This piece was a bit ambitious for me, but I did it!
I love these girls and had to draw them ^^
Does this count as mermay??? mayhap.
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It's waterproof. It's windproof. It's lightweight and durable. And it's made from the intestines of two bears, painstakingly cleaned and sew
I really wanted to know more about this, especially how the water proof stitching works. Here's more information on this project, and hopefully more in the future!
The waterproof stitch is interesting; an insulating layer of dry grass twist between a running stitch and a whip stitch. A smart solution using simple techniques and basic implements. I really love that the ornamentation is applied as the garment is being made - so no additional time is required - and the fringe neither damages the sealed membrane of intestine during construction nor would it tear anything if it caught on something later.
When they say no part of an animal is wasted, this is what they mean.
I know that we are almost to June, so I better sneak this in here before we're officially out of May! I read 14 books in April (Magical Readathon is a great motivator!), only one of which was a re-read. My favorites of the month were definitely Blood Over Bright Haven and Star Shipped, but Wuthering Heights is up there, given the approximately 4 essays I could have written about it when I finished. My least favorite was absolutely All Superheroes Need PR.
The Spy Who Came in From the Cold by John le Carré: 4/5
Friday the Rabbi Slept Late by Harry Kemelman: 3/5
Star Shipped by Cat Sebastian: 4.5/5
The Hands of the Emperor by Victoria Goddard: 5/5, re-read
Blood Over Bright Haven by M. L. Wang: 5/5
All Superheroes Need PR by Elizabeth Stevens: 1/5
The Succubus’s Prize by Katee Robert: 2.25/5
Nobody’s Baby by Olivia Waite: 2.75/5
Wuthering Heights by Emily Brontë: 4.25/5
Alice’s Adventures in Wonderland by Lewis Carroll: 3/5
Indigo Ridge by Devney Perry: 3.5/5
The Incandescent by Emily Tesh: 4.25/5
Juniper Hill by Devney Perry: 3.75/5
The Tusks of Extinction by Ray Nayler: 3.5/5
The River Has Roots by Amal El-Mohtar: 3/5
Goal progress below the cut, as usual:
Complete series: -1 caught up on/finished/dnf’d, +1 started, 0 new books (0 | year total: -2)
Catch up on backlists: 0/143 (+0)
Read FIYAH/Nebula/Hugo finalists & awards: 151/229 (+2, but the Nebula & Hugo finalists both came out so really I went down by 4%)
Read down TBR: end of April 1622 (-3 for the month!! The fact that I'm this excited about that is a little sad considering that's only .2% of the total number!)
Read old top-of-TBR list: 0 (+0, this is really not moving...)
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.
I did technically also do sewing in middle/high school but I don’t count it because I didn’t enjoy it enough to stick with it.
Next one I learn will either be bobbin lace or crochet, but that last one will be probably be kicking and screaming because I have to join my blanket squares together, rather than actually wanting a new hobby. Or maybe dyeing fibre with food colouring, but that depends on when I get my kitchen back from Renovation Hell.
The very early days: cross stitch starting at age 4 (my first project was a rainbow and it took me two years), one attempt by my grandma to teach me to knit which is not in continuity with my adult knitting, friendship bracelets, school projects with bits of sewing or making pompoms or whatever.
Age 11-12: spinning, pin loom weaving. Made a bunch of yarn I didn't use, stopped after a few years.
Age 21+: knitting stuck this time. Got back into spinning now that I could knit with it. Added crochet to supplement the knitting. Shaft loom out of the blue age 27, now learning garment sewing with a goal to use my own fabric. And I've got deeply enough info everything that I now collect new beginner fibrecraft skills for their own sake.
Age 9ish: learned to knit but didn’t really take it up
Age 16: took up knitting more, but still not very seriously
Age 19: got my sewing machine and made a few things. Didn’t last very long
Age 22: started knitting a bit more seriously
Age 23 (2020): learned to crochet and dye yarn. The dyeing stuck more than the crochet did. I fell in love with dyeing, and thus fell in love with knitting with my own dyed yarn so the two hobbies fed each other. I also attempted to learn to spin with my first spindle but very quickly gave up
Age 26+: got my spinning wheel, fell in love with spinning. Not so much processing fibre though. Got my rigid heddle the next year but only used it sporadically until I had a Weaving Moment last year. I’ve done one embroidery. Trying to get back into sewing.
Knitting is the only one I do all the time, the rest I pick up and down. Atm I'm really eyeing sewing. As for what to get into next, I know the basics of weaving from when I was a kid, but it's not a fiber art I've ever really practiced. But I've been thinking about getting into tablet weaving!
“You took an oath to uphold the law and defend the citizens without fear or favor," said Vimes. "And to protect the innocent. That's all they put in. Maybe they thought those were the important things. Nothing in there about orders, even from me. You're an officer of the law, not a soldier of the government.”
-- Night Watch, by Terry Pratchett
Yeah okay there are like 11 species of heron native to the USA and yes fine I’ve only managed to spot 10 of those species. You might think I’m bitter about that one species evading me but I’m not. I’m actually the Least Bittern person about it in the entire world
pixelskylines
gummygunk
jacobvanloon
kianamaiart
kaledo-art
snowghoul
