: the text "the horrors persist but so do i" over a photo of a bejeweled desert scene with a white and pink bat with a bejeweled collar
this is a highly controversial opinion, I have no doubt King Arthur was bisexual but I think he was one of the few people in Camelot not interested in fucking Lancelot. he wanted to retain him as an employee but it did not cross his mind that Lancelot was fucking his wife because Lancelot is such a weird little twerp that he did not perceive him as a sexual being. my interpretation.
So true. The Galehaut/Lancelot relationship was like a dynastic marriage to resolve the conflict between two imperial powers. I like to imagine Galehaut was like “I have decided to abandon my plans of capturing [what is now] all of southern England and surrender to you despite my military advantage, all for the love of my achingly beautiful and spectacular new male wife, Lancelot du Lac.” and Arthur was like “Okay. Weird. Not homophobic or anything but Lancelot? You’re in love with Lancelot?”
Co-signed. That’s some real shit you said. Also, unlike Arthur, he was willing to yield and share his lover for everyone’s benefit. And then he died for love. A real freak. One of the best freaks in 13th century French literature.
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.
In case you just skimmed the post above and missed it, I want to reiterate and highlight Gebru's current position as Executive Director of Distributed AI Research Institute. If you're curious about what AI technology might look like when not applied in the horrifically unethical and damaging way it's currently applied, please check them out.
If we want to have nice things, decentralization is essential, and if we want to decentralize, we need to have our eyes on things that are beyond the scope of the current Big Tech narrative.
The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-roo
random PSA, I know a lot of people use duckduckgo as a Google alternative search engine, but it always kind of annoyed me when I was using it because it felt like No Name Brand Google
I have switched to using Startpage.com and vastly prefer it. for one thing, instead of displaying an "AI summary" at the top of the search results (unless you turn it off, yes I know), it displays the first paragraph of the Wikipedia article, with link, whenever it finds one that's relevant.
also a waaayyyyy better sense of design than duckduckgo
also private, European based, least annoying search I've used lately (RIP old "don't be evil" Google)
i have one of those, scraped from multiple different rec posts:
Search Engines
Infinity Search is an alternative search engine with a special focus on privacy
DuckDuckGo is a popular search engine for those who value their privacy and are put off by the thought of their every query being tracked and logged. Uses bangs, ![site] for in-page search (sells your data to microsoft and draws from fucking bing)
WolframAlpha is a privately owned search engine that allows you to “compute expert-level answers using Wolfram’s breakthrough algorithms, knowledgebase, and AI technology.” A data search engine.
Boardreader is a search engine for forums and message boards. It allows you to search forums and then filter down results by date and language.
Based in France, Qwant is a privacy-based search engine that won’t record your searches or use your personal details for advertising. Uses “&” as a bang search.
Another privacy-based search engine is Search Encrypt, which uses local encryption to ensure that users’ identifiable information cannot be tracked. Metasearch across multiple engines.
Offering unbiased results from several sources, SearX is a metasearch engine that aims to present a free, decentralized view of the internet. Can be self-hosted.
Gibiru’s tagline is “Unfiltered private search” and that’s exactly what it offers. Requires AnonymoX Firefox add-on for privacy.
Disconnect allows you to conduct anonymous searches through a search engine of your choice.
Swisscows provides fully encrypted searches to protect your privacy and security. Built-in violence/porn filter cannot be overridden.
MetaGer offers “Privacy Protected Search & Find” through its anonymised search. A plugin will allow it to be made a default.
Gigablast is a private search engine that indexes millions of websites and servers real-time information without tracking your data, keeping you hidden from marketers and spammers. Variety of filtration and refinement options for searching.
Oscobo is a search engine that protects your privacy while you search the web. By not using any third-party tools or scripts, your data is protected from hacking and misuse. Has a Chrome extension to allow use in toolbar.
https://search.marginalia.nu/ an independent DIY search engine that focuses on non-commercial content, and attempts to show you sites you perhaps weren't aware of in favor of the sort of sites you probably already knew existed. Use old-school searching rather than query-based for the best results.
https://www.mojeek.com/
https://wiby.me/ - It’s goal is to index as many personalized websites as possible, and NOT commercial sites.
https://4get.ca/ it works a lot like SearX, but honestly better. It doesn’t have its own index, but pulls from many others. I think it’s the best for research, since it allows you to search for answers from different indexes, is easy to configure, add free, and avoids censorship as much as it can.
https://www.searchenginemap.com/ for more on how search engines relate to each other.
https://yep.com/ is a crawler
https://www.etools.ch/ retrieves from Google, Mojeek, Bing, and Yandex, like Searx
https://www.dogpile.com/
https://searxng.org/ (next gen Searx)
https://luxxle.com/ - possibly conservative?
https://presearch.com/ - good for academic?
https://kagi.com/smallweb - free/randomised Kagi.
Other Searchers
www.refseek.com - Academic Resource Search. More than a billion sources: encyclopedia, monographies, magazines.
www.worldcat.org - a search for the contents of 20 thousand worldwide libraries. Find out where lies the nearest rare book you need.
https://link.springer.com - access to more than 10 million scientific documents: books, articles, research protocols.
www.bioline.org.br is a library of scientific bioscience journals published in developing countries.
http://repec.org - volunteers from 102 countries have collected almost 4 million publications on economics and related science.
www.science.gov is an American state search engine on 2200+ scientific sites. More than 200 million articles are indexed.
www.base-search.net is one of the most powerful researches on academic studies texts. More than 100 million scientific documents, 70% of them are free.https://cosine.club/ is an electronic music similarity search engine
huge fan of the depth of a good purple but another area that draws me is definitely around aquamarine/turquoise/seafoam. you can not go wrong once the green starts getting just a tinge more blue. a gal could certainly do worse than to pull over there and stay a while
She got the idea for the study while walking with her advisor at Stanford to discuss her thesis topic, and the paper she eventually published in the Journal of Experimental Psychology in 2014 is sharp enough that it should have ended the seated meeting on the day it came out.
