This was supposed to be a commonplace blog. A place to put bits of things that I’ve read that have stuck with me, whether I agree with them completely or not. Now it’s some of that but mostly whatever I feel like! Tbh barely know how to use tumblr.
I was in a hotel recently and one of the tv channels was playing The Great American Bible Challenge, an American game show about Bible trivia.
It is a painfully cringy delight. There’s a gospel choir that sings them into commercial breaks. The podiums are shaped so it looks like an open Bible is laying on top.
The last challenge: “The Final Revelation”
The show’s catchphrase: “In this game, if you don’t know your bible, you don’t have a prayer.”
Maybe Bible game shows exist elsewhere(?) but I feel like they wouldn’t look like this. This is the most concentrated form of American cheesiness I’ve ever seen.
I’m feeling so frickin hopeless about ever finding a job when I can’t even get an interview.
Those last two jobs I applied for, I literally could not imagine something that my resume is so unusually tailored for. There is probably 5 other people maximum in this area that also have experience in both field x and field y (and even less with my skill set and training). I guess I could understand rejections with my past applications (where maybe people wanted someone more experienced/focused on x or y) but I kind of just want to throw in the towel after this.
I know my self worth isn’t (eh… shouldn’t be) tied to having a job or getting interviews but it’s been so demoralizing. I’ve been so depressed lately I don’t think I can deal with asking why I was rejected what would have make me a stronger candidate.
I will get an interview for the last two positions I’ve applied to. I will get a job offer from them, I will get hired and I will have an amazing time working there.
🔮 🕯️ 🔮 🕯️
Feel free to reblog, especially if you want this to apply to you too.
I’m taking a page from Octavia Butler and writing this into existence. However, she wrote these motivational notes for becoming a best selling author. Me, why I’m just trying to get through the applicant tracking system rn. 🥲
I will get an interview for the last two positions I’ve applied to. I will get a job offer from them, I will get hired and I will have an amazing time working there.
🔮 🕯️ 🔮 🕯️
Feel free to reblog, especially if you want this to apply to you too.
I’m taking a page from Octavia Butler and writing this into existence. However, she wrote these motivational notes for becoming a best selling author. Me, why I’m just trying to get through the applicant tracking system rn. 🥲
I will get an interview for the last two positions I’ve applied to. I will get a job offer from them, I will get hired and I will have an amazing time working there.
🔮 🕯️ 🔮 🕯️
Feel free to reblog, especially if you want this to apply to you too.
This is a collage type piece I made from a single issue of a tabloid newspaper in mid-to-late 2019. I cut out and sorted all the ads with the same phone number and grouped them together. For privacy reasons I’ve censored addresses and parts of the phone numbers.
Questions for your consideration:
Why might these people/groups have taken out multiple ads?
Do any of these ads misrepresent themselves? If so, how?
What does putting these ads in conversation show? Would they present differently when spread out across an entire page/issue?
Got to admit, I kind of admire how passionate this hot wheels guy must be to put out two ads.
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.
Not me trying to write an intro post about myself/my blog and just writing a chunk of text about why I use a modified version of Chicago Style notes instead. 😓
I am unfortunately cursed with the knowledge of a poem that would become a damninent doddies/leetle gerls anthem were it ever unleashed onto the internet
Just saw The Mandalorian and Grogu. It’s a very fun, silly movie that honestly feels very much like a tribute to all the puppet and ewok scenes from the original trilogy. It’s not a serious film and it seems to know that. It’s still got a great sound, soundtrack and some really striking (if theatrical) scenes though.
I didn’t see it this way, but I’d bet it’d be one of the few movies that would be worth seeing in those “immersive/moving theatre chairs.”
It’s basically a longer form episode of The Mandalorian. I was a bit worried I wouldn’t be able to follow along because I haven’t seen parts of the tv show or know all the lore, but it’s honestly pretty easy to follow without a ton of prior knowledge.
Honestly, you could probably take someone with almost zero Star Wars knowledge, as long as you explain that a guy who is a part of a nearly extinct warrior group has found himself adopting a magical alien toddler.
(para Pat Rich y todas mis otras hermanas torturadas)
by Esperanza Malavé Cintrón
1.
he said
she didn't need those parts anyway
don't need no teats
they're for cows
she smiled
& later
examined the lumps in her mirror
remembering caresses
the lips of lovers
tugging at taunt nipples
missis
mistress
misogyny
master
mammary
mastectomy
minus
white coats & rubber gloves
claimed the useless part
sliced clean
& tossed onto a steel tray
women are the best customers
frightened little things
worried about useless parts
2.
he kissed the remaining breast
ran his fingers over the scar
& said
she wd look good if she had
no breasts
she cried
missis
master
mammary
minus
no more babies
to suckle
women always harping
she still smiled
rode her bike
to size prostheses
got a new boyfriend
the one she had
couldn't take it
they took the second one
& she
kept fixing up her house
bought new light fixtures
w/ the money she saved
on brassieres & low-cut gowns
riding that bike
& laughing
useless parts
3.
but they kept taking
white coats & rubber gloves
claiming useless parts
shoveling them onto steel trays
don't need those parts anyway
long as she's got
a hole
his
hippocrates
hypocrisy
hysterotomy
hysterectomy
hysteria
no more babies
wd there be muscles to flex
cd she feel him come
no more tampons
she smiled
long as she cd
then she died
wondering if all the cutting
had given her an extra day
like when they used to
bleed people for fevers
or slap on leeches
wondering if her teats
had been testicles
wd they have been considered
useless parts
From Broadside Poets Theater no. 9 (Broadside Press, 1991) and SEEDS: The Literary Journal of Sisters of Color vol 1 (Sisters of Color, 1991).
Cintrón was one of the founders of SEEDS, alongside Cecilia Rodríguez Milanés, and Druis Beasley Knowles. For more info about Cintrón and her published books (including where to purchase them), she has a website.
To read more work published in SEEDS, the University of Central Florida has freely available digital copies of their volumes.