She ran 4 experiments on 176 people. Same person tested twice. Once sitting, once walking. The creativity tasks were the standard ones psychologists have used for decades to measure how good a brain is at generating novel useful ideas.
81% of participants in the first experiment produced more creative ideas while walking than while sitting. In the second experiment, 88%. In the third, 100%. Every single person walked into a more creative version of themselves. On average, people generated 60% more novel useful ideas the moment their legs started moving.
The skeptical question is the obvious one. Maybe it was the fresh air. Maybe it was the scenery passing by. Maybe it was the change of environment doing the work, not the walking itself.
Oppezzo killed every one of those explanations with one experimental decision. She put people on a treadmill facing a blank wall. No scenery. No fresh air. No environmental change. Just legs moving in place while staring at white drywall. The 60% boost held.
Then she ran the experiment that closed the case completely. She took participants outside in two conditions. Half of them walked through a Stanford courtyard. The other half were pushed through the exact same courtyard in a wheelchair. Same outdoor stimulation. Same scenery passing at the same speed. The only difference was whether the legs were moving.
The walkers produced dramatically more novel high-quality ideas than the wheelchair group. The outdoors did almost nothing on its own. The walking did everything.
She also tested the opposite kind of thinking. Convergent thinking. The kind where there is one right answer and you have to narrow down to it. Word puzzles where 3 words share a hidden fourth word that connects them. The seated participants did slightly better on these. Walkers got slightly worse.
Walking is not a general intelligence enhancer. It does one specific thing. It opens up the divergent search inside your brain. The part that generates options. The part that produces unexpected connections. The part that takes a problem and finds five ways into it instead of one.
When you need to converge on the single right answer, sit down. When you need to find the answer in the first place, get up.
The mechanism is now well understood. Walking selectively activates what neuroscientists call the default mode network, the system inside your brain that runs when you are not consciously focused on anything. The DMN is where mind-wandering happens. Where memories cross-reference each other. Where ideas that have been sitting in separate folders inside your head finally bump into each other.
When you sit at a desk and force yourself to concentrate, you suppress the DMN. When you walk at a natural pace, the executive part of your brain gets just busy enough handling the walking that the DMN comes online and starts doing the work that focus was blocking.
The most useful finding in the entire paper is the one almost nobody quotes. The boost did not turn off the moment people stopped walking. Participants who walked first and then sat back down stayed elevated. Their next round of seated creativity work was still significantly better than people who had been sitting the whole time. The rest lingered for at least several minutes after the legs stopped moving.
You do not need to do creative work while walking. You need to walk before the creative work. The brain holds the state.
Take a look at these African peach moths (Egybolis vaillantina). The shades of blue on their wings are so striking against the bright hues and dark edgings. Living art, right?
the real horror is a months or years long time loop. no speedrunning your torture here. you have to sit with the consequences of your actions for a loooong time before the release of knowing the consequences and actions have been erased.
but oh, all the actions and consequences are gone. those relationships you built? empty. you can never build them again without the constant guilt of knowing that it's not as real the second, third, thirtieth time when you already know all their secrets and they none of yours.
but you can't hide. you can't isolate yourself because what if this is the time the loop breaks and then what? years gone by of missed chances with people who have changed you a thousand times and now circumstances have changed. you can never build back what was washed away by your own inaction. they'll never be able to meet you like they did the first time if you don't choose to meet them the first time every time
i knew a surgeon and he once told me “nobodys insides look like how the textbooks say they will. you never know what you’re going to find in there once you open them up” and that was easily the most ominous thing anyone’s ever said to me
when i was taking my first year anatomy lab, we’d occasionally find a cadaver where things would branch off or attach in the wrong order, and when we’d ask our prof about it, he’d just shrug and say “they must not have read the book”
In the past 10 years of teaching in an anatomy lab, I have seen:
- A donor with a scrotum the size of my head. When we opened it up, we discovered it was a MASSIVE inguinal hernia and a good 1.5 ft of intestine were trapped down there.
- A donor with situs inversus totalis, whose organs were a mirror image of what we normally see (ie their heart pointed right and their liver was on the left, just for starters)
- A donor whose right common carotid artery branched off the aorta waaay over on the left hand side of the body and crossed alllll the way back across the thorax to get where it needed to be.
- A donor with 4 lobes for their right lung (should only be 3). We named the 4th lobe the Lisa Loeb, but all of the students were too young to appreciate our sparkling wit.
- A shocking variety of penile and breast implants. Y'all would not believe the number of different ways science has come up to counteract gravity.
- A couple of cases of ectopic kidneys, where a kidney didn't rise to its typical position just deep to the lowest ribs and instead stayed in the pelvis.
There is probably some other stuff that I am forgetting. Take home point is: the human body is weird and wonderful and you should learn more about yours!
Yeah, I don't discover the anatomical weirdness but I've had clients come in with extra ribs, missing ribs, extra vertebra, accessory muscles (that's when you have duplicates - sometimes fine, sometimes not), bones connected where they shouldn't be (spoiler: if your lumbar spine is connected to your hip, it Causes Problems), all sorts of stuff. Bodies are weird!
This made me remember that I had a friend in high school who had one thumb that was like half an inch shorter than the other. Not sure how that happened.
